Ethical Perspectives on Personal Data and Automated Decision Making
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1.
A bit about ethics
2.
Ethics, data and decision making
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
2.
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Ethics is….
Subjective, personal,
unique…
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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
theory
Universality: 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
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All ethical frameworks have their weaknesses…
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…
•
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
6
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…
•
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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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 defined 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.
8
9
A
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1.
A bit about ethics
2.
Ethics, data and decision making
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My 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. Its 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.
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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?
–
…
.
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?
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, via revised
Data Protection Legislation approved in March.
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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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Immutable
(Individual can’t change at all)
Mutable
(Individual can change easily)
Age
Alcohol
consumption
Income
Criminal record
Gas
consumption
Education
Gender
Grocery
purchases
Last book
purchased
Live with
smoker
Marital status
Medical history
Dependents
Race
Religion
Music currently
Listening too
Sexual
orientation
Smoker
Type of car
Driving speed
DNA
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Individual / society
Decision maker
Treatment
for illness
Selection for tax
inspection
Product
marketing
Benefit
payment
Foreclosure
Match on
dating site
Credit
granting
Child protection
Insurance
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
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What is the potential impact of decisions on an individual’s well being?
17
Low Impact
High Impact
Treatment
for illness
Selection for tax
inspection
Product
marketing
Benefit
payment
Foreclosure
Match on
dating site
Credit
granting
Child protection
Insurance
pricing
Suspect selection
in criminal cases
Making
job offers
Redundancy
selection
Home
improvement grants
Parole
Survey selection
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1. Immutability
of data
3. Impact on
individual
2. Beneficiary
of decision
Decision maker
Individual
Immutable
Mutable
Low
High
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•
More legislation
•
Audit & regulatory oversight
•
Public interest
•
Greater manual involvement
•
Simple and explicable models
•
Judgemental overriding
•
Expert “Buy-in”
•
Understand model weaknesses
•
Constant monitoring
•
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
type
applications
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…
•
Its nothing to do with the data or the decision maker…
•
Its how you make the decision that’s important…
–
Impartial, data driven process = GOOD (Ethical)
–
Biased/judgemental decision = BAD (Unethical)
20
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.
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.
I
n
S
u
m
m
a
r
y
•
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.
21
B
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o
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a
p
h
y
a
n
d
f
u
r
t
h
e
r
r
e
a
d
i
n
g
•
Boatright, J. (2014) Ethics in Finance (3
rd
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.
•
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.
22
Hi
My name is Steven Finlay. I’m head of Analytics at a company called HML. You have probably never heard of HML – but it’s Europes largest mortgage outsourcing provider, based in Skipton, Yorkshire. HML is part of the Computershare Group, which employs over 15,000 people at several sites around the world. We specialize in providing the infrastructure to support residential mortgage portfolios from origination through to debt recovery. We currently manage more than £35 billion assets for our customers. Prior to this role I worked for a number of organizations including UK Government and Experian.
By trade I’m a geek/data scientist. For the last 20 years I’ve spent most of my time building or designing decision making systems based around predictive analytics, but around 10-11 years ago I began to get interested in some of the ethical aspects what I was doing.
Over time this has developed in to an approach to how I think about data and the how I advise others on the use that data, given its nature.
A lot of what you are going to hear today is aligned some of the material in my latest book: Predictive analytics, Data Mining and Big data. Myths, misconceptions and Methods, Which will be published by Palgrave Macmillan in the next few weeks.
Delve into the realms of ethics, data, and decision-making with insights from Dr. Steven Finlay. This presentation covers the essence of ethics, common ethical frameworks, ethical practice challenges, and the real-world relevance of ethical considerations in personal and organizational contexts.
