Asam100bb Xinyu Shang Reading journal week1 In the article Immigration and Livelihood, 1840s to 1930s, the key reason why the Asians moved to the United States was to look for jobs. The Asians were desperate for jobs and were ready to work even if they received low salaries. On the other hand, their employers loved the situation since they made a lot of profits. The first Asians to enter the United States made it through the Manila galleon trade. “An act for the governance of masters and servants” (Chan, 1991 p25). However, other communities felt as if the Asians brought competition, which could result in a reduction of job opportunities. Some of these were the Euro-Americans employees who saw the Asians as their competitors. Others were the nativists for all levels who were aggressive to them since they stopped them for restless reasons to prevent their coming. Azuma Introduction tells that people who were born in Japan and later on shifted to America for studies had the right to express their views without any restrictions. Both the Tateishi and the Hoashi had not gotten a chance to become leaders in the Japenese colonist community, and they were not even recognized in America. “East is West West is East” (Azuma, 2005 p9). However, their routes were not highly valued compared to their expressions, especially during their times. These two communities had the capability of offering their shared predicament comprehensibly in public. Linking with the article on Mercantilists, Colonialists, and Laborers, the dilemma of these communities living through the claimed the separation for the East-West separation and linked binaries. The article also concentrates on the global history of Japanese immigrants and the procedure of creating the racial process. Additionally, the collective impacts of the organizational and figurative regulators control the experience of a marginal group that was viewed as a racial project.Chapter one talks about theoretical groups and how they are confusing. There was considerable confusion on whether the Japanese who relocated to the United States were there to colonize the U.S, or they had just come as immigrants. “Going to America” (Azuma, 2005 p23). The difficulty categorized the historical course of Japanese relocation to the United States as a varied nature of the early Issue community. It is clear that later on, after the Japanese had shifted to the United States, they implemented their capitalist economy, which brought more confusion concerning the issue of immigration and colonization. Therefore, this was one of the intercontinental histories of Japanese immigration in the American West, which brought about the contradiction issue. On the Takaki talks about how the Chinese moved to one of the cities in the United States known as California. It happened to be a movement that had been formed by several people from various nations. These were inclusive of the Korean, Chinese, Filipino, and Japanese. “Cheap ...

1. Asam100bb
Xinyu Shang
Reading journal week1
In the article Immigration and Livelihood, 1840s to 1930s, the
key reason why the Asians moved to the United States was to
look for jobs. The Asians were desperate for jobs and were
ready to work even if they received low salaries. On the other
hand, their employers loved the situation since they made a lot
of profits. The first Asians to enter the United States made it
through the Manila galleon trade. “An act for the governance of
masters and servants” (Chan, 1991 p25). However, other
communities felt as if the Asians brought competition, which
could result in a reduction of job opportunities. Some of these
were the Euro-Americans employees who saw the Asians as
their competitors. Others were the nativists for all levels who
were aggressive to them since they stopped them for restless
reasons to prevent their coming.
Azuma Introduction tells that people who were born in Japan
and later on shifted to America for studies had the right to
express their views without any restrictions. Both the Tateishi
and the Hoashi had not gotten a chance to become leaders in the
Japenese colonist community, and they were not even
recognized in America. “East is West West is East” (Azuma,
2005 p9). However, their routes were not highly valued
compared to their expressions, especially during their times.
These two communities had the capability of offering their
shared predicament comprehensibly in public. Linking with the
article on Mercantilists, Colonialists, and Laborers, the dilemma
of these communities living through the claimed the separation
for the East-West separation and linked binaries. The article
also concentrates on the global history of Japanese immigrants
and the procedure of creating the racial process. Additionally,
the collective impacts of the organizational and figurative

2. regulators control the experience of a marginal group that was
viewed as a racial project.Chapter one talks about theoretical
groups and how they are confusing. There was considerable
confusion on whether the Japanese who relocated to the United
States were there to colonize the U.S, or they had just come as
immigrants. “Going to America” (Azuma, 2005 p23). The
difficulty categorized the historical course of Japanese
relocation to the United States as a varied nature of the early
Issue community. It is clear that later on, after the Japanese
had shifted to the United States, they implemented their
capitalist economy, which brought more confusion concerning
the issue of immigration and colonization. Therefore, this was
one of the intercontinental histories of Japanese immigration in
the American West, which brought about the contradiction
issue.
On the Takaki talks about how the Chinese moved to one of the
cities in the United States known as California. It happened to
be a movement that had been formed by several people from
various nations. These were inclusive of the Korean, Chinese,
Filipino, and Japanese. “Cheap labor” (Takaki, 1993 p132)
.They were supposed to shift to Hawaii, which was an American
territory. All these came here to look for labor for them to make
a living. They worked in an industry that produced sugar.
Hawaii was very different from other states such as California
in various perspectives, such as ethical wise. Chapter five
speaks on how the Japanese finally settled in the United States
in California to be specific. It also indicates the level of racism
that the Japanese experienced while in the United States. A
young man whose origin was Hawaai stated that he experienced
how badly the Japanese were treated in California. “But I didn’t
realize the situation until I had a real experience “(Takaki, 1993
p180). It was after he had gone to get a shave, and upon the
barber realizing his nationality, he refused to attend to him and
instead chased him out of his shape. The Japanese were also
mistreated as laborers by receiving peanuts and having poor
working conditions.

3. BIBLIOGRAPHY 1
Prediction, persuasion, and the jurisprudence of behaviourism
Contents
1. I Introduction
2. II Stacking the deck: ‘predicting’ the contemporaneous
3. III Problematic characteristics of the ECtHR textual
‘predictive’ model
4. IV Conclusion
5. Footnotes
Full Text
Listen
There is a growing literature critiquing the unreflective
application of big data, predictive analytics, artificial
intelligence, and machine-learning techniques to social
problems. Such methods may reflect biases rather than reasoned
decision making. They also may leave those affected by
automated sorting and categorizing unable to understand the
basis of the decisions affecting them. Despite these problems,
machine-learning experts are feeding judicial opinions to
algorithms to predict how future cases will be decided. We call
the use of such predictive analytics in judicial contexts a
jurisprudence of behaviourism as it rests on a fundamentally
Skinnerian model of cognition as a black-boxed transformation
of inputs into outputs. In this model, persuasion is passé; what
matters is prediction. After describing and critiquing a recent
study that has advanced this jurisprudence of behaviourism, we
question the value of such research. Widespread deployment of
prediction models not based on the meaning of important
precedents and facts may endanger the core rule-of-law values.
artificial intelligence; cyber law; machine learning;

4. jurisprudence; predictive analysis
I Introduction
A growing chorus of critics are challenging the use of opaque
(or merely complex) predictive analytics programs to monitor,
influence, and assess individuals’ behaviour. The rise of a
‘black box society’ portends profound threats to individual
autonomy; when critical data and algorithms cannot be a matter
of public understanding or debate, both consumers and citizens
are unable to comprehend how they are being sorted,
categorized, and influenced.[ 2]
A predictable counter-argument has arisen, discounting the
comparative competence of human decision makers. Defending
opaque sentencing algorithms, for instance, Christine
Remington (a Wisconsin assistant attorney general) has stated:
‘We don’t know what’s going on in a judge’s head; it’s a black
box, too.’[ 3] Of course, a judge must (upon issuing an
important decision) explain why the decision was made; so too
are agencies covered by the Administrative Procedure Act
obliged to offer a ‘concise statement of basis and purpose’ for
rule making.[ 4] But there is a long tradition of realist
commentators dismissing the legal justifications adopted by
judges as unconvincing fig leaves for the ‘real’ (non-legal)
bases of their decisions.
In the first half of the twentieth century, the realist disdain for
stated rationales for decisions led in at least two directions:
toward more rigorous and open discussions of policy
considerations motivating judgments and toward frank
recognition of judges as political actors, reflecting certain
ideologies, values, and interests. In the twenty-first century, a
new response is beginning to emerge: a deployment of natural
language processing and machine-learning (ML) techniques to
predict whether judges will hear a case and, if so, how they will
decide it. ML experts are busily feeding algorithms with the
opinions of the Supreme Court of the United States, the
European Court of Human Rights, and other judicial bodies as
well as with metadata on justices’ ideological commitments,

5. past voting record, and myriad other variables. By processing
data related to cases, and the text of opinions, these systems
purport to predict how judges will decide cases, how individual
judges will vote, and how to optimize submissions and
arguments before them.
This form of prediction is analogous to forecasters using big
data (rather than understanding underlying atmospheric
dynamics) to predict the movement of storms. An algorithmic
analysis of a database of, say, 10,000 past cumulonimbi
sweeping over Lake Ontario may prove to be a better predictor
of the next cumulonimbus’s track than a trained meteorologist
without access to such a data trove. From the perspective of
many predictive analytics approaches, judges are just like any
other feature of the natural world – an entity that transforms
certain inputs (such as briefs and advocacy documents) into
outputs (decisions for or against a litigant). Just as forecasters
predict whether a cloud will veer southwest or southeast, the
user of a ML system might use machine-readable case
characteristics to predict whether a rainmaker will prevail in the
courtroom.
We call the use of algorithmic predictive analytics in judicial
contexts an emerging jurisprudence of behaviourism, since it
rests on a fundamentally Skinnerian model of mental processes
as a black-boxed transformation of inputs into outputs.[ 5] In
this model, persuasion is passé; what matters is prediction.[ 6]
After describing and critiquing a recent study typical of this
jurisprudence of behaviourism, we question the value of the
research program it is advancing. Billed as a method of
enhancing the legitimacy and efficiency of the legal system,
such modelling is all too likely to become one more tool
deployed by richer litigants to gain advantages over poorer
ones.[ 7] Moreover, it should raise suspicions if it is used as a
triage tool to determine the priority of cases. Such predictive
analytics are only as good as the training data on which they
depend, and there is good reason to doubt such data could ever
generate in social analysis the types of ground truths

