This document discusses the ethics of machine learning algorithms. It notes that algorithms can contain bias since they reflect the values of their developers and are designed with a purpose in mind. This can lead to unfair outcomes like discrimination in areas like advertising or pricing. The document also discusses how algorithms may take inappropriate actions based on inconclusive data, by extrapolating correlations without establishing causation. Overall, the document examines some of the ethical issues around algorithmic bias, discrimination, and actions based on uncertain data that machine learning applications need to address.

1. The Ethics of Machine Learning Algorithms
Introduction
The advent of the ‘Information Age’ gave birth to novel technologies capable of
dealing with large data sets and capable of inferring useful information from
large data sets. One such technology is Machine Learning. Machine Learning is
‘‘any methodology and set of techniques that can employ data to come up with
novel patterns and knowledge, and generate models that can be used for
effective predictions about the data’’ (Van Otterlo, 2013).
Machine Learning has been used extensively to draw significant insights from
unwieldy data sets, and have been proposed to be the “future” of algorithms and
analytics (Tutt, 2016). More and more responsibilities are being delegated to
machine learning algorithms (Mittelstadt et al, 2016). For instance, Machine
Learning algorithms are employed to recognise human bias or inaccurate
information, as displayed by Wikipedia’s Objective Revision Evaluation Service,
to act as recommendation systems for customers advising them on their next
purchase based on their purchase history (Vries, 2010) and to facilitate in the
understanding of behavioural data emerging from the ‘Internet of Things’
(Portmess and Tower, 2014). This list is by no means exhaustive.
Given their wide applications, it is plausible to assume that Machine Learning
algorithms are an integral set of technologies to industrial and academic settings.
(Citron, 2007) argues that developers of algorithms have a tendency to accept
that automated processes, as those encountered during the application of
Machine Learning algorithms, are true and precise, overcoming the hurdle of
human bias (Naik and Bhide, 2014). As controversial as this statement is, it does
highlight the dependency of Machine Learning algorithms in industrial and
academic settings.
However, these technologies come at an ethical cost. Given that algorithms are
unavoidably influenced by personal opinion, since the parameters are finely
tuned by the developer (Wiener, 1988), then it comes as no surprise that
designing and developing algorithms does not guarantee that ethical
considerations will not be violated. For example, profiling algorithms have to
discriminate to achieve their purpose, but sometimes concomitantly are
derogatory (Barocas and Selbst, 2015).

2. The publically familiar definition of algorithms is broad. An algorithm can
essentially be any set of instructions provided to achieve a desired outcome. For
the purpose of this essay, this definition is too broad and should be restricted
appropriately. Therefore, this essay will adopt Hill’s definition of an algorithm as
a “mathematical construct with a finite, abstract, effective, compound control
structure, imperatively given, accomplishing a given purpose under given
provisions” (Hill, 2015). This definition takes into account the implementation
of an algorithm into a technology, and the application of that technology aimed at
fulfilling a predefined purpose. However, the discussion regarding ethics
implicated in the application of technologies based on Machine Learning
algorithms will be limited. Moreover, this essay will only focus on algorithms
whose decision-making is difficult to explain from a human perspective due to
the algorithm’s opaque nature (how a new input will be handled), and
algorithms whose actions are difficult to predict. Algorithms implemented for
mundane tasks, such as manufacturing, are not considered here. The essay will
discuss several broad ethical concerns regarding the use of algorithms, each
placed under a distinct subcategory.
Algorithmic Bias
Algorithms inescapably contain some element of bias, although some would
argue that their deployment is actually an approach to overcome human bias
(Naik and Bhide, 2014). One example of bias, classed as ‘technical bias’, is
observed when the listing of companies for a search result follows alphabetical
order, potentially resulting in more business for companies that appear earlier in
the alphabet (Friedman and Nissenbaum, 1996).
The reason why algorithms can be bias is because the values of the developer are
institutionalised into the algorithm, and because algorithms are designed with an
intended purpose in mind (Macnish, 2012). During the course of the algorithm’s
development, there is not always an objectively correct choice, but rather there
is a myriad of alternatives that could still achieve the same desired objective.
The bias can arise from many situations (Friedman and Nissenbaum, 1996). For
example, bias can arise during supervised learning tasks. During supervised
learning, the Machine Learning algorithm learns from human-tagged data inputs.

