The document is a list of article titles and short summaries written by Neil Raden for Diginomica in 2019-2020 on topics related to AI and AI ethics. The articles address issues such as algorithmic opacity, bias in AI, measuring fairness, data privacy, explainability of AI models, and ethical concerns regarding applications of AI like facial recognition and hiring algorithms. Many of the articles discuss ongoing debates around developing practical frameworks for ensuring AI systems are developed and applied ethically.

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Neil Raden’s 2019-2020 Diginomica articles AI and AI Ethics only
The titles are live links.
The problem of algorithmic opacity, or "What the heck is the algorithm doing?"............. 2
Can fairness be automated with AI? A deeper look at an essential debate........................ 2
Can we measure fairness? A fresh look at a critical AI debate ............................................ 2
Musings on China's 'Global Initiative on Data Security' and the problem of security "back
doors" ..................................................................................................................................... 3
Revisiting ethical AI, part two - on data management, privacy, and the misunderstood
topic of bias............................................................................................................................ 3
Revisiting ethical AI - where do organizations need to go next?......................................... 3
Does small data provide sharper insights than big data? Keeping an eye open, and an
open mind............................................................................................................................... 3
The fragility of privacy - can differential privacy help with a probabilistic approach?....... 3
AI inevitability - can we separate bias from AI innovation?................................................. 3
The explainability problem - can new approaches pry open the AI black box?.................. 4
Unethical AI unfairly impacts protected classes - and everybody else as well ................... 4
AI ethics - why teaching ethics and "ethics training" is problematic................................... 4
Rethinking AI Ethics - Asimov has a lot to answer for.......................................................... 4
AI readiness isn’t just a technology issue – ethics matter too............................................. 4
AI in healthcare - will it help or just make things worse? .................................................... 4
Artificial General Intelligence will not resemble human intelligence.................................. 4
How can we measure fairness beyond bias, discrimination and other undesirable effects
in AI?....................................................................................................................................... 5
AI explainability and interpretability - we have a long way to go ....................................... 5
Still rife with ethical issues, data sharing has a bright side too ........................................... 5
Surveillance AI with a thermal heat twist - anotherlook at Athena Security, with COVID-
19 in mind............................................................................................................................... 5
COVID-19 pandemic models - are Machine Learning models useful?................................. 5
The AI ethics review - eight sticking points we haven't resolved ........................................ 5
Apple and Johnson & Johnson team up for Heartline Study app - a healthcare wearables
breakthrough, or a questionable study?............................................................................... 5
Natural Language Processing - the term is everywhere, but a true NLP app is hard to find
................................................................................................................................................. 6
The data science conundrum - why do commercial businesses eschew causal analysis? .. 6

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The problem of AI explainability - can we overcome it?...................................................... 6
Federated machine learning is coming - here's the questions we should be asking .......... 6
Digital twins for personalized medicine - a critical assessment........................................... 6
AI for AI - evaluating the opportunity for embedded AI in data productivity tools............ 6
AI has a black box explainability problem - can outcome analysis play a role?.................. 6
Precision medicine and AI - data problems ahead................................................................ 7
Facial recognition revisited - can it save lives and actually protect privacy?...................... 7
Beyond the evil facial recognition myth - can AI play an ethical role in predictive threat
detection?............................................................................................................................... 7
Dismissing ethical issues means AI has a long way yet to go in the enterprise .................. 7
To understand AI advancements in health care, there are two storylines we must follow7
Data for good and AI ethics - a movement or just a conversation?..................................... 7
Can we create better algorithms for screening candidates - and reduce hiring bias?........ 8
New York state regulators pushed back on databrokers for insurance - will other states
and industries follow?............................................................................................................ 8
Are hiring decisions ready for AI? How repeatable algorithms can harm people............... 8
Data brokers and the implications of data sharing - the good, bad and ugly...................... 8
The blush is off the rose of Machine Learning…maybe........................................................ 8
When does machine learning acquire a social context?....................................................... 8
Data for Good - a question of relevancy ............................................................................... 9
Thinking about thinking......................................................................................................... 9
Modeling humans for personalized medicine - a prescription for trouble?........................ 9
Getting closer to guidelines on ethical AI.............................................................................. 9
How hard is it to solve the ethics in AI problem?................................................................. 9
Retrofitting AI - key adoption issues in the enterprise 2019-2020 ...................................... 9
The problem of algorithmic opacity, or "What the heck is the algorithm doing?"
Opacity in AI used to be an academic problem - now it's everyone's problem. In this piece, I
define the issues at stake, and how they tie into the ongoing discussion on AI ethics.
Can fairness be automated with AI? A deeper look at an essential debate
I've addressed whether fairness can be measured - but can it be automated? These are
central questions as we contend with the real world consequences of algorithmic bias.
Can we measure fairness? A fresh look at a critical AI debate

