4 papers
Learning from an Unknown DGP: Experimental Evidence on Belief Updating with AI Recommendations
Matthew Kovach, Daniel Martin, Gerelt Tserenjigmid
We use a controlled experiment to study how beliefs are updated after receiving qualitative information (AI recommendations) from an unknown data-generating process (DGP). Across 6…
Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment
Gunnar P. Epping, Andrew Caplin, Erik Duhaime +3
Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormalities). When positives are rar…
Misaligned by Design: Incentive Failures in Machine Learning
David Autor, Andrew Caplin, Daniel Martin +1
The cost of error in many high-stakes settings is asymmetric: misdiagnosing pneumonia when absent is an inconvenience, but failing to detect it when present can be life-threatening…
Testing Capacity-Constrained Learning
Andrew Caplin, Daniel Martin, Philip Marx +2
We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exog…