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Workshop
Fair AI in Finance
Senthil Kumar · Cynthia Rudin · John Paisley · Isabelle Moulinier · C. Bayan Bruss · Eren K. · Susan Tibbs · Oluwatobi Olabiyi · Simona Gandrabur · Svitlana Vyetrenko · Kevin Compher

Fri Dec 11 08:00 AM -- 05:27 PM (PST) @ None
in Workshops Fri »
Event URL: https://sites.google.com/view/faif2020 »

The financial services industry has unique needs for fairness when adopting artificial intelligence and machine learning (AI/ML). First and foremost, there are strong ethical reasons to ensure that models used for activities such as credit decisioning and lending are fair and unbiased, or that machine reliance does not cause humans to miss critical pieces of data. Then there are the regulatory requirements to actually prove that the models are unbiased and that they do not discriminate against certain groups.

Emerging techniques such as algorithmic credit scoring introduce new challenges. Traditionally financial institutions have relied on a consumer’s past credit performance and transaction data to make lending decisions. But, with the emergence of algorithmic credit scoring, lenders also use alternate data such as those gleaned from social media and this immediately raises questions around systemic biases inherent in models used to understand customer behavior.

We also need to play careful attention to ways in which AI can not only be de-biased, but also how it can play an active role in making financial services more accessible to those historically shut out due to prejudice and other social injustices.

The aim of this workshop is to bring together researchers from different disciplines to discuss fair AI in financial services. For the first time, four major banks have come together to organize this workshop along with researchers from two universities as well as SEC and FINRA (Financial Industry Regulatory Authority). Our confirmed invited speakers come with different backgrounds including AI, law and cultural anthropology, and we hope that this will offer an engaging forum with diversity of thought to discuss the fairness aspects of AI in financial services. We are also planning a panel discussion on systemic bias and its impact on financial outcomes of different customer segments, and how AI can help.

Fri 8:00 a.m. - 8:05 a.m.
Opening Remarks (Intro)
Senthil Kumar
Fri 8:05 a.m. - 8:35 a.m.
Invited Talk : Modeling the Dynamics of Poverty (Keynote)
Rediet Abebe
Fri 8:35 a.m. - 9:05 a.m.
Invited Talk 2: Unavoidable Tensions in Explaining Algorithmic Decisions (Keynote) Video
Solon Barocas
Fri 9:05 a.m. - 9:15 a.m.
Break
Fri 9:15 a.m. - 9:45 a.m.
Invited Talk 3: Stories of Invisibility: Re-thinking Human in the Loop Design (Keynote) Video
Madeleine Elish
Fri 9:45 a.m. - 10:15 a.m.
Invited Talk 4: Actionable Recourse in Machine Learning (Keynote) Video
Berk Ustun
Fri 10:15 a.m. - 10:30 a.m.
Break
Fri 10:30 a.m. - 11:00 a.m.
Invited Talk 5: Navigating Value Trade-offs in ML for Consumer Finance - A Legal and Regulatory Perspective (Keynote) Video
Nikita Aggarwal
Fri 11:00 a.m. - 11:30 a.m.
Invited Talk 6: Reconciling Legal and Technical Approaches to Algorithmic Bias (Keynote) Video
Alice Xiang
Fri 11:30 a.m. - 12:30 p.m.
Lunch Break (Break)
Fri 12:30 p.m. - 1:15 p.m.
Panel Discussion: Building a Fair Future in Finance (Panel)
Madeleine Elish, Alice Xiang, Ana Stoica, Cat Posey
Fri 1:15 p.m. - 1:20 p.m.
Break
Fri 1:20 p.m. - 1:50 p.m.
Invited Talk 7:Fair Portfolio Design (Keynote)
Michael Kearns
Fri 1:50 p.m. - 2:20 p.m.
Invited Talk 8: Fair AI in the securities industry, a review of methods and metrics (Keynote)
Jonathan Bryant
Fri 2:20 p.m. - 2:50 p.m.
Invited Talk 9: Building Compliant Models: Fair Feature Selection with Multiobjective Monte Carlo Tree Search (Keynote) Video
Jiahao Chen
Fri 2:50 p.m. - 3:10 p.m.
Break
Fri 3:10 p.m. - 3:25 p.m.
Spotlight Talk 1: Quantifying risk-fairness trade-off in regression (Talk) Video
Nicolas Schreuder, Evgenii Chzhen
Fri 3:25 p.m. - 3:40 p.m.
Spotlight Talk 2: Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination (Talk) Video
Mark Weber
Fri 3:40 p.m. - 3:55 p.m.
Spotlight Talk 3: An Experiment on Leveraging SHAP Values to Investigate Racial Bias (Talk) Video
Ramon Vilarino, Renato Vicente
Fri 3:55 p.m. - 4:10 p.m.
Spotlight Talk 4: Fairness, Welfare, and Equity in Personalized Pricing (Talk) Video
Nathan Kallus, Angela Zhou
Fri 4:10 p.m. - 4:25 p.m.
Spotlight Talk 5: Robust Welfare Guarantees for Decentralized Credit Organizations (Talk) Video
Rediet Abebe, Christian Ikeokwu, Samuel Taggart
Fri 4:25 p.m. - 4:40 p.m.
Spotlight Talk 6: Partially Aware: Some Challenges Around Uncertainty and Ambiguity in Fairness (Talk) Video
Francois Buet-Golfouse
Fri 4:40 p.m. - 4:55 p.m.
Spotlight Talk 7: Hidden Technical Debts for Fair Machine Learning in Financial Services (Talk) Video
Chong Huang, Arash Nourian, Home Griest
Fri 4:55 p.m. - 4:58 p.m.
Lightning Talk 1: Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning (Talk) Video
Alexander Wong, Andrew Hryniowski, Xiao Yu Wang
Fri 4:58 p.m. - 5:01 p.m.
Lightning Talk 2: Pareto Robustness for Fairness Beyond Demographics (Talk) Video
Natalia Martinez, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
Fri 5:01 p.m. - 5:04 p.m.
Lightning Talk 3: Developing a Philosophical Framework for Fair Machine Learning: The Case of Algorithmic Collusion and Market Fairness (Talk) Video
James Michelson
Fri 5:04 p.m. - 5:07 p.m.
Lightning Talk 4: Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations (Talk) Video
C. Bayan Bruss, Rachana Balasubramanian, Brian Barr, Samuel Sharpe, Jason Wittenbach

Author Information

Senthil Kumar (Capital One)
Cynthia Rudin (Duke)
John Paisley (Columbia University)
Isabelle Moulinier (Capital One)
C. Bayan Bruss (Capital One)
Eren K. (BoA/Columbia University)
Susan Tibbs (FINRA)
Oluwatobi Olabiyi (Capital One)
Simona Gandrabur (Banque Nationale du Canada)
Svitlana Vyetrenko (J. P. Morgan, Artificial Intelligence Research)
Kevin Compher (U.S. Securities and Exchange Commission)

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