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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





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)
Invited Talk : Modeling the Dynamics of Poverty (Keynote)
Invited Talk 2: Unavoidable Tensions in Explaining Algorithmic Decisions (Keynote)
Break
Invited Talk 3: Stories of Invisibility: Re-thinking Human in the Loop Design (Keynote)
Invited Talk 4: Actionable Recourse in Machine Learning (Keynote)
Break
Invited Talk 5: Navigating Value Trade-offs in ML for Consumer Finance - A Legal and Regulatory Perspective (Keynote)
Invited Talk 6: Reconciling Legal and Technical Approaches to Algorithmic Bias (Keynote)
Lunch Break (Break)
Panel Discussion: Building a Fair Future in Finance (Panel)
Break
Invited Talk 7:Fair Portfolio Design (Keynote)
Invited Talk 8: Fair AI in the securities industry, a review of methods and metrics (Keynote)
Invited Talk 9: Building Compliant Models: Fair Feature Selection with Multiobjective Monte Carlo Tree Search (Keynote)
Break
Spotlight Talk 1: Quantifying risk-fairness trade-off in regression (Talk)
Spotlight Talk 2: Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination (Talk)
Spotlight Talk 3: An Experiment on Leveraging SHAP Values to Investigate Racial Bias (Talk)
Spotlight Talk 4: Fairness, Welfare, and Equity in Personalized Pricing (Talk)
Spotlight Talk 5: Robust Welfare Guarantees for Decentralized Credit Organizations (Talk)
Spotlight Talk 6: Partially Aware: Some Challenges Around Uncertainty and Ambiguity in Fairness (Talk)
Spotlight Talk 7: Hidden Technical Debts for Fair Machine Learning in Financial Services (Talk)
Lightning Talk 1: Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning (Talk)
Lightning Talk 2: Pareto Robustness for Fairness Beyond Demographics (Talk)
Lightning Talk 3: Developing a Philosophical Framework for Fair Machine Learning: The Case of Algorithmic Collusion and Market Fairness (Talk)
Lightning Talk 4: Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations (Talk)