1. Big Data and Employment Discrimination Aaron Konopasky, J.D., Ph.D.

2. A Puzzle As an employment lawyer I am interested in some of the ethical issues raised by Big Data Although the background reading provided on the conference website is exceptional, I was surprised to find that most of it seemed relatively unconcerning (in my role as an employment discrimination lawyer) 2

3. Explanation Most people here are interested in potentially negative effects of Big Data research on study participants, and/or Believe that the negative effects of Big Data are best avoided through greater privacy protections, but... 3

4. I Don’t Care About Study Participants or Privacy...in my role as an employment discrimination lawyer ◦ (This is not quite true. If a research participant is also a job candidate or employee, then perhaps I might be interested in the possibility that his or her personally identifiable data is obtained by the employer, contrary to the wishes of the subject. But I don’t have anything to add to that conversation, so I will ignore it.) 4

5. What I am Concerned About That Big Data researchers will create products (e.g., algorithms that rate or categorize job applicants) that unfairly exclude people with certain protected characteristics from employment Query: Should the possibility that research findings will be misused affect how or whether the research should be done? 5

6. What I Will Discuss Federal employment antidiscrimination laws ◦ Goals ◦ How they are designed to accomplish those goals How Big Data could threaten those goals Whether/the extent to which current law protects us 6

7. FEDERAL EEO LAWS 7

8. EEO Laws There are certain characteristics that generally shouldn’t get in the way of a job, but often do – “protected characteristics” Federal EEO laws are meant to take these characteristics out of the equation 8

9. Protected Characteristics Race, color, national origin, sex (including pregnancy), religion (including atheism) ◦ Title VII of the Civil Rights Act of 1964 (“Title VII”) ◦ Equal Pay Act (“EPA”) (sex only) Medical condition/disability ◦ Americans with Disabilities Act (“ADA”) Being “too old” (minimum 40) ◦ Age Discrimination in Employment Act of 1967 (“ADEA”) Genetic information (including family medical history) ◦ Genetic Information Nondiscrimination Act of 2008 (“GINA”) 9

10. Protection #1: Disparate Treatment Adverse actions cannot be motivated by protected characteristics ◦ E.g., terminating someone because of a disability, or refusing to hire someone based on race ◦ Severe & pervasive harassment based on protected characteristics is also prohibited 10

11. Protection #2: Disparate Impact A policy or practice that disadvantages people with a certain protected characteristic, as a group, is prohibited unless it can be justified from a business standpoint 11

12. Protection #3: Reasonable Accommodation Employers may be required to make certain accommodations for people who need them because of a disability, or for religious reasons ◦ E.g., a permanent shift assignment for someone who needs to work around a treatment schedule, or an exception to the dress code for someone who wears a hijab for religious reasons 12

13. Protection #4: Privacy  Access to genetic and medical information is restricted  Information that the employer does have must be kept confidential 13

14. A Related Law: Fair Credit Reporting Act (FCRA) Employer needs written permission to purchase background reports (including credit and criminal history reports) ◦ Only if purchased from a background reporting company Must promise to not use the information in violation of EEO laws 14

15. BIG DATA: EMPLOYEE ASSESSMENT 15

16. Disparate Treatment? Under current law, some problematic uses of Big Data would constitute disparate treatment, e.g.-- ◦ Employer uses algorithm that predicts health status in order to screen out people with disabilities ◦ Employer uses an algorithm that is known to use protected characteristics as predictors But what if -- ◦ Neither the employer nor the programmer knows that the product takes a protected characteristic into account, or ◦ The product disadvantages a protected group by using proxies for protected status? 16

17. Disparate Impact? If an Big Data product disproportionately disadvantages a protected group, it is illegal unless it can be justified from a business point of view 17

18. Justification There may be reason to questions whether some troubling uses of Big Data could be justified ◦ Products that measure the wrong thing  Repurposed research  Stereotypes and assumptions ◦ Products that measure similarity to the status quo  Actual success may be partly a result of discrimination 18

19. A Story About Credit 19

20. Strategies Suppress or forego research Attach strong privacy rights to all information Keep research results a private Create even more rigorous standards for justifying employment practices... ? 20

21. CONTACT 21

22. Aaron Konopasky, J.D., Ph.D. Senior Attorney-Advisor ADA/GINA Policy Division Office of Legal Counsel Equal Employment Opportunity Commission 131 M Street NE Washington, DC 20507 Phone: (202) 663-4127 email: aaron.konopasky@eeoc.gov 22