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Presentation Transcript
Ethical Perspectives on Personal Data and Automated Decision Making Dr Steven Finlay 15/5/2014
Agenda 1. A bit about ethics 2. Ethics, data and decision making 2
A bit about ethics. Definitions 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 ethikos from 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 2. Its about right and wrong. Ethics is . Subjective, personal, unique 3
Common ethical frameworks Ethics Non-Consequentialist It s more about the journey than where you end up Consequentialist The means justify the ends Religious Teaching (e.g. the ten commandments) Kant s ethical theory Universality: Ethical is something all rational people would agree with Virtues Virtuous modes of behaviour (Aristotle) Utilitarianism Greatest good for the greatest number (Jeremy Bentham and John Stuart Mills) Golden rule Do unto others as you would have done unto you (Do no evil) (Human) rights Right to life, liberty, property, privacy, etc. (Locke and Rawls) 4
Ethics in practice All ethical frameworks have their weaknesses 5
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? Legal Lots of laws allow unethical actions to occur: Ethical 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 6
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) 7
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 defined 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. 8
Agenda 1. A bit about ethics 2. Ethics, data and decision making 9
Ethics, data and decision making. Whose data is it anyway? Kantian/Rights based perspective Utilitarian orientated perspective My data is a resource to be harvested and put to use. My data is a part of who and what I am. Its mine! Constraints (laws) to prevent specific abuses and misuse of my data. 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. 10
Ethics, data and decision making. Whose data is it anyway? Approach Pros Cons 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. Each individual has control over their data and the uses to which it is put. Less social exclusion.. Right change/withdraw permission to use data, including Right to be forgotten. 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, its out there for good. Utilitarian orientated perspective 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). Kantian/Rights based perspective 11
Ethics, data and decision making. Is more data and better prediction always better? 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? .. 12
Ethics, data and decision making. Whose data is it anyway? What s the direction of Travel? EU has taken a rights based approach, and looks like it will continue to do so, via revised Data Protection Legislation approved in March. USA, has to date, followed a more utility based model. Use data for whatever you want, but we will legislate where needed. 13
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 14
Ethics, data and decision making. 1. Immutability of data? Mutable Immutable (Individual can t change at all) (Individual can change easily) DNA Income Criminal record Alcohol consumption Last book purchased Age Education Gender Type of car Dependents Race Smoker Music currently Listening too Religion Live with smoker Gas Sexual orientation Grocery purchases consumption Medical history Driving speed Marital status 15
Ethics, data and decision making. 2. Beneficiary? For whose benefit is a decisions made ? (This is not the same thing as if the individual benefits from the decision) Decision maker Individual / society Match on dating site Parole Benefit payment Foreclosure Survey selection Product marketing Redundancy selection Treatment for illness Credit granting Selection for tax inspection Home Making job offers improvement grants Suspect selection in criminal cases Insurance pricing Child protection 16
Ethics, data and decision making: 3. Impact What is the potential impact of decisions on an individual s well being? Low Impact High Impact Match on dating site Foreclosure Home Benefit payment improvement grants Parole Credit granting Making job offers Survey selection Child protection Insurance pricing Redundancy selection Product marketing Treatment for illness Selection for tax inspection Suspect selection in criminal cases 17 17
Ethics, data and decision making. Risk in decision making Decision maker High 3. Impact on individual 2. Beneficiary of decision Low Individual Mutable 1. Immutability of data Immutable 18
You need to decide whats most important within your ethical view (i.e. column order). E.G, foreclosure, redundancy, parole More legislation Audit & regulatory oversight Public interest Greater manual involvement Simple and explicable models Judgemental overriding Expert Buy-in Understand model weaknesses Constant monitoring Impact of decision on individual Beneficiary of the decision Immutability of data used Ethical challenge / risk High Decision maker High Low High Low High Low High Low Greatest Individual Low Decision maker Less legislation Predictive ability trumps all else Complex black box models Automated model generation Rapid redevelopment of models Little oversight Individual Least E.G. Marketing type applications
Ethics, data and decision making: Alternative perspective Its nothing to do with the data or the decision maker Its how you make the decision that s important Impartial, data driven process = GOOD (Ethical) Biased/judgemental decision = BAD (Unethical) Example: If women more likely to do X or Y than men (or vice versa), then its 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. 20
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. 21
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. 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. 22