6. characteristic of scientific methods applied to the natural world.
While fundamental physical laws rarely if ever change, human
behaviour can change dramatically in a short period of time.
Therefore, one should always be cautious when applying
automated methods in the human context, where factors as basic
as free will and political change make the behaviour of both
decision makers, and those they impact, impossible to predict
with certainty.[ 8]
Nor are predictive analytics immune from bias. Just as judges
bring biases into the courtroom, algorithm developers are prone
to incorporate their own prejudices and priors into their
machinery.[ 9] In addition, biases are no easier to address in
software than in decisions justified by natural language. Such
judicial opinions (or even oral statements) are generally much
less opaque than ML algorithms. Unlike many proprietary or
hopelessly opaque computational processes proposed to replace
them, judges and clerks can be questioned and rebuked for
discriminatory behaviour.[ 10] There is a growing literature
critiquing the unreflective application of ML techniques to
social problems.[ 11] Predictive analytics may reflect biases
rather than reasoned decision making.[ 12] They may also leave
those affected by automated sorting and categorizing unable to
understand the basis of the decisions affecting them, especially
when the output from the models in anyway affects one’s life,
liberty, or property rights and when litigants are not given the
basis of the model’s predictions.[ 13]
This article questions the social utility of prediction models as
applied to the judicial system, arguing that their deployment
may endanger core rule-of-law values. In full bloom, predictive
analytics would not simply be a camera trained on the judicial
system, reporting on it, but it would also be an engine of
influence, shaping it. Attorneys may decide whether to pursue
cases based on such systems; courts swamped by appeals or
applications may be tempted to use ML models to triage or
prioritize cases. In work published to widespread acclaim in
2016, Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel

7. Preoţiuc-Pietro, and Vasileios Lampos made bold claims about
the place of natural language processing (NLP) in the legal
system in their article Predicting Judicial Decisions of the
European Court of Human Rights: A Natural Language
Processing Perspective.[ 14] They claim that ‘advances in
Natural Language Processing (NLP) and Machine Learning
(ML) provide us with the tools to automatically analyse legal
materials, so as to build successful predictive models of judicial
outcomes.’[ 15] Presumably, they are referring to their own
work as part of these advances. However, close analysis of their
‘systematic study on predicting the outcome of cases tried by
the European Court of Human Rights based solely on textual
content’ reveals that their soi-disant ‘success’ merits closer
scrutiny on both positive and normative grounds.
The first question to be asked about a study like Predicting
Judicial Decisions is: what are its uses and purposes? Aletras
and colleagues suggest at least three uses. First, they present
their work as a first step toward the development of ML and
NLP software that can predict how judges and other authorities
will decide legal disputes. Second, Aletras has clearly stated to
media that artificial intelligence ‘could also be a valuable tool
for highlighting which cases are most likely to be violations of
the European Convention of Human Rights’ – in other words,
that it could help courts triage which cases they should
hear.[ 16] Third, they purport to intervene in a classic
jurisprudential debate – whether facts or law matter more in
judicial determinations.[ 17] Each of these aims and claims
should be rigorously interrogated, given shortcomings of the
study that the authors acknowledge. Beyond these
acknowledged problems, there are even more faults in their
approach which cast doubt on whether the research program of
NLP-based prediction of judicial outcomes, even if pursued in a
more realistic manner, has anything significant to contribute to
our understanding of the legal system.
Although Aletras and colleagues have used cutting edge ML and
NLP methods in their study, their approach metaphorically

8. stacks the deck in favour of their software and algorithms in so
many ways that it is hard to see its relevance to either practising
lawyers or scholars. Nor is it plausible to state that a method
this crude, and disconnected from actual legal meaning and
reasoning, provides empirical data relevant to jurisprudential
debates over legal formalism and realism. As more advanced
thinking on artificial intelligence and intelligence augmentation
has already demonstrated, there is an inevitable interface of
human meaning that is necessary to make sense of social
institutions like law.
II Stacking the deck: ‘predicting’ the contemporaneous
The European Court of Human Rights (ECtHR) hears cases in
which parties allege that their rights under the articles of the
European Convention of Human Rights were violated and not
remedied by their country’s courts.[ 18] The researchers claim
that the textual model has an accuracy of ‘79% on
average.’[ 19] Given sweepingly futuristic headlines generated
by the study (including ‘Could AI [Artificial Intelligence]
Replace Judges and Lawyers?’), a casual reader of reports on
the study might assume that this finding means that, using the
method of the researchers, those who have some aggregation of
data and text about case filings can use that data to predict how
the ECtHR will decide a case, with 79 per cent accuracy.[ 20]
However, that would not be accurate. Instead, the researchers
used the ‘circumstances’ subsection in the cases they claimed to
‘predict,’ which had ‘been formulated by the Court itself.’[ 21]
In other words, they claimed to be ‘predicting’ an event (a
decision) based on materials released simultaneously with the
decision. This is a bit like claiming to ‘predict’ whether a judge
had cereal for breakfast yesterday based on a report of the
nutritional composition of the materials on the judge’s plate at
the exact time she or he consumed the breakfast.[ 22] Readers
can (and should) balk at using the term ‘prediction’ to describe
correlations between past events (like decisions of a court) and
contemporaneously generated, past data (like the circumstances
subsection of a case). Sadly, though, few journalists

9. breathlessly reporting the study by Aletras and colleagues did
so.
To their credit, though, Aletras and colleagues repeatedly
emphasize how much they have effectively stacked the deck by
using ECtHR-generated documents themselves to help the
ML/NLP software they are using in the study ‘predict’ the
outcomes of the cases associated with those documents. A truly
predictive system would use the filings of the parties, or data
outside the filings, that was in existence before the judgement
itself. Aletras and colleagues grudgingly acknowledge that the
circumstances subsection ‘should not always be understood as a
neutral mirroring of the factual background of the case,’ but
they defend their method by stating that the ‘summaries of facts
found in the “Circumstances” section have to be at least framed
in as neutral and impartial a way as possible.’[ 23] However,
they give readers no clear guide as to when the circumstances
subsection is actually a neutral mirroring of factual background
or how closely it relates to records in existence before a
judgment that would actually be useful to those aspiring to
develop a predictive system.
Instead, their ‘premise is that published judgments can be used
to test the possibility of a text-based analysis for ex ante
predictions of outcomes on the assumption that there is enough
similarity between (at least) certain chunks of the text of
published judgments and applications lodged with the Court
and/or briefs submitted by parties with respect to pending
cases.’[ 24] But they give us few compelling reasons to accept
this assumption since almost any court writing an opinion to
justify a judgment is going to develop a facts section in ways
that reflect its outcome. The authors state that the ECtHR has
‘limited fact finding powers,’ but they give no sense of how
much that mitigates the cherry-picking of facts or statements
about the facts problem. Nor should we be comforted by the fact
that ‘the Court cannot openly acknowledge any kind of bias on
its part.’ Indeed, this suggests a need for the Court to avoid the
types of transparency in published justification that could help

10. researchers artificially limited to NLP better understand it.[ 25]
The authors also state that in the ‘vast majority of cases,’ the
‘parties do not seem to dispute the facts themselves, as
contained in the “Circumstances” subsection, but only their
legal significance.’ However, the critical issues here are, first,
the facts themselves and, second, how the parties characterized
the facts before the circumstances section was written. Again,
the fundamental problem of mischaracterization – of
‘prediction’ instead of mere correlation or relationship – crops
up to undermine the value of the study.
Even in its most academic mode – as an ostensibly empirical
analysis of the prevalence of legal realism – the study by
Aletras and colleagues stacks the deck in its favour in important
ways. Indeed, it might be seen as assuming at the outset a
version of the very hypothesis it ostensibly supports. This
hypothesis is that something other than legal reasoning itself
drives judicial decisions. Of course, that is true in a trivial
sense – there is no case if there are no facts – and perhaps the
authors intend to make that trivial point.[ 26] However, their
language suggests a larger aim, designed to meld NLP and
jurisprudence. Given the critical role of meaning in the latter
discipline, and their NLP methods’ indifference to it, one might
expect an unhappy coupling here. And that is indeed what we
find.
In the study by Aletras and colleagues, the corpus used for the
predictive algorithm was a body of ECtHR’s ‘published
judgments.’ Within these judgments, a summary of the factual
background of the case was summarized (by the Court) in the
circumstances section of the judgments, but the pleadings
themselves were not included as inputs.[ 27] The law section,
which ‘considers the merits of the case, through the use of legal
argument,’ was also input into the model to determine how well
that section alone could ‘predict’ the case outcome.[ 28]
Aletras and colleagues were selective in the corpus they fed to
their algorithms. The only judgments that were included in the
corpus were those that passed both a ‘prejudicial stage’ and a

11. second review.[ 29] In both stages, applications were denied if
they did not meet ‘admissibility criteria,’ which were largely
procedural in nature.[ 30] To the extent that such procedural
barriers were deemed ‘legal,’ we might immediately have
identified a bias problem in the corpus – that is, the types of
cases where the law entirely determined the outcome (no matter
how compelling the facts may have been) were removed from a
data set that was ostensibly fairly representative of the universe
of cases generally. This is not a small problem either; the
overwhelming majority of applications were deemed
inadmissible or struck out and were not reportable.[ 31]
But let us assume, for now, that the model only aspired to offer
data about the realist/formalist divide in those cases that did
meet the admissibility criteria. There were other biases in the
data set. Only cases that were in English, approximately 33 per
cent of the total ECtHR decisions, were included.[ 32] This is a
strange omission since the NLP approach employed here had no
semantic content – that is, the meaning of the words did not
matter to it. Presumably, this omission arose out of concerns for
making data coding and processing easier. There was also a
subject matter restriction that further limited the scope of the
sample. Only cases addressing issues in Articles 3, 6, and 8 of
the ECHR were included in training and in verifying the model.
And there is yet another limitation: the researchers then threw
cases out randomly (so that the data set contained an equal
number of violation/no violation cases) before using them as
training data.[ 33]
III Problematic characteristics of the ECtHR textual ‘predictive’
model
The algorithm used in the case depended on an atomization of
case language into words grouped together in sets of one-, two-,
three-, and four-word groupings, called n-grams.[ 34] Then,
2,000 of the most frequent n-grams, not taking into
consideration ‘grammar, syntax and word order,’ were placed in
feature matrices for each section of decisions and for the entire
case by using the vectors from each decision.[ 35] Topics,

