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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

2020-12-11T08:00:00-08:00 - 2020-12-11T17:27:00-08:00
Abstract: 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.


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2020-12-11T08:00:00-08:00 - 2020-12-11T08:05:00-08:00
Opening Remarks
Senthil Kumar
2020-12-11T08:05:00-08:00 - 2020-12-11T08:35:00-08:00
Invited Talk : Modeling the Dynamics of Poverty
Rediet Abebe
2020-12-11T08:35:00-08:00 - 2020-12-11T09:05:00-08:00
Invited Talk 2: Unavoidable Tensions in Explaining Algorithmic Decisions
Solon Barocas
2020-12-11T09:05:00-08:00 - 2020-12-11T09:15:00-08:00
2020-12-11T09:15:00-08:00 - 2020-12-11T09:45:00-08:00
Invited Talk 3: Stories of Invisibility: Re-thinking Human in the Loop Design
Madeleine Elish
2020-12-11T09:45:00-08:00 - 2020-12-11T10:15:00-08:00
Invited Talk 4: Actionable Recourse in Machine Learning
Berk Ustun
2020-12-11T10:15:00-08:00 - 2020-12-11T10:30:00-08:00
2020-12-11T10:30:00-08:00 - 2020-12-11T11:00:00-08:00
Invited Talk 5: Navigating Value Trade-offs in ML for Consumer Finance - A Legal and Regulatory Perspective
Nikita Aggarwal
2020-12-11T11:00:00-08:00 - 2020-12-11T11:30:00-08:00
Invited Talk 6: Reconciling Legal and Technical Approaches to Algorithmic Bias
Alice Xiang
2020-12-11T11:30:00-08:00 - 2020-12-11T12:30:00-08:00
Lunch Break
2020-12-11T12:30:00-08:00 - 2020-12-11T13:15:00-08:00
Panel Discussion: Building a Fair Future in Finance
Madeleine Cla Elish, Alice Xiang, Ana Stoica, Cat Posey
2020-12-11T13:15:00-08:00 - 2020-12-11T13:20:00-08:00
2020-12-11T13:20:00-08:00 - 2020-12-11T13:50:00-08:00
Invited Talk 7:Fair Portfolio Design
Michael Kearns
2020-12-11T13:50:00-08:00 - 2020-12-11T14:20:00-08:00
Invited Talk 8: Fair AI in the securities industry, a review of methods and metrics
Jonathan Bryant
2020-12-11T14:20:00-08:00 - 2020-12-11T14:50:00-08:00
Invited Talk 9: Building Compliant Models: Fair Feature Selection with Multiobjective Monte Carlo Tree Search
Jiahao Chen
2020-12-11T14:50:00-08:00 - 2020-12-11T15:10:00-08:00
2020-12-11T15:10:00-08:00 - 2020-12-11T15:25:00-08:00
Spotlight Talk 1: Quantifying risk-fairness trade-off in regression
Nicolas Schreuder, Evgenii Chzhen
2020-12-11T15:25:00-08:00 - 2020-12-11T15:40:00-08:00
Spotlight Talk 2: Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination
Mark Weber
2020-12-11T15:40:00-08:00 - 2020-12-11T15:55:00-08:00
Spotlight Talk 3: An Experiment on Leveraging SHAP Values to Investigate Racial Bias
Ramon Vilarino, Renato Vicente
2020-12-11T15:55:00-08:00 - 2020-12-11T16:10:00-08:00
Spotlight Talk 4: Fairness, Welfare, and Equity in Personalized Pricing
Nathan Kallus, Angela Zhou
2020-12-11T16:10:00-08:00 - 2020-12-11T16:25:00-08:00
Spotlight Talk 5: Robust Welfare Guarantees for Decentralized Credit Organizations
Rediet Abebe, Christian Ikeokwu, Samuel Taggart
2020-12-11T16:25:00-08:00 - 2020-12-11T16:40:00-08:00
Spotlight Talk 6: Partially Aware: Some Challenges Around Uncertainty and Ambiguity in Fairness
Francois Buet-Golfouse
2020-12-11T16:40:00-08:00 - 2020-12-11T16:55:00-08:00
Spotlight Talk 7: Hidden Technical Debts for Fair Machine Learning in Financial Services
Chong Huang, Arash Nourian, Home Griest
2020-12-11T16:55:00-08:00 - 2020-12-11T16:58:00-08:00
Lightning Talk 1: Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning
Alexander Wong, Andrew Hryniowski, Xiao Yu Wang
2020-12-11T16:58:00-08:00 - 2020-12-11T17:01:00-08:00
Lightning Talk 2: Pareto Robustness for Fairness Beyond Demographics
Natalia Martinez, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
2020-12-11T17:01:00-08:00 - 2020-12-11T17:04:00-08:00
Lightning Talk 3: Developing a Philosophical Framework for Fair Machine Learning: The Case of Algorithmic Collusion and Market Fairness
James Michelson
2020-12-11T17:04:00-08:00 - 2020-12-11T17:07:00-08:00
Lightning Talk 4: Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
C. Bayan Bruss, Rachana Balasubramanian, Brian Barr, Samuel Sharpe, Jason Wittenbach