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Workshop
Fri Dec 11 08:00 AM -- 05:27 PM (PST)
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





Workshop Home Page

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.

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