12. which are created by ‘clustering together n-grams,’ were also
created.[ 36] Both topics and n-grams were used to ‘to train
Support Vector Machine (SVM) classifiers.’ As the authors
explain, an ‘SVM is a machine learning algorithm that has
shown particularly good results in text classification, especially
using small data sets.’[ 37] Model training data from these
opinions were ‘n-gram features,’ which consist of groups of
words that ‘appear in similar contexts.’[ 38] Matrix
mathematics, which are manipulations on two-dimensional
tables, and vector space models, which are based on a single
column within a table, were programmed to determine clusters
of words that should be similar to one another based on textual
context.[ 39] These clusters of words are called topics. The
model prevented a word group from showing up in more than
one topic. Thirty topics, or sets of similar word groupings, were
also created for entire court opinions. Topics were similarly
created for entire opinions for each article.[ 40] Since the court
opinions all follow a standard format, the opinions could be
easily dissected into different identifiable sections.[ 41] Note
that these sorting methods are legally meaningless. N-grams and
topics are not sorted the way a treatise writer might try to
organize cases or a judge might try to parse divergent lines of
precedent. Rather, they simply serve as potential independent
variables to predict a dependent variable (was there a violation,
or was there not a violation, of the Convention).
Before going further into the technical details of the study, it is
useful to compare it to prior successes of ML in facial or
number recognition. When a facial recognition program
successfully identifies a given picture as an image of a given
person, it does not achieve that machine vision in the way a
human being’s eye and brain would do so. Rather, an initial
training set of images (or perhaps even a single image) of the
person are processed, perhaps on a 1,000-by-1,000-pixel grid.
Each box in the grid can be identified as either skin or not skin,
smooth or not smooth, along hundreds or even thousands of
binaries, many of which would never be noticed by a human

13. being. Moreover, such parameters can be related to one another;
so, for example, regions hued as ‘lips’ or ‘eyes’ might have a
certain maximum length, width, or ratio to one another (such
that a person’s facial ‘signature’ reliably has eyes that are 1.35
times as long as they are wide). Add up enough of these ratios
for easily recognized features (ears, eyebrows, foreheads, and
so on), and software can quickly find a set of mathematical
parameters unique to a given person – or at least unique enough
that an algorithm can predict that a given picture is, or is not, a
picture of a given person, with a high degree of accuracy. The
technology found early commercial success with banks, which
needed a way to recognize numbers on cheques (given the wide
variety of human handwriting). With enough examples of
written numbers (properly reduced to data via dark or filled
spaces on a grid), and computational power, this recognition can
become nearly perfect.
Before assenting too quickly to the application of such methods
to words in cases (as we see them applied to features of faces),
we should note that there are not professions of ‘face
recognizers’ or ‘number recognizers’ among human beings. So
while Facebook’s face recognition algorithm, or TD Bank’s
cheque sorter, do not obviously challenge our intuitions about
how we recognize faces or numbers, applying ML to legal cases
should be marked as a jarring imperialism of ML methods into
domains associated with a rich history of meaning (and, to use a
classic term from the philosophy of social sciences, Verstehen).
In the realm of face recognizing, ‘whatever works’ as a
pragmatic ethic of effectiveness underwrites some societies’
acceptance of width/length ratios and other methods to assure
algorithmic recognition and classification of individuals.[ 42]
The application of ML approaches devoid of apprehension of
meaning in the legal context is more troubling. For example,
Aletras and colleagues acknowledge that there are cases where
the model predicts the incorrect outcome because of the
similarity in words in cases that have opposite results. In this
case, even if information regarding specific words that triggered

14. the SVM classifier were output, users might not be able to
easily determine that the case was likely misclassified.[ 43]
Even with confidence interval outputs, this type of problem
does not appear to have an easy solution. This is particularly
troubling for due process if such an algorithm, in error,
incorrectly classified someone’s case because it contained
language similarities to another very different case.[ 44] When
the cases are obviously misclassified in this way, models like
this would likely ‘surreptitiously embed biases, mistakes and
discrimination, and worse yet, even reiterate and reinforce them
on the new cases processed.’[ 45] So, too, might a batch of
training data representing a certain time period when a certain
class of cases were dominant help ensure the dominance of such
cases in the future. For example, the ‘most predictive topic’ for
Article 8 decisions included prominently the words ‘son, body,
result, Russian.’ If the system were used in the future to triage
cases, ceteris paribus, it might prioritize cases involving sons
over daughters or Russians over Poles.[ 46] But if those future
cases do not share the characteristics of the cases in the training
set that led to the ‘predictiveness’ of ‘son’ status or ‘Russian’
status, their prioritization would be a clear legal mistake.
Troublingly, the entire ‘predictive’ project here may be riddled
with spurious correlations. As any student of statistics knows, if
one tests enough data sets against one another, spurious
correlations will emerge. For example, Tyler Vigen has shown a
very tight correlation between the divorce rate in Maine and the
per capita consumption of margarine between 2000 and
2009.[ 47] It is unlikely that one variable there is driving the
other. Nor is it likely that some intervening variable is affecting
both butter consumption and divorce rates in a similar way, to
ensure a similar correlation in the future. Rather, this is just the
type of random association one might expect to emerge once
one has thrown enough computing power at enough data sets.
It is hard not to draw similar conclusions with respect to Aletras
and colleagues’ ‘predictive’ project. Draw enough variations
from the ‘bag of words,’ and some relationships will emerge.

15. Given that the algorithm only had to predict ‘violation’ or ‘no
violation,’ even a random guessing program would be expected
to have a 50 per cent accuracy rate. A thought experiment easily
deflates the meaning of their trumpeted 79 per cent ‘accuracy.’
Imagine that the authors had continual real time surveillance of
every aspect of the judges’ lives before they wrote their
opinions: the size of the buttons on their shirts and blouses,
calories consumed at breakfast, average speed of commute,
height and weight, and so forth. Given a near infinite number of
parameters of evaluation, it is altogether possible that they
could find that a cluster of data around breakfast type, or button
size, or some similarly irrelevant characteristics, also added an
increment of roughly 29 per cent accuracy to the baseline 50 per
cent accuracy achieved via randomness (or always guessing
violation). Should scholars celebrate the ‘artificial intelligence’
behind such a finding? No. Ideally, they would chuckle at it, as
readers of Vigen’s website find amusement at random
relationships between, say, number of letters in winning words
at the National Spelling Bee and number of people killed by
venomous spiders (which enjoys a 80.57 per cent correlation).
This may seem unfair to Aletras and colleagues since they are
using so much more advanced math than Vigen is. However,
their models do not factor in meaning, which is of paramount
importance in rights determinations. To be sure, words like
‘burial,’ ‘attack,’ and ‘died’ do appear properly predictive, to
some extent, in Article 8 decisions and cause no surprise when
they are predictive of violations.[ 48] But what are we to make
of inclusion of words like ‘result’ in the same list? There is
little to no reasoned explanation in their work as to why such
words should be predictive with respect to the corpus, let alone
future case law.
This is deeply troubling, because it is a foundational principle
of both administrative and evidence law that irrelevant factors
should not factor into a decision. To be sure, there is little
reason the ECtHR would use such a crude model to determine
the outcome of cases before it or even to use it as a decision

16. aide. However, software applications often are used in ways for
which they were not intended. When they are billed as
predictive models, attorneys and others could likely use the
models for their own triage purposes. This is especially
dangerous when attorneys are generally not very familiar with
statistical analysis and ML. The legal community’s ability to
scrutinize such models, and correctly interpret their results, is
questionable.[ 49] Journalistic hype around studies like this one
shows that public understanding is likely even more
impaired.[ 50]
Aletras and colleagues are aware of many problems with their
approach, and, in the paper, they continually hedge about its
utility. But they still assert:
Overall, we believe that building a text-based predictive system
of judicial decisions can offer lawyers and judges a useful
assisting tool. The system may be used to rapidly identify cases
and extract patterns that correlate with certain outcomes. It can
also be used to develop prior indicators for diagnosing potential
violations of specific Articles in lodged applications and
eventually prioritise the decision process on cases where
violation seems very likely. This may improve the significant
delay imposed by the Court and encourage more applications by
individuals who may have been discouraged by the expected
time delays.50
The paper’s abstract claims the model ‘can be useful, for both
lawyers and judges, as an assisting tool to rapidly identify cases
and extract patterns which lead to certain decisions.’ Aletras, in
a podcast interview, also stated that the model could be used for
case triage.[ 52] However, a judicial system that did so, without
attending to all of the critiques we have developed above (and
perhaps many more), would seriously jeopardize its legitimacy.
For example, consider how non-representative the training data
here is. Aletras and colleagues openly acknowledge a potential
issue with ‘selection effect,’ or the ability of the model to be
useful to the multitude of cases that were dismissed before
being heard by the Grand Chamber.[ 53] Petitions that were

17. determined to be inadmissible before trial were not included in
this study, as they were ‘not reported.’ Therefore, the model’s
output is narrowed significantly. Despite these problems, there
is a danger that the model could be deployed by bureaucrats at
the ECtHR to prioritize certain petitions, given that the Court is
deluged with thousands of petitions each year and can only
decide a fraction of those cases. Without a clear understanding
of how the model is predicting the success of a claim, it would
be irresponsible for judges or their clerks or subordinates to use
it in this way.[ 54]
IV Conclusion
This article has explored flaws in Aletras and colleagues’
Predicting Judicial Decisions to flag potential flaws in many
ML-driven research programs using NLP to predict outcomes in
legal systems. When such research programs ignore meaning –
the foundation of legal reasoning – their utility and social value
is greatly diminished. We also believe that such predictive tools
are, at present, largely irrelevant to debates in jurisprudence. If
they continue to gloss over the question of social and human
meaning in legal systems, NLP researchers should expect
justified neglect of their work by governments, law firms,
businesses, and the legal academy.[ 55]
Of course, the critiques above should not be construed as a
rejection of all statistical analysis of patterns in judicial
decision making. Such analyses can shed light on troubling
patterns of rulings. They can also alert decision makers when
biases begin to emerge.[ 56] For example, a notable study in
behavioural economics recently exposed judges imposing
shorter sentences after lunch than before it.[ 57] Ideally, such a
study does not inspire predictive analytics firms to find other
extraneous influences on decision making and to advise clients
on how to take advantage of them (by, for example, sending tall
attorneys to advocate before judges revealed to be partial to tall
advocates). Rather, such disturbing findings are better framed
as a prompt to judges to start developing ways of guarding
against this hunger bias once they are alerted to it (or, failing