3. The labelling of these inputs can inadvertently be bias (Diakopoulos, 2015).
Furthermore, the interpretation of the outputs of the algorithm can also be bias
(Hildebrandt, 2011).
Not many improvements can be made for bias emerging from technical
constraints (i.e. technical bias). However, a collective effort could reduce bias
emerging from other sources, such as social bias that become manifested in the
algorithms. Raymond (2014) proposes that human monitoring may be an
effective approach to reduce some bias.
Unfair Outcomes Based on Discrimination
One broad category of ethical considerations is discrimination resulting from
profiling (Vries, 2010). Classification algorithms are often employed in industry
and academia for various purposes. These classification algorithms classify new
inputs based on the characteristics of the training data. The description of
classification algorithms already hints at some ethical issues that can be
encountered during the employment of these algorithms. Practical examples of
discrimination include the delivery of online advertisements based on perceived
ethnicity (Sweeny, 2013), and personalised pricing (Danna and Gandy, 2002).
Another example is an infamous ‘Data Science gone wrong’ story pertinent to
classification algorithms, which is of Target’s unfortunate labelling of a 13-year-
old girl as pregnant, before her father was even aware of her pregnancy
(Golgowksi, 2012). It is important to note though, that the last example has
ethical concerns reaching beyond discrimination. The ethics of classification
algorithms are even more extensive in the context of classification algorithms
recruited for medical diagnoses (Cohen et al, 2014). Such models aid in the
diagnosis and also recommend viable treatment plans to physicians (Mazoue,
1990).
Schermer (2011) argues that the act of discriminatory treatment itself is not
ethically challenging, but that the resulting effects from discriminatory treatment
are ethically challenging. However, (Mittelstadt et al, 2016) suggests that
Schermer is ‘muddling’ up the concepts of bias and discrimination, incorrectly
using discrimination to describe bias.

4. There are multiple diverse reasons to consider discriminatory effects as adverse,
implying that they should be prevented if possible, or at least minimised to
almost extinction. It has been postulated that discrimination can contribute to
self-fulfilling prophecies and that it can exaggerate stigmatisation (Macnish,
2012). Furthermore, discrimination may reinforce advantages and
disadvantages already existing in society, exacerbating social discrepancy, which
violates ethical and legal principles of fair treatment (Newell and Marabelli,
2015).
Fortunately, it may be possible to develop algorithms that do not take into
account sensitive attributes or features, which could potentially contribute to
discriminatory outcomes (Barocas and Selbst, 2015). For instance, (Kamiran and
Calders, 2010) have developed algorithms that do not take into account gender
or ethnicity, without a loss in performance. However, this is strongly subject to
context; some classification tasks may require the inclusion of sensitive
attributes in order to perform to an adequate level. Thus, the question then
becomes, what classification tasks can be delegated to classification algorithms
in the first place? Moreover, using proxies for sensitive attributes may not be an
effective solution (Romei and Ruggieri, 2014).
Inappropriate Actions Based on Inconclusive Data
Correlations are commonly used to complement inductive knowledge in
decision-making tasks. Correlations based on a large volume of data are
extrapolated to direct actions, without the requirement to establish a causal
relationship first (Hildebrandt, 2011). This can result in unjustified actions based
on inconclusive evidence. Mittelstadt et al (2016) coin this as ‘actionable
insights’. To exacerbate this issue of actionable insights, (Lazer et al, 2014) argue
that correlations identified in large proprietary data sets are seldom
reproducible due to their commercial sensitivity, and thus often present the
opportunity of being fabricated. Ananny (2016) postulates that correlations are
doubly uncertain; not only can they result in actionable insights, but acting upon
on correlations concerns individuals, while the knowledge concerns the
population from which the conclusion was drawn from (Illari and Russo, 2014).

