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)
35:26When Prediction Becomes Policy: What Do Risk Models Actually Learn? (SIMONS-ECON)
This lecture investigates how predictive risk models in child welfare, when acting as policy, learn "risk" from administrative records shaped by bureaucracy, resources, and discretionary judgment.
Shion Guha (University of Toronto)
Bridging Prediction and Intervention Problems in Social Systems
Predictive risk models in child welfare are often defended as forecasting devices. In the field, they behave like policy. They reorganize triage, supervision, service allocation, and documentation, and they thereby reshape the administrative record that later stands in for “ground truth.” This talk starts from that pragmatic observation and asks a simple but consequential question: what do risk models actually learn when “risk” is produced through street level bureaucracy, resource constraints, and discretionary judgment rather than revealed as a stable label?
Human Values in Clinical Algorithms, Old and New (SIMONS-ECON)
Next · Lecture 7Evaluating Probabilistic Decision Making by LLMs: A Decision-Theoretic Framework and Application (SIMONS-ECON)