18. that, to snack regularly).[ 58]
As clients, bar associations, and legislators debate how far to
permit software to substitute for legal counsel and human
consideration of cases, they should keep the limits of predictive
analytics in mind. Access to legal information does not
constitute access to justice. That depends on well-staffed courts,
qualified advocates, and an executive willing and able to
enforce the law. Software can generate useful lists of relevant
facts and cases, templatized forms, and analytics. However,
futurists are too quick to downplay the complexity of legal
processes and documents in order to portray them as readily
computerizable. It takes time and effort to understand the values
internal to substantive law and legal processes. Those outside
the profession have little sense of what they are missing when
politicians or business leaders opt for computer code to displace
legal code in the resolution of disputes – or even to prioritize
which disputes are to be heard.[ 59]
Efficiency simpliciter is not an adequate rationale for modelling
predictions of judicial behaviour. Nor are such approaches’
potential to generate more complaints and litigation (where
success is predicted) or to discourage such interventions (where
success is not predicted) necessarily a positive affordance for
society as a whole. Critics of ML have long complained that the
bias in corpora of past data may simply be recycled into bias in
future predictions. Heretofore, authors who have attempted to
model the future behaviour of courts from their past behaviour
have given us little sense of how such biases may be
counteracted, or even detected, once their approaches are more
widely disseminated. Modelling of judicial behaviour is often
heralded as an advance in access to justice; the better we can
predict judgments, so the thinking goes, the better we can know
what penalties law breaking will result in. But its partisans
underestimate the degree to which inequality in access to human
attorneys is exacerbated by current inequality in access to the
best software, automated forms, legal analytics, and other
technology and by the many ways in which past inequality has

19. deeply shaped training data.[ 60]
To be sure, predictive analytics in law may improve over time.
But that possibility does not undermine our position. The most
important contribution of our critique is not to cast doubt on the
likelihood of further advances in algorithms’ predictive power;
rather, we question where the results of such projects are useful
to the legal system and demonstrate how they threaten to
undermine its legitimacy. For example, the pragmatic and the
critical uses of predictive algorithms are in tension. An analyst
may reveal biases in judgments, such as legally irrelevant
details that somehow seem to be correlated with, and perhaps
even driving, decisions. The same analyst may sell the
predictive tool to attorneys or courts as a case selection or
triage tool. But precisely to the extent that past training data
reflect bias, they are likely to reinforce and spread the influence
of that bias when they are used by actors outside the judicial
system (who may, for example, not even try to advocate for a
particular class of meritorious cases since decision makers are
systematically biased against them). Academics should never
assume that merely increasing the ability to predict the future
(or analyze what was most important in decisions of the past) is
an unalloyed good. Rather, a long history of social scientific
research on reflexivity reveals how easily such analysis
exacerbates, rather than resolves, the problems it reveals.
To the extent that such reflexivity develops, better that the
Pandora’s Box of legal predictive analytics had never been
opened. ML may simply replay regrettable aspects of the past
into the future. On the other hand, once robust predictive
models are available, jurisdictions should carefully consider
rules to level the playing field and to ensure that all parties to a
dispute have access to critical technology. The law itself is free
to consult and copy. To the extent that legal technology
determines or heavily influences advocacy, it, too, should be
open on equal terms to all parties to a dispute. And, at the very
least, any deployment of such approaches during litigation
should be revealed to the judge presiding over it, and to

20. opposing parties, when it is deployed. Such a general rule of
disclosure is vital to future efforts to understand the influence
of ML, artificial intelligence, and predictive analytics on the
legal system as a whole.
BIBLIOGRAPHY 2
Emergence of BigData Research in Operations Management,
Information Systems, and Healthcare: Past Contributions
and Future Roadmap
Contents
1. Introduction
2. Information Systems
3. Operations and Supply Chain Management
4. Healthcare Systems
5. Bioinformatics
6. Healthcare Information Exchange
7. Medical Image Informatics
8. Health Management
9. Privacy Concerns
10. Way Ahead: Potential Applications and Challenges
11. Internet of Things (IoT) and Smart City
12. Predictive Manufacturing and 3‐D Printer
13. Smart Healthcare
14. Fading Boundaries
15. Closing Thoughts
16. References
Full Text
Listen
In this day, in the age of bigdata, consumers leave an easily
traceable digital footprint whenever they visit a website online.
Firms are interested in capturing the digital footprints of their
consumers to understand and predict consumer behavior. This
study deals with how bigdata analytics has been used in the
domains of information systems, operations management, and
healthcare. We also discuss the future potential

21. of bigdata applications in these domains (especially in the areas
of cloudcomputing, Internet of Things and smart city, predictive
manufacturing and 3‐D printing, and smart healthcare) and the
associated challenges. In this study, we present a framework for
applications of bigdata in these domains with the goal of
providing some interesting directions for future research.
bigdata; information systems; operations management;
healthcare
Data is the new science. BigData holds the answers
—Pat Gelsinger (Gelsinger[ 43] )
Introduction
This is an era where we are generating data at an exponential
rate. Large quantities of data representing our digital footprint
are generated whenever we interact over social media and chat
applications, use online shopping portals, or even when we use
such ubiquitous applications as Google Search or Google Maps
(Marr [ 89] ). Aside from data generated by us as users, an
enormous amount of data comes from “smart” devices, that is,
devices with sensors that collect data from the physical world
and convert them into a digital form (Hashem et al. [ 56] ,
Riggins and Wamba [ 117] ). This ever‐growing stream
of data generation is made possible by the advancements
in computing and mobile technology and the increasing
accessibility of the Internet. For example, according to a report
by the United States Census Bureau, in 2015, 78% of U.S.
households had a desktop or laptop, 75% had a handheld
computer such as a smartphone, and 77% had a broadband
Internet connection (Ryan and Lewis [ 124] ). All of these
devices, when connected to the Internet, have the ability to
generate data in large quantities for those who know how to
aggregate it.
It is these data—texts, reviews, ratings, news, images, videos,
audio, email, chat communications, search history, etc.—that
form the foundation of bigdata. Bigdata is characterized by four
dimensions: Volume, Velocity, Variety, and Veracity (Dykes
[ 38] , McAfee et al. [ 92] , Zikopoulos and Eaton [ 155] ).

22. Since the data is in unstructured form, a few years ago, it was
almost impossible to analyze the data in this form and get
meaningful insights. However, today with betterment of
analytics tools and technology, not only can we obtain valuable
information from the data but also use the insights to
predict futuretrends (Chen et al. [ 17] ). Most of the analytics
involve artificial intelligence and machine learning (Marr [ 90]
). The computers are trained to identify patterns from
the data and they can spot patterns much more reliably and
efficiently than humans. Advanced analytics tools can produce
millions of these results in a very short time. A report by
Rubinson Partners, a marketing and research firm, shows that
advertisers can boost their Return on Advertisement Spending
(ROAS) by up to 16× using aggregated bigdatawhich give them
information about the right time of advertising to the consumer
(Rubinson [ 122] ).
As a result, there is tremendous curiosity about the application
of bigdata among corporate houses. Anyone who wants to have
or maintain leverage over their competitors today is encouraged
to gather data and analyze them using bigdata analytics.
However, there is still a lack of knowledge about how to
implement bigdata analytics in many companies. In this article,
we investigate how several disciplines, specifically Information
systems, operations and supply chain management, and
healthcare, have applied bigdata in their domain. We also
explore future research avenues for bigdata in these areas.
Information Systems
There was a time in academic research when data were collected
solely for testing hypotheses to confirm our belief about certain
phenomena or behaviors. However, when we use the Internet
today, we leave a digital footprint that can be easily traced,
collected, and utilized by bigdata analytics to understand and
predict consumer behavior. Today it is even possible to store
and analyze such massive data at an inexpensive rate. These
analytics technologies can deliver new knowledge on their own
without active human intervention (Dhar [ 34] ), and as such

23. can be very valuable.
Information systems (IS) has been an interdisciplinary domain
conducting research at the intersection of computer technology
and data from the business world (Agarwal and Dhar [ 2] ). A
majority of the existing research in the IS domain focuses on
understanding and implementing processes that increase the
efficiency of business operations. Since IS researchers were
accustomed to handling huge volume of data, they started with
an early advantage as far as research in bigdata is concerned,
when compared to other business disciplines (Goes [ 49] ). IS
has contributed to the field of work surrounding bigdata in
many ways, including surrounding issues
of data integrity, data security and cybersecurity, social media,
e‐commerce, and web/mobile advertising. We briefly discuss
the recent work in each of these areas.
Data integrity is critical to bigdata. To semantically integrate
heterogeneous databases, it is essential to identify what entities
in a data source map to the same entities in some
other data sources so that data have a uniform and common
structure across all heterogeneous databases (Kong et al. [ 69] ).
This process is called entity reconciliation (Enríquez et al. [ 39]
, Zhao and Ram [ 153] ). Entity reconciliation is of paramount
importance to the process of data integration and management
in the bigdata environment. Researchers have studied entity
reconciliation from various perspectives. For example, Li et al.
([ 82] ) propose a context‐based entity description (CED) for
entity reconciliation where objects can be compared with the
CED to ascertain their corresponding entities. Some researchers
have also studied rule‐based frameworks for entity
reconciliation (Li et al. [ 83] ).
Data security is another topic in bigdata where several research
studies have been conducted (e.g., Chen and Zhang [ 16] ,
Demchenko et al. [ 31] , Katal et al. [67] ). Some studies
suggest the use of real‐time security analysis as a measure for
risk prevention (Lafuente [ 77] ), whereas some others
investigate privacy‐preserving data mining (PPDM) operations

24. (Xu et al. [ 148] ). PPDM is a method of preserving data in such
a way that applying data mining algorithms on the data do not
disclose any sensitive information about
the data. Bigdata analytics and optimization can be used as an
answer against advanced cybersecurity threats (Ji et al. [ 64] ).
Since bigdata covers massive breadth of information sources
and enormous depth of data, specifying and detecting risks
become very precise (Hurst et al. [ 61] , Sagiroglu and Sinanc
[ 125] ).
Some work at the interface of IS‐Marketing research has also
touched on the topic of bigdata. For example, data from social
media have been analyzed to comprehend behavior and predict
events (Ruths and Pfeffer [ 123] , Xu et al. [ 149] ). In
this direction, Qiu and Kumar ([ 115] ) study the performance
of prediction markets through a randomized field experiment
and find that an increase in audience size and a higher level of
online endorsement lead to more precise predictions. Moreover,
they also suggest integrating social media in predicting market
because social effects and reputational concerns improve the
participants’ prediction accuracy. The results from this study
recommend that the predictions will be more refined by
targeting people of intermediate abilities. Another area of social
media research where bigdata has contributed is text analysis
and sentiment mining (Mallipeddi et al. [ 88] , Salehan and Kim
[ 126] ). In this area, Kumar et al. ([ 74] ) study the importance
of management responses to online consumer reviews. The
results show that organizations who chose to respond to
consumer comments and reviews experienced a surge in the
total number of check‐ins. Findings from this study also
confirm that the spillover effect of online management response
on neighboring organizations depends on whether the focal
organization and the neighboring organizations are direct
competitor of each other. Furthermore, Millham and Thakur
([ 96] ) examine the pitfalls of applying bigdata techniques to
social media data. In this direction, Kumar et al. ([ 75] )
propose a novel hierarchical supervised‐learning approach to