5. Furthermore, the sheer volume of ‘Big Data’ does not imply that fundamental
theoretical aspects of measurement, reliability and face validity can be neglected,
and that solely large quantities of data do not serve as an appropriate
replacement for improper methodology (Boyd and Crawford, 2012). This
concern is not specific to correlations either, as it is applicable to all domains
where ‘Big Data’ is employed to draw conclusions.
One possible solution to this problem is to train data analysts not to draw hasty
conclusions, and to ensure that data quality is of the highest form. Whether
actions based on conclusive evidence are deemed ethically acceptable is not a
discussion that is pertinent to the discussion of actionable insights.
Accountability
Another major ethical concern of Machine Learning algorithms, as suggested by
(Mittelstadt et al, 2016), is traceability. The authors coined this term to describe
the difficulty of assigning responsibility or culpability to an agent when ethical
concerns, such as any form of harm tout court, are violated during the
application of Machine Learning algorithms. Firstly, it is important to note that
identifying whether some Machine Learning algorithms suffer from some ethical
concerns due to human subjectivity is difficult to decipher.
Typically, it is the designers and users of the algorithm that are held accountable
if adversity is encountered (Kraemer et al, 2011). However, (Matthias, 2004)
argues that blame can only be equipped when the designer or user has some
element of control over the algorithm, and is also ill intentioned. Often, the
developer or user does not have as much control over the algorithm as
conventionally believed, creating an ‘accountability gap’, where it is not clear to
determine who is culpable for the violation of ethical considerations (Cardona,
2008). In essence, shifting blame upon the developer may only be applicable to
cases where the operational rules of the algorithm are hand-written, and not
rules that the algorithm learns independently (i.e. Machine Learning algorithms).
The opposing view suggests that algorithms, which act independently and result
in ethically unjust outcomes, should be held accountable (Wiltshire, 2015).
However, this may provide an opportunity for developers to assign blame to the
algorithm (Crnkovic and Curuklu, 2011). Furthermore, the capacity of algorithms

6. to make moral and ethically justifiable decisions is still disputable (Anderson,
2008).
Nonetheless, it can be argued that developers and users should monitor the
workings of the algorithm for any ethical violations, as done so by (Fole and
Ruddick, 2004). Allen et al. (2006) insinuate that the void in ‘machine ethics’
needs to be filled, so that ethical violations can be avoided. There is much
research currently focused on developing algorithms with a concrete
understanding of moral and ethical responsibilities (Wiegel and Berg, 2009), so
that neither extreme of assigning blame (solely blaming the developer or the
algorithm) has to be adopted.
Opacity of Algorithms
The opacity of Machine Learning algorithms is another major concern in the
Literature. Supposedly, algorithms that constitute mechanisms that are difficult
to explain, or algorithms whose behaviour is difficult to predict, are also
concomitantly difficult to control, regulate and correct (Tutt, 2016). Introna and
Nissenbaum (2000) is among pioneering publications discussing this topic of
transparency. The authors suggested that search engines filter information
based on market conditions, with a slight preference towards powerful actors. In
the paper, the authors discuss corrective mechanisms intended to improve the
‘fairness’ of search engines.
Anyhow, the accessibility of information elucidating the functionality of
algorithms is often restricted because of national security (Leese, 2014),
competitive advantage (Glenn and Monteith, 2014), or privacy in general. Thus,
transparency is not always desired, especially not at the expense of invasion of
privacy. On the contrary, it can be argued that the privacy of an algorithm is not
as important, except for cases concerning competitive advantage, and that only
the privacy of the data itself should be regarded as important. The argument of
exposing the algorithm’s architecture can also be swayed in the opposite
direction, that is, informing the public about the architecture of algorithms may
prompt some users to abusively manipulate the algorithm for tailored use, while
also placing non-tech-savvy users at a disadvantage (Granka, 2010).

7. Intelligibility also contributes to the opacity of algorithms (Burrell, 2016).
Machine Learning algorithms are commonly described as ‘black boxes’, which
are fed input to produce an output, with the mechanisms of the algorithm being
obscured. For many applications of Machine Learning algorithms, the developer
cannot articulate the exact procedure needed to achieve a particular goal, hence
the requirement for these algorithms in the first place (Datta et al, 2016). Even
algorithms whose operation can be decomposed into a set of hand-written rules
have the capacity to be inscrutable (Kitchin, 2016). To complicate matters even
more, algorithms are often designed by groups of engineers, rendering a
comprehensive understanding of the algorithms extremely difficult, if not
practically infeasible (Sandvig et al, 2014). It is also important to note that the
intelligibility of algorithms can be limited by the questioner’s capacity to
understand it. The problem with this is that full legitimate consent cannot be
granted if the mechanisms of the algorithm are not fully understood (Schermer,
2011). To make the algorithms more accessible, the operational logic of the
algorithm could be explained in simplified terms. However, (Kitchin, 2016)
suggests that this may only be effective for simple algorithms, and may not be a
plausible solution for more complex algorithms, which could then still
potentially escalate to a lack of trust for both the algorithm, and the data analyst
(Rubel and Jones, 2014). It is important to recognise that trust can be placed in
the algorithm independent of the data analyst, but whether this is appropriate is
still controversial, as in the case of autonomous weapons (Swiatek, 2012).
(Zarsky, 2013) suggests that transparency should be disclosed to trained third
parties, seen as regulators, acting in the interest of the public, as opposed to the
questioners themselves to prevent any loss of trust. This sense of trust is pivotal
(Cohen et al, 2014) and can alleviate concerns with the opacity of algorithms
based on personal information (Mazoue, 1990).
Transformative Effects
The results obtained from Machine Learning algorithms can change the
perceptions of how humans perceive the world. These transformative effects can
lead to ethically challenging concepts.

