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Economics and Fairness


   Workshop Report   

May 22-23, 2019

Cambridge MA
1585 Massachusetts Avenue, Cambridge, MA, USA



Event Contact

Ann Drobnis
adrobnis@cra.org


Event Type

2019 Events, 2019 Visioning Activities, Workshop


Event Category

CCC


Tags

AI, Alexa, algorithmic decision making, data driven, data science, economic inequality, economics, equity, fairness and accountability, IoT, machine learning, privacy

Overview

The Computing Community Consortium’s (CCC) Fairness and Accountability Task Force held a visioning workshop on Economics and Fairness, May 22-23, 2019 in Cambridge, Massachusetts. This workshop brought together computer science researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders to discuss methods to ensure economic fairness in a data-driven world.

The workshop addressed five main areas:

Data Concentration

  • Machine learning (ML), regression, etc. are essentially commodities; as a result, the data is the primary source of differentiation. Should we be concerned with the concentration of data in a small set of hands? If so, how do we guard against this?

Algorithmic Decision Making

  • With many consequential decisions being delegated to algorithms, how do we ensure that such decisions comply with both the spirit and letter of the law, as well as with ethical/societal fairness considerations?

Algorithmic Recommendation

  • In many settings algorithms make recommendations rather than decisions. How do humans interpret and make use of these recommendations? In particular, can the combination be worse than either one on its own? Particular focus will be paid to the following three domains: 1) Interaction between crime prediction and judicial decision making, 2) news feeds and misinformation, 3) marketing and consumer decisions (e.g, search by voice).

Implications for Platforms

  • New technologies have disrupted traditional industries (taxi, hotel) by reducing barriers to entry and lowering the costs of search and coordination. The traditional structure made regulating them easier and ensuring that they complied with extant norms. The new forms disperse decision making (to various degrees). Who is responsible for ensuring compliance? If it is the platform, how can algorithms be used to ensure compliance with the law and/or norms. For example, how does AirBnB ensure compliance with fair housing? How does Uber ensure equality of earning opportunities for drivers?

Equality of Opportunity

  • Much of the fairness discussion in the context of ML has been on fairness of outcomes with less attention paid to measuring equality of opportunity and/or ensuring access to opportunity. However, all three areas warrant consideration when discussing fairness. What constitutes fair equality of opportunity, and what role do algorithms play in ensuring economic equality through data driven decisions?

The goal of the workshop was to produce a report or white paper that articulates best practices and research challenges with regards to fairness and economics, as well as provides a sense of direction for the field. A workshop report is now available.

Agenda

May 22, 2019 (Wednesday)

07:30 AM BREAKFAST | Concord Room - Sheraton Commander Hotel
08:30 AM Welcome and Introductions | WCC 2004 - Harvard Law School
08:45 AM Economics View on Fairness | WCC 2004 - Harvard Law School

Mallesh Pai, Rice- “Can Free Markets lead to Fair Markets?”

Recommended Reading:

  • The Economics of Discrimination by Gary Becker
  • Theories of Statistical Discrimination and Affirmative Action: A Survey by Hanming Fang and Andrea Moro
09:30 AM Computer Science View on Fairness | WCC 2004 - Harvard Law School

Sharad Goel, Stanford– “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning”

Recommended Reading:

  • The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning* by Sam Corbett-Davis and Sharad Goel
10:15 AM BREAK | Outside WCC 2004 - Harvard Law School
10:30 AM Algorithm Decision Making | WCC 2004 - Harvard Law School

Prasanna Tambe, Wharton – “Artificial Intelligence in Human Resources Management: Challenges and a Path Forward”

Recommended Reading:

  • Artificial Intelligence in Human Resources Management: Challenges and a Path Forward by Prasanna Tambe

Lindsey Zuloaga, HireVue – “Algorithms for Hire”

Bo Cowgill, Columbia – “Economics, Fairness and Algorithmic Bias”

Recommended Reading:

  • Bias and Productivity in Humans and Machines by Bo Cowgill
  • Economics, Fairness and Algorithmic Bias by Bo Cowgill and Catherine E. Tucker