25. increase the likelihood of detecting anomalies in online reviews
by analyzing several user features and then characterizing their
collective behavior in a unified manner. The dishonest online
reviews are difficult to detect because of complex interactions
between several user characteristics, such as review velocity,
volume, and variety. Kumar et al. ([ 75] ) model user
characteristics and interactions among them as univariate and
multivariate distributions. They then stack these distributions
using several supervised‐learning techniques, such as Logistic
Regression, Support Vector Machine, and k‐Nearest Neighbors
yielding robust meta‐classifiers.
Bigdata analytics has also been studied from the point of view
of strategic decision‐making in e‐commerce (Akter and Wamba
[ 3] ) and digital marketing (Fulgoni [ 41] , Minelli et al. [ 97]
). Some of the growing areas of research in e‐commerce include
the advertising strategy of online firms and their use of
recommender systems (Ghoshal et al. [ 47] , [ 48] , Liu et al.
[ 85] ). For example, Liu et al. ([ 85] ) study the advertising
game between two electronic retailers subject to a given level of
information technology (IT) capacity. They reach the conclusion
that if IT capacity constraints of the firms are not included in
advertisement decisions, then it may result in wastage of
advertisement expenditure. Based on their results, they present
implementable insights for policy makers regarding how to
control wasteful advertising. Ghoshal et al. ([ 48] ) find that
recommendation systems impact the prices of products in both
personalizing and non‐personalizing firms.
Furthermore, web and mobile advertising has been an
interesting area of research since the arrival of dot‐com firms
(Dawande et al. [ 28] , [ 29] , Fan et al. [ 40] , Kumar and Sethi
[ 71] , Kumar et al. [ 72] ). Dutta et al. ([ 37] ) and Kumar
([ 70] ) summarize the use and futuretrends of data analytics
and optimization in web and mobile advertising. Mookerjee
et al. ([ 100] ) develop a model predicting visitor's click on web
advertisements. They then discuss an approach to manage
Internet ads so that both click‐rate and revenue earned from

26. clicks are increased. The above group of scholars has also
developed a decision‐model that maximizes the advertising
firm's revenue subject to a click‐through rate constraint
(Mookerjee et al. [ 98] , [ 100] ). Another study uses the
real‐world data to validate new optimization methods for mobile
advertising (Mookerjee et al. [ 99] ).
IS scholars have also studied bigdata as a service, for example,
a platform combining bigdata and analytics
in cloudcomputing (Assunção et al. [ 6] , Demirkan and Delen
[ 33] , Zheng et al. [ 154] ). For instance,
the Big‐Data‐as‐a‐Service (BDaaS) has been explored to yield
user‐friendly application programming interfaces (APIs) so that
the users can easily access the
service‐generated bigdata analytic tools and corresponding
results (Zheng et al. [ 154] ). Cloudcomputingplays a vital role
in the use and adaption of bigdata analytics because
infrastructure requirement and cost of resources can be adjusted
according to actual demand (Assunção et al. [ 6] ).
Some studies have also been conducted on IT governance from
the perspective of bigdata (Hashem et al. [ 55] , Tallon [ 135] )
and deception detection (Fuller et al. [ 42] , Rubin and
Lukoianova [ 121] ). In the IT governance domain, Tallon
([ 135] ) suggests that good data governance practices maintain
a balance between value creation and risk exposure.
Implementing such practices help firm earn a competitive
leverage from their use of bigdata and application
of bigdataanalytics.
Figure summarizes the above discussion. This figure also
includes the contributions of bigdata in Operations and Supply
Chain Management, and Healthcare (discussed in the following
sections).
Operations and Supply Chain Management
With the betterment of enterprise resource planning (ERP)
software, it is easier to capture data at different levels of
operations. Firms want to analyze these data to develop more
efficient processes. Hence, bigdata and bigdata analytics are

27. being used by operations and supply chain academia as well as
the industry to get insights from existing data in order to make
better and informed decisions (Muhtaroglu et al. [ 103] , Wamba
et al. [ 143] ). The key areas in this domain where bigdata has
left an impact are supply chain network design, risk
management, inventory management, and retail operations.
Bigdata analytics has been used to align sourcing strategies with
the organizational goals (Romano and Formentini [ 119] ) and to
evaluate the performance of suppliers (Chai and Ngai [ 15] ,
Choi [ 21] ). Supply chain network design can itself account for
a massive amount of data and hence is a favorite area for
applying bigdata analytics. Researchers have studied supply
chain network design where the demand is uncertain (Benyoucef
et al. [ 9] , Bouzembrak et al. [ 11] , Soleimani et al. [ 133] ) as
well as where the demand is certain (Jindal and Sangwan [ 66] ,
Tiwari et al. [ 138] ). Firms can use analytics to ascertain the
cost, quality, and time‐to‐market parameters of products to gain
leverage over competitors (Bloch [ 10] , Luchs and Swan [ 86] ,
Srinivasan et al. [ 134] ).
Bigdata analytics has also been applied to maximize production
(Noyes et al., [ 108] ) and minimize the material waste (Sharma
and Agrawal [ 130] ). Noyes et al. ([ 108] ) recommend that
changes in existing manufacturing processes, incorporating
automation, and simplification of methods and raw materials,
will result in increasing the speed and throughput of in‐process
analytics during polysaccharide manufacturing processes.
Moreover, Sharma and Agrawal ([ 130] ) implemented fuzzy
analytic hierarchy process to solve production control policy
selection problem. Inventory challenges, such as cost, demand,
and supply fluctuations have also been studied
using bigdata analytics (Babai et al. [ 8] , Hayya et al. [ 58] ).
In this direction, Babai et al. ([ 8] ) discuss a new dynamic
inventory control method where forecasts and uncertainties
related to forecast are exogenous and known at each period.
Bigdata has also been increasingly used in retailing. In the last
decade, retailing has been one of the key areas of research for

28. the OM researchers, especially with the growth of multi‐channel
retailing (Mehra et al. [ 95] ). Bigdata analytics has also been
applied to retail operations by firms to reduce cost and to
market themselves better than the competition (Dutta et al. [ 37]
, Janakiraman et al. [ 62] , Kumar et al. [ 73] ). For
instance, bigdata techniques are now being heavily used in
recommender systems that reduce consumer search efforts
(Dutta et al. [ 37] ). Kumar et al. ([ 73] ) study how the
presence of brick‐and‐mortar stores impacts consumers’ online
purchase decision. Furthermore, Janakiraman et al. ([ 62] )
study product returns in multi‐channel retailing taking into
consideration consumers’ channel preference and choice.
Healthcare Systems
Healthcare systems in the United States have been rapidly
adopting electronic health records (EHRs) and Healthcare
Information Exchanges (HIEs) that are contributing to the
accumulation of massive quantities of heterogeneous
medical data from various sections of the healthcare industry—
payers, providers, and pharmaceuticals (Demirezen et al. [ 32] ,
Rajapakshe et al. [ 116] ). These data can be analyzed in order
to derive insights that can improve quality of healthcare
(Groves et al. [ 50] ). However, the analyses and practical
applications of such data become a challenge because of its
enormity and complexity. Since bigdata can deal with
massive data volume and variety at high velocity, it has the
potential to create significant value in healthcare by improving
outcomes while lowering costs (Roski et al. [ 120] ). It has been
shown to improve the quality of care, make operational
processes more efficient, predict and plan responses to disease
epidemics, and optimize healthcare spending at all levels
(Nambiar et al. [ 105] ). Here, we explore how bigdata analytics
has revolutionized the healthcare industry.
Bioinformatics
One of the subsections of the healthcare industry
where bigdata has contributed the most is biomedical research.
With the emergence and enhancement

29. of parallelcomputing and cloudcomputing—two of the most
important infrastructural pillars of bigdata analytics—and with
the extensive use of EHRs and HIEs, the cost and effort of
capturing and exploring biomedical data are decreasing.
In bioinformatics, bigdata contributes in yielding infrastructure
for computing and data processing, including error detection
techniques. Cloud‐based analytics tools, such as Hadoop and
MapReduce, are extensively used in the biomedical domain
(Taylor [ 136] ). Parallelcomputing models, such as CloudBurst
(Schatz [127] ), Contrail (Schatz et al. [ 128] ), and Crossbow
(Gurtowski et al. [ 52] ), are making the genome mapping
process easier. CloudBurst improves the performance of the
genome mapping process as well as reduces the time required
for mapping significantly (Schatz [ 127] ). DistMap, a
scalable, integrated workflow on a Hadoop cluster, supports
nine different mapping tools (Pandey and Schlötterer [ 112] ).
SeqWare (D O'Connor et al. [ 24] ), based on Apache HBase
database (George [ 45] ), is used for accessing large‐scale
whole‐genome datasets, whereas Hydra (based on
Hadoop‐distributedcomputing framework) is used for processing
large peptide and spectra databases (Lewis et al. [ 81] ). Tools
such as SAMQA (Robinson et al. [ 118] ), ART (Huang et al.
[ 59] ), and CloudRS (Chen et al. [ 18] ) help in identifying
errors in sequencing data. Furthermore, Genome Analysis
Toolkit (GATK) (McKenna et al. [ 94] , Van der Auwera et al.
[139] ), BlueSNP (Huang et al. [ 60] ), and Myrna (Langmead
et al. [ 78] ) are toolkits and packages that aid researchers in
analyzing genomic data.
Healthcare Information Exchange
Clinical informatics focuses on the application of IT in the
healthcare domain. It includes activity‐based research, analysis
of the relationship between a patient'smain diagnosis (MD) and
underlying cause of death (UCD), and storage of data from
EHRs and HIEs (Luo et al. [ 87] ). Bigdata's main contributions
have been to the manner in which EHR and HIE data are stored.
The clinical real‐time stream data are stored using NoSQL

