grappling with ethics in the age of big data presentation at the chief data scientist, usa 2016

The Moral Dimension – Grappling with Ethics in the Age of Big Data

Chief Data Scientist, USA

Introduction

Bennett B. BordenChief Data ScientistChair, IGED GroupDrinker Biddle & ReathBennett.Borden@dbr.com

3

Information is Overwhelmingly Electronic

4

And is coming from more sources than ever

5

6

Insight into Human Conduct to Unparalleled Degree

7

Unparalleled Market Efficiency The Right (Public or Private) Product or Service

- Right time

- Right place

- Right consumer

- Right cost

- Right price

8

Unparalleled Potential for Disruption and Misuse

9

10

11

12

Differential Pricing – Disparate Impact

Algorithm used to set prices for online SAT tutoring by The Princeton Review showed customers in high density Asian neighborhoods were charged more.

13

Cumulative Economic Impact

14

15

16

What do we mean by ethics?

Common ethical frameworks?

Legal ethics?

Corporate Ethics – Social Responsibility?

Right v. Wrong?

Creepiness Factor?

17

Common Ethical FrameworksUtilitarianism: The greatest good for the greatest number.

Liberalism: Individual freedom and autonomy

Communitarianism: Promotes the “common good” or shared values.

18

Current Laws and Regulations Don’t Fit Current Applications of Analytics

In 2012 Facebook conducted an “emotional contagion” study, manipulating the display of happy and sad content in the news feeds of 150,000 users to see if they would share happy or sad content

19

Notice and Consent

20

Data Ownership

21

The Power of Algorithms to Ferret Out Latent Traits in Datasets: Facebook “Likes” Research Finding “latent traits” in the Likes of 58,000 volunteers, an algorithm could model the

following otherwise undisclosed traits with 80 to 90% accuracy- Sexual orientation- Ethnicity- Religious and political views- Personality traits- Intelligence- Happiness- Use of addictive substances- Parental separation - Age- Gender- Etc.

Source: Z. Tufekci, “Algorithmiic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency,” 13 Colo. Tech. L. J. 203, 210 n.20 (2015)

22

Predictive Policing

23

How far can this be taken?

24

25

26

27

Predicting Human Conduct – Good or Bad Idea?

28

Avoiding Bias

29

Avoiding False Correlations

30

The Digital Divide – Where do we get our information?

“This ‘digital divide’ is concentrated among older, less educated, less affluent populations, as well as in rural parts of the country that has fewer choices and slower connections.”- - Council of Economic Advisors Issue Brief – July 2015

31

2016 Presidential Election Polling Data

How all of them were wrong? -Didn’t have data on the right people

The data they did have was wrong (reporting bias)

32

Acting in a Legal Greenfield

• Identify and reasonably mitigate risks

33

Corporate Ethical Review Boards Identify and quantify risks in analytics projects

Identify mitigation strategies

Include a diversity of opinion

Potential for greater transparency in decisionmaking

Builds confidence in corporate strategies and tactics

Modeled on IRBs, an ERB to be housed as component of Chief IG Officer or Chief Data Officer or CIO

34

Hello Barbie vs. Amazon EchoUnderstand the risks you are creating and act reasonably to mitigate them

35

36