Discussant: Matt Weinberg, Princeton  

12:30 PM LUNCH | WCC 2036 Milstein East A - Harvard Law School
01:45 PM Lightning Round / Rump Session | WCC 2004 - Harvard Law School
02:30 PM BREAK | Outside WCC 2004 - Harvard Law School
03:00 PM Platforms | WCC 2004 - Harvard Law School

Daniel Knoefle, Uber- “Pricing Efficiently in Designed Markets: Evidence from Ride-Sharing”

Recommended Reading:

  • Pricing Efficiently in Designed Markets: Evidence from Ride-Sharing by Jonathan Hall, John Horton, Daniel Knoefle

Karen Levy, Cornell University- “Trade-offs in Designing Against Discrimination”

Recommended Reading:

  • Designing Against Discrimination in Online Markets by Karen Levy and Solon Barocas

Mike Luca, HBS- “Discrimination in Online Marketplaces”

Recommended Reading:

  • Fixing Discrimination in Online Marketplaces by Ray Fisman and Michael Luca
  • Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment by Benjamin Edelman, Michael Luca, and Dan Svirsky

Discussant: Ayelet Israeli, HBS  (slides) 

05:00 PM BREAK | Outside WCC 2004 - Harvard Law School
05:15 PM Discussion/Synthesis | WCC 2004 - Harvard Law School
06:30 PM DINNER | Concord Room - Sheraton Commander Hotel

May 23, 2019 (Thursday)

07:30 AM BREAKFAST | Concord Room - Sheraton Commander Hotel
08:50 AM Algorithm Recommendations | WCC 2004 - Harvard Law School

Megan Stevenson, George Mason- “Algorithmic Risk Assessment in the Hands of Humans”

Recommended Reading:

  • Algorithmic Risk Assessment in the Hands of Humans by Megan Stevenson and Jennifer Doleac

Michael D. Ekstrand, Boise State- “Recommendations, Decisions, Feedback Loops, and Maybe Saving the Planet”

Recommended Reading:

  • Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System by Alain Starke, Martijn Willemsen, and Chris Snijders
  • Behaviorism in Not Enough by Michael D. Ekstrand and Martijn C. Willemsen

Katrina Ligett, The Hebrew University of Jerusalem- “Humans and algorithms, deciding together”

Discussant: Aaron Roth, University of Pennsylvania (slides)

10:50 AM BREAK | Outside WCC 2004 - Harvard Law School
11:00 AM Lightning Round/Rump Session | WCC 2004 - Harvard Law School
11:45 AM LUNCH | WCC 2036 Milstein East A - Harvard Law School
01:00 PM Equality of Opportunity | WCC 2004 - Harvard Law School

John E. Roemer, Yale University – “Equalizing Opportunities through policy: A primer.”

Recommended Reading:

  • Equality of Opportunity: Theory and Measurement by John E. Roemer and Alain Trannoy
  • Equity in Health Care Delivery: Some Thoughts and an Example by John Roemer

Rediet Abebe, Cornell University – “Mechanism Design for Social Good”

Berk Ustun, Harvard University – “Actionable Recourse in Linear Classification“

Recommended Reading:

  • Actionable Recourse in Linear Classification by Berk Ustun, Alexander Spangher, and Yang Liu

Discussant: Rakesh Vohra, University of Pennsylvania (slides)

03:00 PM Working Session | WCC 2004 - Harvard Law School
05:00 PM Adjourn
Organizers

Organizing Committee:

David Parkes, Harvard University
Parkes
Rakesh Vohra, University of Pennsylvania
Vohra
Logistics

The CCC will cover travel expenses for all participants who desire it. Participants are asked to make their own travel arrangements to get to the workshop, including purchasing airline tickets. Following the symposium, CCC will circulate a reimbursement form that participants will need to complete and submit, along with copies of receipts for amounts exceeding $75.

In general, standard Federal travel policies apply: CCC will reimburse for non-refundable economy airfare on U.S. Flag carriers; and no alcohol will be covered.

For more information, please see the Guidelines for Participant Reimbursements from CCC.

Additional questions about the reimbursement policy should be directed to Ann Drobnis, CCC Director (adrobnis [at] cra.org).

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