Triage Score: A Counterfactual Risk Assessment Instrument (SIMONS-ECON)
39:56LLM-Guided Reinforcement Learning for Mastery Learning: Large-Scale Field Evidence (SIMONS-ECON)
35:06Non-parametric Causal Inference in Dynamic Thresholding Designs (SIMONS-ECON)
30:21Agreement and Alignment for Human-AI Collaborative Decision Making (SIMONS-ECON)
45:21Human Values in Clinical Algorithms, Old and New (SIMONS-ECON)
38:46When Prediction Becomes Policy: What Do Risk Models Actually Learn? (SIMONS-ECON)
33:31Evaluating Probabilistic Decision Making by LLMs: A Decision-Theoretic Framework and Application (SIMONS-ECON)
32:16Algorithmic UDAP (SIMONS-ECON)
32:13Operational Dosage: The Impact of Capacity Constraints on RCT Evaluation (SIMONS-ECON)
35:01The Role of Standardized Tests in Admissions: Predictions vs. Decisions (SIMONS-ECON)
33:37Individual Gaps in Prediction Generalization (SIMONS-ECON)
30:41Beyond Prediction Performance: How Modeling Decisions Shape Fairness Outcomes in Statistical Profiling (SIMONS-ECON)
27:56The Feedback Loop of Statistical Discrimination (SIMONS-ECON)
32:03Decision Theoretic Foundations for Human-AI Collaboration (SIMONS-ECON)
31:01Evaluation Design (SIMONS-ECON)
29:16Recommendation in Equilibrium for the NYC High School Match (SIMONS-ECON)
32:51Automating the US Welfare State: Historical Experiments in Technological Federalism (SIMONS-ECON)
35:41On Machine Learning for Prediction and Prioritization in the Allocation of Scarce Societal Resources (SIMONS-ECON)
35:26Algorithmic UDAP (SIMONS-ECON)
This talk compares disparate impact and UDAAP legal frameworks for evaluating algorithmic discrimination, formalizing their application in a simulated lending setting to assess harms.
Talia Gillis (Columbia University)
Bridging Prediction and Intervention Problems in Social Systems
The talk will compare two legal frameworks---disparate impact (DI) and unfair, deceptive, or abusive acts or practices (UDAAP)---as tools for evaluating algorithmic discrimination, focusing on the example of fair lending. While DI has traditionally served as the foundation of fair lending law, recent regulatory efforts have invoked UDAAP, a doctrine rooted in consumer protection, as an alternative means to address algorithmic discrimination harms. We formalize and operationalize both doctrines in a simulated lending setting to assess how they evaluate algorithmic disparities. While some regulatory interpretations treat UDAAP as operating similarly to DI, we argue it is an independent and analytically distinct framework. In particular, UDAAP’s "unfairness" prong introduces elements such as avoidability of harm and proportionality balancing, while its "deceptive" and "abusive" standards may capture forms of algorithmic harm that elude DI analysis. At the same time, translating UDAAP into algorithmic settings exposes unresolved ambiguities, underscoring the need for further regulatory guidance if it is to serve as a workable standard.
Evaluating Probabilistic Decision Making by LLMs: A Decision-Theoretic Framework and Application (SIMONS-ECON)
Next · Lecture 9Operational Dosage: The Impact of Capacity Constraints on RCT Evaluation (SIMONS-ECON)
Seminar notes & discussion
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