30. database, Hadoop, and HBase database because of their
high‐performance characteristics (Dutta et al. [ 36] , Jin et al.
[ 65] , Mazurek [ 91] ). Some research work has also studied
and proposed several interactive methods of sharing
medical data from multiple platforms (Chen et al. [ 19] ).
Healthcare Information Exchanges are used for efficient
information sharing among heterogeneous healthcare entities,
thus increasing the quality of care provided. Janakiraman et al.
([ 63] ) study the use of HIEs in emergency departments (EDs)
and find that the benefits of HIEs increase with more
information on patients, doctors, and prior interaction between
them. Yaraghi et al. ([ 150] ) model HIE as a multi‐sided
platform. Users evaluate the self‐service technologies of the
model based on both user‐specific and network‐specific factors.
Another body of research studies whether healthcare reforming
models leads to better patient‐centric outcomes (Youn et al.
[ 151] ).
Bigdata techniques have enabled the availability and analyses of
a massive volume of clinical data. Insights derived from
this data analysis can help medical professionals in identifying
disease symptoms and predicting the cause and occurrence of
diseases much better, eventually resulting in an overall
improved quality of care (Genta and Sonnenberg [ 44] ,
McGregor [ 93] , Wang and Krishnan [ 144] ). Since the size
and complexity of data are enormous and often involve
integrating clinical data from various platforms to understand
the bigger picture, data security is often compromised during
analysis of clinical data. Bigdatatechniques can address this
issue (Schultz [ 129] ). Researchers have proposed several
models and frameworks to efficiently protect the privacy of
the data as well as effectively deal with concurrent analyses of
datasets (Lin et al. [ 84] , Sobhy et al. [ 132] ).
Medical Image Informatics
With the dawn of improved imaging technology, EHRs are often
accompanied with high quality medical images. Studying the
clinical data along with the analysis of such images will lead to

31. better diagnoses, as well as more accurate prediction of diseases
in future (Ghani et al. [ 46] ). Medical image informatics
focuses on processing images for meaningful insights
using bigdata tools and technologies. Similarly, picture
archiving and communication systems (PACS) have been
critically advantageous for the medical community, since these
medical images can be used for improved decision regarding
treatment of patients and predicting re‐admission (Ghani et al.
[ 46] ). Silva et al. ([ 131] ) discuss how to integrate data in
PACS when the digital imaging and communications in
medicine (DICOM) object repository and database system of
PACS are transferred to the cloud. Since analyzing large
quantities of high quality clinical images
using bigdata analytics generates rich, spatially oriented
information at the cellular and sub‐cellular levels, systems such
as Hadoop‐GIS (Wang et al. [ 145] ), that is,
cost‐effective parallelsystems, are being developed to aid in
managing advanced spatial queries.
Health Management
Recent studies have also used bigdata techniques to analyze the
contents of social media as a means for contagious disease
surveillance, as well as for monitoring the occurrence of
diseases throughout the world (Hay et al. [ 57] , Young et al.
[ 152] ). Bigdata analytics tools are used on social media
communications to detect depression‐related emotional patterns,
and thus identify individuals suffering from depression from
among the users (Nambisan et al. [ 106] ). Health IT
infrastructures, such as the US Veterans Health
Administration's (VHA), have facilitated improved quality of
care by providing structured clinical data from EHRs as well as
unstructured data such as physician's notes (Kupersmith et al.
[ 76] ).
Privacy Concerns
In coming times, there is a massive potential of HIEs becoming
public utility infomediaries that many interested markets can
access to derive information (De Brantes et al. [ 30] ). However,

32. a major hurdle that adaption of HIEs faces is privacy concern
among consumers. A section of researchers is building HIE
frameworks incorporating privacy and security principles. For
example, Pickard and Swan ([ 113] ) have created a health
information sharing framework, which increases sharing of
health information, built on trust, motivation, and informed
consent. Trust is necessary for dealing with access control
issues, motivation maps the willingness to share, and informed
consent enforces the legal requirement to keep the information
safe. In another study, Anderson and Agarwal ([ 5] ) find that
type of the requesting stakeholder and how the information will
be used are two important factors that affect the privacy
concern of an individual while providing access to one's health
information. Numerous states in the United States have enacted
laws that incentivize HIE efforts and address the concerns of
patients regarding sharing of health information. In another
study, Adjerid et al. ([ 1] ) observe whether various forms of
privacy regulation policies facilitate or decrease HIE efforts.
They find that although privacy regulation alone negatively
effects HIE efforts, when combined with incentives, privacy
regulation with patient consent requirement positively impacts
HIE efforts.
Way Ahead: Potential Applications and Challenges
In this section, we discuss the potential of bigdata applications
in Information Systems, Operations/Supply Chain, and
Healthcare domains. Figure summarizes the key areas
of future research.
Internet of Things (IoT) and Smart City
The Internet of Things creates a world of interconnected
sensory devices containing sensors that can collect and store
information from their respective real‐world surroundings
(Hashem et al. [ 56] , Riggins and Wamba [ 117] ). According to
Business Insider, the number of IoT devices will be 75 billion
by the year 2020 (Danova [ 26] ). These devices can be sensors,
databases, Bluetooth devices, global positioning system (GPS),
and radio‐frequency identification (RFID) tags (O'Leary [ 109]

33. ). These devices collect massive amount of data, and if we delve
down deep into this information using bigdata analytic tools and
techniques, we may be able to derive useful insights. The
applications of IoT and bigdata analytics combined have the
potential to bring path‐breaking changes to various industries
and academic research. However, at the same time, since these
subjects are still very new, there are uncertainties among
scholars about how to implement them, and how best to extract
the business value from these concepts (Riggins and Wamba
[ 117] ).
One of the domains where the coupling of bigdata techniques
and IoT has made significant progress is the concept of a smart
city, that is, where each component of urban surrounding
consists of devices that are connected to a network (Hashem
et al. [ 55] ). These devices can collect data from their
surroundings and share among themselves. These data can be
used to monitor and manage the city in a refined dynamic
manner, to improve the standard of living, and to also support
the sustainability of the smart city (Kitchin [ 68] ). IoT concepts
enable information sharing across various devices, thus aiding
in the creation bigdata caches. Furthermore, bigdata analytics
are used to conduct real‐time analysis of smart city components.
Kitchin ([ 68] ) mentions that urban governance decisions
and future policies regarding city life are based on these
analyses. Some sub‐areas under smart city where the bulk of
research is being conducted are energy grids (Chourabi et al.
[ 22] ), smart environments (Atzori et al. [ 7] , Nam and Pardo
[ 104] , Tiwari et al. [ 137] ), waste management (Neirotti et al.
[ 107] , Washburn et al. [ 146] ), smart healthcare (Nam and
Pardo [ 104] , Washburn et al. [ 146] ), and public security
(Neirotti et al. [ 107] , Washburn et al. [ 146] ). An emerging
field surrounding smart city research is an area
where bigdata has the potential to make a lot of contribution in
the coming days.
Predictive Manufacturing and 3‐D Printer
Predictive manufacturing is based on cyber physical systems

34. (CPS). CPS consists of devices that communicate with each
other, as well as with the physical world, with the help of
sensors and actuators (Alur [ 4] ). CPS technology is becoming
increasingly popular among manufacturers in the United States
and Europe as it allows them to gain an edge in international
manufacturing dynamics (Wright [ 147] ). CPS technology can
also be used to improve the design of products, to track its
production and in‐service performance, and to enhance
productivity and efficiency of the manufacturers. General
Electric (GE) and Rolls Royce have embedded sensors on their
jet engines that capture data during flight and post‐flight, and
maintenance decisions can then be made based on these
logged data (Dai et al. [ 25] ).
Massive amounts of data are being collected from
manufacturing plants through RFID and CPS technologies (Lee
et al. [ 79] ). As more advancement is made in bigdata analytics,
these data about production equipment and operations can be
processed better. Security of CPS and predictive manufacturing
is another potential area where bigdata techniques can be
applied for better security outcomes. Furthermore, additive
manufacturing processes, also known as 3‐D printing, are used
to build three‐dimensional objects by depositing materials
layer‐by‐layer (Campbell et al. [ 12] , Conner et al. [ 23] ).
3‐D printing is a path‐breaking technology that, in
coming future, will make the existing models of manufacturing
for certain products obsolete (Waller and Fawcett [ 142] ).
Hence, it is profoundly important that we study the applications
of bigdata analytics to additive manufacturing in order to derive
insights.
Smart Healthcare
Smart Healthcare is an extension of IoT ideas in the healthcare
industry; that is, IoT devices equipped with RFID, Wireless
Sensor Network (WSN), and advanced mobile technologies are
being used to monitor patients and biomedical devices
(Catarinucci et al. [ 14] ). In the smart healthcare architecture,
IoT‐supporting devices are being used for seamless and

35. constant data collection, and bigdata technology on the cloud is
being used for storing, analyzing, and sharing this information
(Muhammad et al. [ 102] ). The nexus of IoT
and bigdata analytics hosted on cloud technology will not only
help in more accurate detection and treatment of illnesses, but
will also provide quality healthcare at a reduced cost (Varshney
and Chang [ 140] ). Moreover, smart healthcare enables to bring
specialized healthcare to people who have restricted movement,
or who are in remote areas where there is a dearth of specialized
doctors (Muhammad et al. [ 102] ).
Recently, the use of wearable devices has seen a rapid growth,
and the number of such units shipped annually is expected to
reach 148 million by 2019 (Danova [27] ). Olshansky et al.
([ 110] ) discuss how data captured by wearable devices can be
transmitted to health data aggregation services, such as Human
API (humanapi.co) and Welltok (welltok.com), who can
transform the data into measures of risk. These measures can be
used to observe health trends as well as to detect and prevent
diseases. Some promising topics of research in the smart
healthcare domain where bigdata can play an important role are
smart and connected health (Carroll [ 13] , Harwood et al. [ 54]
, Leroy et al. [ 80] ), and privacy issues in the smart healthcare
framework (Ding et al. [ 35] ).
Fading Boundaries
In this article, we explored the application of bigdata in three
different domains—information systems, operations and supply
chain, and healthcare. But, the line between these disciplines
are blurring with each passing day. Several new avenues of
research are becoming popular that are common to at least two
of these domains. One such topic is use of ERP platforms in
healthcare that is common to all the three fields.
Healthcare organizations accumulate massive amounts of
information from various departments and then different entities
in healthcare management rely on to carry out their services. An
automated integrated system, such as an ERP system to manage
the information coming from different services and processes,

