ethics for artificial intelligence, machine learning and automated decision making
TRANSCRIPT
Ethical Perspectives on Personal Data, Machine Learning and Automated Decision Making
Dr Steven [email protected]
Objectives
• Discuss some of the ethical issues associated with
personal data, machine learning and automated
decision making.
• Present a general and pragmatic framework for
assessing the risk associated with using new types of
personal data, and new applications of predictive
models.
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Agenda
1. Introduction
2. A bit about ethics
3. Ethics and personal data
4. Ethics, machine learning and automated decision
making. A risk management framework
Introduction
• Why consider ethical issues in automated decision making?
– Automated decision making, using personal data and based
on predictive models (e.g. credit scoring and direct marketing
models) is old hat to those of us working in financial services.
– In widespread use since 1960s.
– Lots of existing laws and regulations.
– It’s data driven and unbiased, right?
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Introduction
• Recent explosion in Machine Learning/Predictive Analytics
based systems, which are replacing or supporting human
decision making in many walks of life
• Siegal (2016) lists well over 100 uses for predictive models.
• All automated decision making systems display bias!
– The question is: Is it unfair, unethical or illegal bias?
• E.g. when did you last assess the gender, race, religion or
sexual bias expressed by your credit scoring systems?
• On-going concerns being raised by governments, regulators
and the media over the data that organisations hold, and
the uses to which it is put.
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Agenda
1. Introduction
2. A bit about ethics
3. Ethics and personal data
4. Ethics, machine learning and automated decision
making. A risk management framework
1. Ethics, sometimes known as philosophical ethics, ethical theory,
moral theory, and moral philosophy, is a branch of philosophy that
involves systematizing, defending and recommending concepts of
right and wrong conduct, often addressing disputes of moral
diversity. The term comes from the Greek word ἠθικός ethikos from
ἦθος ethos, which means "custom, habit". The superfield within
philosophy known as axiology includes both ethics and aesthetics
and is unified by each sub-branch's concern with value…http://en.wikipedia.org/wiki/Ethics
Alternatively
2. It’s about right and wrong.
Ethics is….
Subjective, personal, unique… 7
A bit about ethics. Definitions
Common ethical frameworks
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Consequentialist“The means justify the ends”
Non-Consequentialist“It’s more about the journey than where you end up…”
Virtues“Virtuous modes
of behaviour”(Aristotle)
(Human) rights“Right to life, liberty,
property, privacy, etc.”(Locke and Rawls)
Religious
Teaching(e.g. the ten
commandments)
Kant’s ethical
theoryUniversality: Ethical is something
all rational people would agree with
Golden rule“Do unto others as you
would have done unto you”
(Do no evil)
Utilitarianism“Greatest good for the
greatest number”(Jeremy Bentham and
John Stuart Mills)
Ethics
Ethics in practice
• All ethical frameworks have their weaknesses…
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A bit about ethics. Relevance in the real
world…
• If I follow all laws and regulations, then that’s all I need
to worry about right?
• Lots of laws allow unethical
actions to occur:
“It is illegal to give alcohol to a child under 5”
Another example is tax avoidance:A great example of what we mean when we talk about the
spirit of the law as opposed to the letter of the law
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Legal
Ethical
A bit about ethics. Relevance in the real
world…
• It pays to be ethically minded:
• Organizations adopting ethical policies tend to reap the
benefits.
• Largest ever study of the relationship between ethical
performance and financial performance:
– Losses from reputational damage, resulting from actions
that are perceived to be unethical, are particularly severe.
– “Corporate virtue in the form of social and, to a lesser
extent, environmental responsibility is rewarding in more
ways than one.” (Orlitzky et al. 2003)
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A bit about ethics. Summary
• There are many ethical perspectives. We all have our own
view on the rightness/wrongness of different actions.
• Ethical theory is all very well, but putting it into practice is
difficult. The world is a messy mixed up place.
• The one thing that can be said to apply across all ethical
frameworks:
– An ethical action is one which the perpetrator can defend in
terms of more than self interest. (Finlay 2000).
• Ethics pays. A well thought out, well implemented ethical
corporate policy benefits both organizations and
consumers/individuals in the long run.
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Agenda
1. Introduction
2. A bit about ethics
3. Ethics and personal data
4. Ethics for machine learning and automated decision
making. A risk management framework
Ethics, data and Machine learning
Whose data is it anyway?
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Utilitarian
orientated
perspective
Kantian/Rights
based perspectiveMy data is a
resource to be
harvested and put
to use.
Constraints (laws) to
prevent specific
abuses and misuse of
my data.
My data is a part
of who and what I
am. It’s mine!
My data should be
treated with respect,
just as I expect to be
treated with respect.
I will decide how data
about me is used. You
have no right to use my
data without my
permission.
Better data &
predictions =
better outcomes.
Everyone benefits.
Ethics, data and decision making.
Whose data is it anyway?
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Approach Pros Cons
Utilitarian orientated
perspective
• More/better data means better
decision making.
• More get the very best deals (if
they warrant it).
• Social benefits. More data to
support national / community
initiatives (e.g. medical research
and counterterrorism).
• Best for the economy.
• People less in control of their
own destinies.
• Better predictions does not
always equate to increased in
well-being.
• The have-nots have even less.
• Once the data is out there, it’s
out there for good.
Kantian/Rights based
perspective
• Each individual has control over
their data and the uses to which
it is put.
• Less social exclusion.
• Right to change/withdraw
permission to use data, including
“Right to be forgotten.”
• Poorer decisions for individuals
may result, if data is withheld or
otherwise unavailable.
• Lower economic benefits.
• Society as a whole may suffer
because large scale studies are
data limited. (e.g. medical
research and counter terrorism).