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By now, most AI practitioners acknowledge the universal prevalence of bias, and the problem
of bias in AI modeling. But what about fairness? Can fairness be measured via quantifiable
metrics? Some say no - but this is where the debate gets interesting.
Musings on China's 'Global Initiative on Data Security' and the problem of security
"back doors"
A review of 'Global Initiative on Data Security' led me to an exchange with a company doing
business in China. With new 5G security issues on the horizon, it's a good time to reflect on
the implications of "back doors," ethical AI, and where the responsibility lies.
Revisiting ethical AI, part two - on data management, privacy, and the
misunderstood topic of bias
No, you can't program your AI for empathy or ethics. But you can certainly confront the
problem of bias. In part two of revisiting AI ethics, we examine how bias, data management,
and privacy should be addressed.
Revisiting ethical AI - where do organizations need to go next?
AI ethics are having a hard time keeping up with AI. Academic debates may be interesting,
but organizations need a practical AI ethics framework. Where do we go from here?
Does small data provide sharper insights than big data? Keeping an eye open, and
an open mind
Big data gets all the hype. Small data is perceived as inadequate for today's in vogue
algorithms. But by overlooking small data, are enterprises missing a superior source of
insight?
The fragility of privacy - can differential privacy help with a probabilistic approach?
Enterprises crave personalized data, but protecting privacy is non-negotiable. Anonymizing
the data brings limitations. Can differential privacy help?
AI inevitability - can we separate bias from AI innovation?

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AI evangelists pay lip service to solving AI bias - perhaps through better algorithms or other
computationalmeans. But is this viable? Is bias in AI inevitable?
The explainability problem - can new approaches pry open the AI black box?
Explainability has moved from an academic debate to a significant barrier toAI adoption. A
slew of new tools and approaches are intended toaddress this problem - but will they close
the explainability gap?
Unethical AI unfairly impacts protected classes - and everybody else as well
We've established that unethical AI hurts protected classes - but it doesn't stop there. Across
industries and regions, unethicalAI can impact the entire population. Here's some questions
to consider.
AI ethics - why teaching ethics and "ethics training" is problematic
We've been trying to teach "ethics" for years. Teaching AI ethics to organizations is proving to
be just as problematic. Yet as the urgency of ethical AI increases, we need a way forward.
What are the options?
Rethinking AI Ethics - Asimov has a lot to answer for
Is the current obsession with AI Ethics doing any good? Maybe Asimov's Three Laws of
Robotics wasn't such a great starting point afterall
AI readiness isn’t just a technology issue – ethics matter too
With the possibility of serious negative consequences springing directly from AI projects,
there needs to be more focus and discussion around ensuring ethical standards are upheld.
AI in healthcare - will it help or just make things worse?
Those who laud the potential benefits of AI in healthcare are too often silent on the risk of
exacerbating the healthcare system's current failings
Artificial General Intelligence will not resemble human intelligence
Airplanes don't flap their wings like birds, and artificial general intelligence (AGI) will never
think like the human brain, which is more complex than we imagine

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How can we measure fairness beyond bias, discrimination and other undesirable
effects in AI?
The question of AI ethics and bias remains a potent one - but are we framing these issues in
the right way? A better approach would be centered around AI fairness. But can fairness be
monitored?
AI explainability and interpretability - we have a long way to go
AI explainability remains an important preoccupation - enough so to earn the shiny acronym
of XAI. There are notable developments in AI explainability and interpretability toassess.
How much progress have we made?
Still rife with ethical issues, data sharing has a bright side too
Data brokers and personal data collection continues to cross ethical lines. But there are bright
spots - including supply chain data sharing startup Aperity. I talked with their CEO about how
their approach is different, and why AI and machine learning play a crucial data processing
role.
Surveillance AI with a thermal heat twist - anotherlook at Athena Security, with
COVID-19 in mind
The ethical questions raised by AI-powered surveillance are numerous. Athena Security has
some thoughtful answers - but what happens when we extend those capabilities into thermal
heat detection?
COVID-19 pandemic models - are Machine Learning models useful?
Applying Machine Learning toCoronavirus data is tempting - but deeply problematic.
DataRobot shared lessons on working with smaller data sets, but the predictive limitations of
ML for assessing pandemics go much further.
The AI ethics review - eight sticking points we haven't resolved
AI tech is moving quickly - but the ethical problems aren't going away. Here's eight AI ethics
issues that persist.
Apple and Johnson & Johnson team up for Heartline Study app - a healthcare
wearables breakthrough, ora questionable study?