36. will enable healthcare organizations to improve efficiency of
service and quality of care (Handayani et al. [ 53] ). The
motivations underlying the adoption of ERP system in
healthcare management are technological, managerial, clinical,
and financial (Poba‐Nzaou et al. [ 114] ). An ERP system
integrates various business units of healthcare organization,
such as finance, operation and supply chain management, and
human resource, and provides easy access within each unit. It
can also address the disparity in healthcare quality between
urban and rural settings. ERP provides connectivity among all
healthcare centers and hence information can also be accessed
from rural centers (Padhy et al. [ 111] ). Benefits from
implementing ERP can be classified into four categories—
patients’ satisfaction, stakeholders’ satisfaction, operations
efficiency, and strategic and performance management (Chiarini
et al. [ 20] ). However, ERP systems are costly to acquire and
involve hidden costs even after successful implementation such
as integration testing and staff members training costs (Gupta
[ 51] , Wailgum [ 141] ). Till date, majority of research work
involving ERP in healthcare domain has revolved around
implementation of ERP systems (Mucheleka and Halonen [ 101]
). One potential research avenue is to conduct empirical studies
to quantify the benefits from implementation of such systems.
Closing Thoughts
We generate data whenever we use the Internet. Aside from
the data generated by us, several interconnected smart devices
collect data, that is, devices with sensors collect data from their
surrounding real world. With this tremendous quantity
of data generated each day, bigdata and bigdata analytics are
very much in demand in several industries as well as among
scholars. In this study, we discussed the contributions
of bigdata in information systems, operations and supply chain
management, and healthcare domains. At the end, we talked
about four sub‐areas of these domains—cloudcomputing,
Internet of things (IoT) and smart city, predictive manufacturing
and 3‐D printer, and smart healthcare—

37. where bigdata techniques can lead to significant improvements.
We also discussed the corresponding challenges
and future research opportunities in the field, noting numerous
areas for growth and exploration.
BIBLIOGRAPHY 3
Is a computer capable, like a human, of experiencing emotions
(empathy, jealousy, fear)? Can a computer, through cunning,
imitate the expression of such emotions for "personal" gain?
Allowing for all this to be possible, it would follow necessarily
that the computer must not only be self-conscious but also have
awareness and understanding of the human mind, in order to
know its interlocutors' expectations and anticipate their
response. Perhaps the real question is beyond "Can a computer
think?" One may ask: "Can a computer be as manipulative, as
deceptive, as duplicitous-or as charming, as honest, and as kind
as a human can be?"
SUNDAY, May 11, 1997, was a day like any other. Everything
that was supposed to happen in politics, sports, and
entertainment happened on that day, with one notable exception.
In a New York City hotel an unexpected history-making event
took place. A chess tournament pitting a human against a
machine saw Garry Kasparov, the then reigning world chess
champion, being defeated by a computer called Deep Blue. A
new era had dawned.
In 2011, the prowess of the question-answering computer
Watson on the television game show Jeopardy! captured the
public's imagination. Watson won a match against two
seasoned Jeopardy!players and received the $1-million prize.
More recently, in 2016 a Go-playing program by the name of
AlphaGo won a tournament against Lee Sedol, the recognized
best player on the planet, by a score of 4 to 1. And on June 18,

38. 2018, a program dubbed Project Debater engaged two humans in
debates, on the topics of government subsidy of space
exploration and increased investment in telemedicine,
respectively, and did remarkably well. The world is beginning
to pay attention.
These four achievements are harbingers of greater things to
come. What is the common thread between Deep Blue, Watson,
AlphaGo, Project Debater, and many other successes? Artificial
Intelligence, a branch of computer science that aims to create
intelligent systems. Over the past two or three years, Artificial
Intelligence (AI), a scientific enterprise, has become a social
phenomenon, with myriad economic, cultural, and philosophical
implications. The advent of self-driving cars, speech-activated
automated assistants, and data analytics more generally has
transformed every sector of society. AI is reshaping and
reinventing such fields as health care, business, transportation,
education, and entertainment. The news media are replete with
stories on the new cognitive technologies generated by AI and
their effect on our daily lives and lifestyles. What is the reason
for this explosion of excitement over AI?
As a result of some recent advances in machine learning
technologies, the field is about a decade ahead of where we
thought it would be at this time, with advances proceeding at an
exponential rate. So says Elon Musk. In a BBC interview,
famous physicist Stephen Hawking (1942-2018) warned that
"the development of full artificial intelligence could spell the
end of the human race." And fears that the singularity is nigh
have resulted in websites, YouTube videos, and articles
describing our impending doom. But is it the takeover of an
artificial intelligence we should be worrying about? Or should
we be more concerned about giving too much power
to unintelligentAI? To make an accurate judgement, we need to
understand what all the fuss is about.
MACHINE "learning" refers to a family of computational
methods for analyzing data into statistically significant
regularities that are useful for some purpose. These regularities

39. are called "features" and the process of uncovering them
"feature detection." Humans and other animals detect features
whenever they recognize an object in the world: to perceive that
a particular bone is the kind of thing that can be chomped on is
to recognize a pattern of similarity between the bone being
perceived and a host of bone experiences in the past.
Machine learning technologies have become increasingly adept
at such classification tasks in well-understood areas. In the
context of human faces, for example, systems that are sensitive
to features such as noses, lips, eyes, and so on perform as well
as humans on face recognition tasks. But some domains are so
vast and multivaried that even humans don't have a good handle
on what set of features will be useful for a given task. For
example, we know that online "clicking" behaviour is a source
of potentially useful data, but we aren't sure how to organize it
in order to highlight the useful patterns. But if a human
programmer doesn't know what features an AI system should
detect, how can the system be built?
The AI excitement over the last few years is the result of some
very promising advances toward solving this problem. Deep
Learning algorithms can "extract" features from a set of data
and thereby move beyond what humans know. The techniques
have been used successfully on labelled data sets, where humans
have already tagged the photographs with captions-"DOG
PLAYING BALL"-that are used as a way of "supervising" a
system's learning by tracking how close or far it is on a given
input from the correct answer. Recently there has been success
with unlabelled data sets, what is called, "unsupervised"
learning. The millennial Pandora's Box has been opened.
AlphaGo is a result of this new wave of machine learning. Deep
Blue played chess by brute force, searching deeply through the
hardcoded array of possible outcomes before choosing the
optimal move. A human has no chance against this kind of
opponent, not because it is so much smarter, but simply because

40. it has a bigger working memory than a human does. With Go
this canned approach was not possible: there are far more
possible choices for each move, too many to hardcode and then
search in real time. But Deep Learning systems such as
AlphaGo can "learn" the relevant patterns of game play by
extracting move features from millions of example games. The
more games it plays, the more subtle its feature set becomes. On
March 15, 2016, AlphaGo was awarded the highest Go
grandmaster rank by South Korea's Go Association. Even the
creators of AlphaGo at Google's DeepMind in London have no
idea what move it will play at any given point in a game. Is the
singularity at hand?
TO answer that question, we need to consider carefully whether
such systems are in fact learningand
becoming intelligent. These questions take on urgency as
increasingly we use them to make important decisions about
human lives.
In 2017 the Canadian Institute for Advanced Research (CIFAR)
was awarded a $i25-million budget for the Pan-Canadian
Artificial Intelligence Strategy, an initiative to revamp every
facet of our bureaucracy with AI technology. The health care
system is one of the first areas targeted for change. And a pilot
project for early detection of possible suicides is already
underway.
How will such technology be used? Sally might be at risk for
suicide, but it doesn't follow from this that she ought to be put
under surveillance, institutionalized, or otherwise have her
autonomy undermined. More generally, machine learning is an
excellent tool for data analysis, but it cannot tell us what to do.
Practical judgement, the ability to bring relevant considerations
to bear on a particular situation, guides us in our considered
actions. Determining relevance is the critical skill here. How do
we do it? This is the million-dollar question, of course, and we
won't answer it here. Minimally, however, it requires a capacity
to synthesize what is important in a given situation with what is

41. important in human life more generally. In other words, it
requires an understanding of what it means to be a laughing,
working, eating, resting, playing being.
We still do not understand how meaning works. But we do know
that being an expert pattern-detector in some domain or other is
not all there is to it. The failures of our new AI heroes tell the
story. During the Jeopardy! -IBM Challenge, Watson revealed
what was behind its curtain-lots of meaningless data-in its
answer to the "Final Jeopardy! " question. The category was
"US Cities," and the answer was "Its largest airport is named for
a World War II hero; its second largest, for a World War II
battle." Watson's response? "What is Toronto?"
No surprise, then, that strategy games have been an AI industry
focus: the tight constraints of game situations make the field of
relevance narrow and, consequently, the chances of success
great. Even so, the story of chess and AI is far from over. The
1,400-year-old game recently received a boost with the
invention of Quantum Chess at Queen's University. This variant
uses the weird properties of quantum physics in order to
introduce an element of uncertainty into the game, thereby
giving humans an equal chance when playing against computers.
Unlike the chess pieces of the classical game, where a rook is a
rook, and a knight is a knight, a Quantum Chess piece is
a superposition of states, each representing a different classic
chess piece. A player does not know the identity of a piece (that
is, whether it is a pawn, a bishop, a queen, and so on) until the
piece is selected for a move. Furthermore, thanks to the bizarre
property of entanglement, the state of a chess piece is somehow
"bound" to the state of another piece, regardless of how far they
are separated; touch one, and you affect the other! The
unpredictability inherent in Quantum Chess creates a level
playing field for humans and computers. Unlike the case in
classic chess (where a program can engage in a deterministic
and thorough search for a good move), the hidden identities of
the pieces and the probabilistic nature of quantum physics
greatly diminish the computer's ability to conduct a reliable

42. search. Perhaps judgment will give humans an edge, even in this
limited domain. When it comes to Quantum Chess, even a
novice chess player may have a chance against a more
experienced human, as demonstrated by the following anecdote.
On January 26, 2016, a movie was premiered at the California
Institute of Technology during an event entitled One Entangled
Evening: A Celebration of Richard Feynman's Quantum
Legacy. The movie captured an exciting, and at times funny,
game of Quantum Chess between Hollywood actor Paul Rudd
and Stephen Hawking. It is worth noting that in this version of
Quantum Chess superposition has a different meaning from
being a superposition of states. Rather, superposition is spatial,
in the sense that the same chess piece can be, at the same time,
in two separate locations on the chess board (one known and
one unknown). Touching a piece in order to execute a move
determines probabilistically from which of the two locations the
piece is to move. It is as though the piece manifests itself
suddenly, either choosing to stay in its visible location or
possibly disappearing and materializing elsewhere on the board
(thereby revealing the unknown location).
OTHER aspects of AI are increasingly being addressed in
popular culture. The dark and suspenseful movie Ex
Machina (2014), directed by Alex Garland, offers an interesting
treatment of issues surrounding machine intelligence. An
experimental female robot is being tested for possessing
intelligence. She beguiles the young man testing her and
persuades him to take actions leading to her liberation from
captivity and simultaneously to his tragic end. The movie adds
the following unexpected twist to the standard question of
whether a machine can possess intelligence. If a robot displays
an emotion toward a human that may be interpreted, for
example, as love, is the emotion real, in the sense of being the
repetition of a learned vocabulary, or is it purposefully faked?
Is the robot being sincere, or might it be pretending? In other
words, has the computer reached a level of intelligence that
allows it to be able not only to automatically utter the words

