ethics for artificial intelligence, machine learning and automated decision making

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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3

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



4. Ethics, machine learning and automated decision


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



4. Ethics for machine learning and automated decision


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.


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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).


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?

– …..



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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



4. Ethics and automated decision making. A risk

management framework


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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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


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


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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https://www.amazon.co.uk/Predictive-Analytics-Data-Mining-Misconceptions/dp/1137379278/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=1492778632&sr=8-2

ethics for artificial intelligence, machine learning and automated decision making

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