8. One example of such ethical concern is observed during the employment of
personalisation algorithms. These algorithms obscure content encountered by
the user that is believed to be unaligned to the user’s belief, or be contradictory
to the user’s belief (Barnet, 2009). Ananny (2016) suggests that algorithms need
not be explicitly coercing to influence perception and decision-making through
filtering. Personalisation algorithms can be seen as supporting the user by
filtering out relevant information from the masses of information presented, or
they can be seen as controlling by presenting the user with only information the
personalisation algorithms deems relevant (Bozdag, 2013). This could result in
ethical violations when the choice of what data is presented prioritises the
interests of a third-party over the interests of the user (Applin and Fischer,
2015), as commonly demonstrated with online consumers (Coll, 2013). The
users may then be prompted to make decisions not purely based on their own
preference, but rather, choices will be based on inherently subjective restrictions
of information load (Johnson, 2013).
The diversity of information presented is paramount in exercising autonomy
(Van Den Hoven and Rooksby, 2008). Thus, any inappropriate restriction of
information diversity can disrupt the autonomy of the user. Furthermore, a lack
of information diversity may handicap the exploration of new ideas and options
(Raymond, 2014). To counteract this ethical concern, (Lewis and Westlund,
2015) propose that personalisation algorithms should be taught to be aware of
this ethical consideration, of not being too persuasive at the expense of limiting
the decisional autonomy of the user.
However, it’s not only personalisation algorithms that can have a transformative
impact. The general utilisation of algorithms is also transforming the
conventional notions of informational privacy (Mittelstadt et al, 2016). For
instance, (Schermer, 2011) confronts identifiability in profiling algorithms by
arguing that profiling algorithms aim to designate data inputs into meaningful
assemblies, for which individual identity is truly trivial. Yet, (Van Wel and
Royakkers, 2004) rebut by suggesting that profiling is a form of de-
personalisation by assigning judgements based on group characteristics, and not
individual merit. However, it must be noted that this rebuttal addresses an

9. ethical concern not related to informational privacy, thus its relevance in this
context is negligible.
What is classified as ‘identifiable’ still remains rather controversial. Data subjects
who are anonymised prior to processing are not considered ‘identifiable’
(European Commission, 2012), thus anonymised data is not considered as a
breach of privacy. Furthermore, algorithmic techniques that do not require
access to personal information may alleviate the risks of privacy breaches (Fule
and Roddick, 2000).
Conclusion
This essay has summarised some ethical concerns encountered in the literature,
but is by no means exhaustive. More ethical challenges will emerge as the
application of algorithms becomes more extensive. (Tutt, 2016) suggests that the
challenges regarding the ethics of Machine Learning algorithms will become
increasingly difficult as the complexity of the algorithms increases. The
increasing utilisation of Machine Learning algorithms, or algorithms in general,
will undoubtedly have an impact on policies regarding the use of such
technologies. These policies should be designed in a way that does not stifle the
predictive power of the algorithms, but rather, algorithms should be designed
with ethical considerations in mind.
Already existing ethical issues also demand further research (Mittelstadt et al,
2016). For instance, it is still unclear whether all the ethical concerns discussed
here are applicable to all types of data sets; are these ethical considerations only
pertinent when data is collected on humans, as opposed to objects such as cars?
Theoretically, ethical concerns may be difficult, if not impossible, to avoid.
However, it may be possible to minimise ethical concerns by training developers
and users to be aware of the ethical issues arising from the application of the
algorithms in the specified context. Furthermore, establishing governing bodies
or a framework of regulatory policies such as the EU General Data Protection
Regulation (2016/679), can facilitate in the fair regulation of algorithmic
applications.

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