43. that express a human sentiment but in fact to intentionally
simulate that feeling for a good or a bad purpose? Is a computer
capable, like a human, of experiencing emotions (empathy,
jealousy, fear)? Can a computer, through cunning, imitate the
expression of such emotions for "personal" gain? Allowing for
all this to be possible, it would follow necessarily that the
computer must not only be self-conscious but also have
awareness and understanding of the human mind, in order to
know its interlocutors' expectations and anticipate their
response. Perhaps the real question is beyond "Can a computer
think?" One may ask: "Can a computer be as manipulative, as
deceptive, as duplicitous-or as charming, as honest, and as kind
as a human can be?"
What will an AI system capable of making practical judgements
look like? Obviously, this is a foundational question for AI.
Whatever the answer is, we know that we don't yet have it. We
shouldn't be worried about the singularity-we are a long way off
from that. But we should be concerned about the use to which
AI technologies are being put. In our over-confidence in these
technologies, we are giving them too much power.
FROM a more optimistic perspective, human knowledge will be
deepened and broadened by the revolutionary paradigm of AI.
Consider its influence on one area of endeavour of vital
importance. AI will have a profound impact on the health care
landscape. Machine learning and data analytics will lead to the
discovery of improved tools for the detection, diagnosis, and
treatment of disease. More effective pharmaceuticals with fewer
or no side effects will be developed. In fact, computer scientists
will design better search algorithms that will make it possible to
produce drugs capable of being customized for each specific
individual. Once personalized medicine is attained, the entire
approach to health care will be completely revamped. Artificial
Intelligence will allow the behaviour of a biological cell to be
modelled as a computer algorithm. From this perspective, a cell
with a disease is seen as a program with a flaw. Correcting the

44. error in the program allows the cell to be healed. The positive
disruptive force of AI on health care will have resulted in a
great benefit for humankind.
Asam100bb
Xinyu Shang
Reading journal week2
The beginning of the chapter narrates the rapport of the
immigrant Japanese among the Americans and the Japanese-
Americans in the early 1880s. The immigrant Japanese were
mostly low-incomed people with illegal wives who were being
pushed into prostitution by their husbands to make the ends
meet. Most of these people were poor laborers, brothel owners
and relied on unethical means of earning. They were involved in
gambling and addiction which created a narrative of all the
Japanese which affected the lives of the Japanese-Americans.
The last decade of the nineteenth century caused havoc on the
Japanese when the immigration officer declared them as illegal
contractors and was denied entry in America, just like Chinese.
Issei i.e. the first generation Japanese-Americans and the elite
immigrants saw this as a threat to their social image and a cause
for racial discrimination thus they made it very clear how they
are different from the Chinese. Issei leaders stated, How do
Japanese in America and the Chinese in America differ? First,
the Chinese in America represent the lower class of the Chinese
race, and the Japanese in America the upper class of the
Japanese race. Second, the Chinese are so backward and
stubborn that they refuse the American way. The Japanese, on
the other hand, are so progressive and competent as to fit right
into the American way of life….In no way do we, energetic and
brilliant Japanese men, stand below those lowly Chinese
(Azuma,ch2).
The divergence of the Japanese-Americans escalated as they

45. believed the shabbily clothed laborers do not represent the
Japanese and do not meet the standard American lifestyle. The
bubble of the Issei leaders burst when the San Francisco Board
of Health forcefully tackled the elite and vaccinated them in
their groins during the outbreak of Sinification. They began to
realize they were nonetheless better than the Chinese according
to the Americans. This led to the formation of the Japanese
Deliberative Council of America whose aim was to save the
national stature of the Japanese and increasing the rights of
imperial issues. A significant decrease in meager jobs like
mining and manual workers was observed in the twentieth
century among the Japanese. The Gentlemen’s Agreement was
signed between the American and Japanese governments which
terminated the influx of the dekasegi migrants into America.
This resulted in jeopardizing the economic sources of Issei
contractors who used to hire these laborers.
The political riots up roared in Japan in the early 1900s and the
socialist and the leftist groups took refuge in San Francisco.
The blasphemous leaflets against Emperor Meiji were
distributed in which the Diplomats try to cover up with the
American authorities but it was useless and thus Foreign
Ministers of Japan ordered the prosecution of the socialists
upon their return to Japan.
Meanwhile many reformative measures were being also taken
such as anti-gambling campaigns and educating the rural
masses. This was also being done to shed the narrative of being
associated with the corrupt Chinese. The women were being
tutored by the elite Issei Japanese to be more palatable to the
American taste. They were being subtly brainwashed to stay in
America, adopt their lifestyle and bear their children and be
more presentable to the Americans to be inclusive of the
American culture. Organizations such as the Japanese Young
Women's Christian Association (YWCA) were formed whose
sole purpose was to introduce the Japanese women as the
modern women who wear western attire and behave lady-like.
Another noticeable community of the diasporic Japanese in the

46. 1920s had been molded who were not being accepted by the
Americans and rejected by the Japanese back home. They
suffered a great deal with racial subordination in the white
American regime. In areas like Walnut Groove, the Issei
farmers had started their successful journey by yielding the top-
quality produce, using the labor of Japanese farmers, which the
white farmers had overlooked and gained profits triple folds.
The Alien Land Law made the farmers more vulnerable and
were being exploited by the tenancy contracts by the hands of
white property owners. This made them more socially and
economically dependent on the Americans and thus they shifted
to shared-cropping which had less monetary benefits for the
farmers. The institutionalized racism and abuse made the
farmers give up on farming.
After the war years, the racial discriminatory lines were blurred
to some extent due to the growing population of the Japanese.
The debate to permit interracial interactions began beyond the
economic ties. America's dismissal of racial equality showed the
American hypocrisy and thus left an adverse impact on the
young Japanese.
Prepared at the end of the quarter, this is your chance to reflect
on the course and the work you have done over the past ten
weeks. This should be written in the style of a letter addressed
either to me or to you. The letters should do two things, namely:
reflect on the course and report/ reflect on the team(s) with
whom you worked during the course. The reflections should
discuss the readings and topics you found most meaningful,
struggles and victories that you had, the ways that elements
from the course connect to your life and/or your other courses,
and your thoughts on what you would do if you took another
course like this. The discussion of your team(s) is your chance

47. to tell me how things worked (or didn’t) with your classmates.
If everything went fine and everyone contributed, from your
perspective, then you can simply say that. If there were issues
with distribution of work, communication, unequal effort, or
other problems, then please include those. This will help me
adjust the grades for individuals on the team assignments.
Regardless, please also reflect on what you (or I) could do to
improve team projects in the future. The letter can be as long or
as short as you want it to be (though make sure to cover the
required elements).
Length: Probably not less than 1.5 pages, but as long as you
want beyond that Style: Letter
Citation: Mention the authors and use quotations marks if you
quote something
Xinyu Shang
ASAM 100 BB
Reading Journal
This reflection is premised on the YouTube video entitled, “A
community Grows
Despite Racism.” This is a 4 minutes 07 seconds video which
showcase the growth of the
Japanese community in American despite the several efforts and
legislations to
discriminate against them and limit their existence in the United
States. The Japanese

48. went to the US in search of jobs more than 100 years ago
whereby more than 3000
Japanese found their way into the United States. They migrated
to the Haiwai mainland
to work as farmers, plantation workers, fishermen, and railway
workers.
However, despite their hard work, the Japanese remained
unwelcome. For
example, Frank Miyamoto explains what made his father left
Carrington. He says that the
father found workplace harassment unpleasant as he was
discriminated against by the
White workers. The Japanese were threatened both workers and
their families. Besides
the harassment at work, the Americans passed laws that
discriminated against the
Japanese and even the Supreme Court (in 1922) ruled that the
Japanese could not be
naturalized in attempts to limit their growth. For example, the
anti-alien laws in
California were enacted (in 1913) to bar Japanese from owning
land. Congress also
passed the Immigration Act in 1924 which halted the Japanese
migration to the United

49. States.
Nonetheless, despite all these efforts to restrict the number of
the Japanese, the
community grew as the next generation was born. This
generation were US citizens by
birthrights and adapted to the American way of life as they
attended public schools and
grew up with their non-Japanese American children's
classmates. However,
discrimination of this new generation continued in restaurants,
theater and swimming
pools where they were treated differently as their fellow white
American children
counterparts even in 1940 CRO. This is explained by Frank
Yamasaki who gives an
account of how the new Japanese generation born in America
continued facing
discrimination. He gives an account of how he was surprised
when they had to be
discriminated against by the cashier because they were racially
different.
Therefore, from this video, we get to understand how

50. American’s were racists
against in other races more than 100 years ago. We get to learn
that despite the
discrimination and biased treatment against the Japanese, their
resolve to stay and grow
in America was never collapsed. They stayed foot and faced all
the challenges but
ultimately remained in America. The moral lesson we can learn
from this video is that
even if one is facing challenges, really tough ones in life, one
should always stay focused
on their goals. The Japanese could have decided to leave
America and get back home,
however, they would have meant that they gave up and hence
could have not have
managed to accomplish their very first goal of migrating into
the United States.
Therefore, this video presents me with a historically rich
knowledge of how the Japanese
found their way into the United States more 100 ago and how
this community persevered
to grow despite the apparent racism against them by their fellow
American counterparts.

51. Asam100bb
Xinyu Shang
Reading journal week3
On the reading A Shocking Fact of Life the author gives a
recount of their
historical past and a trail of events on how she realized that she
was a Japanese and not a
Yankee. She does not admit that she is not a Japanese till
revealed to her by her parents.
The author is told by her mother, “Your father and I have
Japanese blood, and so do you,
too” (A shocking fact of life 4). The parents to the narrator had
migrated to the U.S to
look for greener opportunities, especially education. The
narrator says, “Mother and her
sister sailed into the port looking like exotic tropical butterflies
( A shocking fact of life
6). This statement shows that they looked like foreigners. Also,
the author gives a recount
of the Japanese culture and the lifestyle of their family. The