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Johnson & Johnson recently announced its Heartline Study app, which utilizes Apple Watches
and iPhones, with the expected fanfare. But is this really an advancement in wearables? And,
based on the official guidelines of clinical trials, does it qualify as a study?
Natural Language Processing - the term is everywhere, but a true NLP app is hard
to find
Just about every vendor claims they have NLP capabilities of some kind. But not all apps
tagged with the "NaturalLanguage Processing" label are created equal.
The data science conundrum - why do commercial businesses eschew causal
analysis?
When we talk about the limits of data science, we often revert to issues like scalability, or the
lack of talent. But there's another burning question that data science projects overlook at
their peril: just how important is causation?
The problem of AI explainability - can we overcome it?
Explainability is not just a roadblockto AI adoption - it also has implications for public health
and safety. This is how the tensions between transparency, accuracy and performance are
coming to a head.
Federated machine learning is coming - here's the questions we should be asking
With the introduction of Google's Tensor Flow federated, the hype around federated machine
learning is surging. But there are important questions about data privacy, performance and
cost that need answering.
Digital twins for personalized medicine - a critical assessment
Digital twins are amongst the most hyped technologies in recent years. It's time for a critical
look at the possibilities - and drawbacks - of digital twins for modern medicine.
AI for AI - evaluating the opportunity for embedded AI in data productivity tools
AI-for-AI is gaining attention - but is the capacity for embedding AI for data productivity
overlooked? Let's do a gut check on the views of industry experts.
AI has a black box explainability problem - can outcome analysis play a role?
One of AI's major stumbling blocks is explainability. But can we address AI's black box by
evaluating outcomes? One example from the insurance industry pushes this debate forward.

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Precision medicine and AI - data problems ahead
The promise of personalized medicine has sparked a proliferation of AI hype. But the
obstacles AI faces in the healthcare industry are daunting. Lookno further than data silos -
and the factors that spawned them.
Facial recognition revisited - can it save lives and actually protect privacy?
Facial recognition technology has an ominous reputation - and for good reason. But are there
beneficial applications? AthenaSecurity and D-ID believe the answer is yes. Here's my take
on our recent discussions.
Beyond the evil facial recognition myth - can AI play an ethical role in predictive
threat detection?
The stories on facial recognition advances lean strongly towards the concern side, with a host
of consequences poorly addressed during rollouts. But is there an AI-for-good role in threat
prediction?
Dismissing ethical issues means AI has a long way yet to go in the enterprise
The time has come think critically about the value of AI as it stands, and whether to be
concerned that a concerted effort to press it forward to true intelligence bypasses ethical
questions.
To understand AI advancements in health care, there are two storylines we must
follow
Yes, health care needs AI - but maybe not in the ways we think. A new book on AI's medical
potential needs a critical eye. With AI, there is always a human consequence beyond the tech
storyline.
Data for good and AI ethics - a movement or just a conversation?

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The issue of AI ethics has sharpened - ideas for governing AI and ethical oversight are gaining
a foothold. But will they have any teeth? And what about the possibility that AI can oversee
itself?
Can we create better algorithms for screening candidates - and reduce hiring bias?
A new research paper from Georgia Tech takes a surprising position on algorithmic bias in
hiring. Their view: we can reduce screening bias if algorithms take the impacted demographic
groups into account. Here's my critique.
New York state regulators pushed back on data brokers for insurance - will other
states and industries follow?
A warning letter from New York State's Department of Financial Services (DFS) raised far-
reaching data privacy questions for the insurance industry. With the increasing role of
algorithmic claims processing, this is an ethics debate we can't ignore.
Are hiring decisions ready for AI? How repeatable algorithms can harm people
AI marketing literature extols the benefits of algorithmic hiring. But the problem of
algorithmic bias and hiring fairness raises serious questions.
Data brokers and the implications of data sharing - the good, bad and ugly
The term "data sharing" is expanding, but in a problematicway that raises flags for
companies and consumers alike. Neil Raden provides a deeper context for data sharing
trends, dividing them intothe good, bad and ugly.
The blush is off the rose of Machine Learning…maybe
Geeky reviews of two ML studies - and something nice to say about Tom Davenport!
When does machine learning acquire a social context?
Whether it’s MCS, simple linear regression or Adversarial Neural Networks, if it affects
people, then there is an ethical issue.

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Data for Good - a question of relevancy
Data for Good? A personal view from Neil Raden.
Thinking about thinking
Thoughts on thinking in an AI context.
Modeling humans for personalized medicine - a prescription for trouble?
Digital Twin technology for modeling individualpeople, or “personalized medicine,” is a
concept for simulating the whole.
Getting closer to guidelines on ethical AI
AI is moving fast enough that our ethical frameworkis falling behind. Here's a critique of
four AI characteristics, and a new way of thinking about AI ethics.
How hard is it to solve the ethics in AI problem?
Advances in deep learning techniques throw up fresh challenges in the field of ethical AI.
Much work needs to be done before we get comfortable with applied AI. It won't be easy.
Retrofitting AI - key adoption issues in the enterprise 2019-2020
AI technology has moved beyond the hype phase, but short-term adoption of AI in
organizations will primarily come through third-party software and relatively
straightforward application of Machine Learning, even though many organizations are not
yet ready for the latter.

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