Ethics, data and decision making.
Is more data and better prediction always better?
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• More/better data leads to the promise of near perfect
predictions in some areas. Is this a good thing?
• Sometimes:
– Identify terrorist subjects with high degree of certainty
– Predict that a heart attack is very likely in the next 24 hours
– Long term compatibility on a dating site
– …..
• But not always
– Near perfect insurance claim predictions are no benefit to
anyone (except the insurer)
– Do I want to know, years in advance, when I am likely to die?
– …..
Ethics, data and decision making.
Whose data is it anyway?
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What’s the direction of Travel?
USA, has to date, followed a more utility
based model. Use data for whatever you
want, but we will legislate where needed.
EU has taken a rights based approach, and
looks like it will continue to do so, with the
General Data Protection Regulation (GDPR)
which will come into force in 2018 in EU/UK.
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Agenda
1. Introduction
2. A bit about ethics
3. Ethics and personal data
4. Ethics and automated decision making. A risk
management framework
Ethics, data and decision making.
What data to use when?
• Age
• Alcohol consumption
• Credit history
• Criminal records
• Dependents
• DNA
• Driving speed
• Education
• Gas consumption
• Gender
• Grocery purchases at supermarket
• Income
• Last book purchased
• Live with smoker (Y/N)
• Marital status
• Medical history
• Music currently listening too
• Race
• Religion
• Sexual orientation
• Smoker (Y/N)
• Type of car you drive
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Ethics, data and decision making.
1. Immutability of data?
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Immutable
(Individual can’t change at all)
Mutable
(Individual can change easily)
Age
Alcohol
consumption
IncomeCriminal record
Gas
consumption
Education
Gender
Grocery
purchases
Last book
purchased
Live with
smoker
Marital status
Medical history
Dependents
Race
ReligionMusic currently
Listening too
Sexual
orientation
Smoker
Type of car
Driving speed
DNA
Ethics, data and decision making.
2. Beneficiary?
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Individual / society Decision maker
Treatment
for illness
Selection for tax
inspection
Product
marketing
Benefit
paymentForeclosure
Match on
dating site
Credit
granting
Child protectionInsurance
pricing
For whose benefit is a decisions made ?(This is not the same thing as if the individual benefits from the decision)
Suspect selection
in criminal cases
Making
job offers
Redundancy
selection
Home
improvement grants
Parole
Survey selection
Ethics, data and decision making:
3. Impact
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What is the potential impact of decisions on an individual’s well being?
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Low Impact High Impact
Treatment
for illness
Selection for tax
inspection
Product
marketing
Benefit
payment
ForeclosureMatch on
dating site
Credit
granting
Child protection
Insurance
pricing
Suspect selection
in criminal cases
Making
job offers
Redundancy
selection
Home
improvement grantsParole
Survey selection
Ethics, data and decision making.
Risk in decision making
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1. Immutability
of data
3. Impact on
individual2. Beneficiary
of decision
Decision maker
Individual
Immutable
Mutable
Low
High
You need to decide what’s most important
within your ethical view (i.e. column order).
Impact of
decision on
individual
Beneficiary
of the
decision
Immutability
of data used
Ethical
challenge
/ risk
High Decision
maker
High Greatest
Least
Low
Individual High
Low
Low Decision
maker
High
Low
Individual High
Low
• More legislation
• Audit & regulatory oversight
• Public interest
• Greater manual involvement
• Simple and explicable models
• Judgemental overriding
• Expert “Buy-in”
• Understand model weaknesses
• Constant monitoring/feedback
• Less legislation
• Predictive ability trumps all else
• Complex “black box” models
• Automated model generation
• Rapid redevelopment of models
• Little oversight
E.G,
foreclosure,
redundancy,
parole
E.G. Marketing
applications,
Music playlists
Ethics, data and decision making:
Alternative perspective…
• It’s nothing to do with the data or the decision maker…
• It’s how you make the decision that’s important…
– Impartial, data driven process = GOOD (Ethical)
– Biased/judgemental decision = BAD (Unethical)
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Example: If women more likely to do X or Y than men (or
vice versa), then it’s fine for Gender to feature in a predictive
model, if that’s what the data is telling us.
However, this view is not popular, at least not in the UK or EU.
As evidenced by (fairly) recent decisions on the use of Gender
in insurance, despite gender being one of the most predictive
data items for all sorts of insurance claim behaviour.
In Summary
• Ethical data use and decision making brings its own rewards
• An ethical strategy is about more than just following the law.
– Ethical and legal is where you want to be…
• Some things to consider when formulating an ethical data
and decision making policy:
– The immutability of the data that you use.
– The impact that your decisions will have on individuals.
– The beneficiaries of the decisions you make.
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Bibliography and further reading
• Boatright, J. (2014) Ethics in Finance (3rd Edition). Wiley
• Finlay, P. (2000). An introduction to Business and Corporate Strategy. Pearson
Education.
• Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths,
Misconceptions and methods. Palgrave Macmillan.
https://www.amazon.co.uk/Predictive-Analytics-Data-Mining-
Misconceptions/dp/1137379278/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=14
92778632&sr=8-2
• O’Neil, C. (2016). Weapons of math Destruction. How Big Data Increases Inequality
and Threatens Democracy. Allen Lane.
• Orlitzky, M., Schmidt, F. L., Rynes, S. L. (2003). Corporate Social and Financial
Performance: A Meta-analysis. Organization Studies, volume 24, number 3, pages
403-441.
• Siegel, E. (2016). Predictive Analytics: the Power to Predict Who Will Click, Buy,
Lie, or Die. (2nd Edition). Wiley.
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