
TL;DR
This paper introduces a statistical falsification framework to verify if predictive algorithms are estimating intended outcomes, helping identify unintended biases before deployment using minimal assumptions.
Contribution
It develops a novel, principled testing method for discriminant validity applicable with multiple proxies and minimal assumptions, inspired by causal inference and econometrics.
Findings
Framework successfully detects discriminant validity in admissions data
Identifies lack of discriminant validity with respect to race in the admissions setting
Highlights limitations of the approach in criminal justice applications
Abstract
Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended. These exposes highlight the need to identify when algorithms predict unintended quantities - ideally before deploying them into consequential settings. We propose a falsification framework that provides a principled statistical test for discriminant validity: the requirement that an algorithm predict intended outcomes better than impermissible ones. Drawing on falsification practices from causal inference, econometrics, and psychometrics, our framework compares calibrated prediction losses across outcomes to assess whether the algorithm exhibits discriminant validity with respect to a specified impermissible proxy. In settings where the target outcome is difficult to observe, multiple permissible proxy outcomes may be available; our framework…
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Taxonomy
TopicsEthics and Social Impacts of AI · Explainable Artificial Intelligence (XAI) · Generative Adversarial Networks and Image Synthesis
