Falsifying Predictive Algorithm
This addresses the need to identify unintended predictions in algorithms before deployment, particularly in consequential settings like admissions and criminal justice, though it is incremental by building on existing falsification practices.
The paper tackles the problem of algorithms predicting unintended outcomes by proposing a falsification framework that statistically tests discriminant validity, comparing prediction losses across intended and impermissible proxies. In an admissions setting, it established validity for gender but not for race, demonstrating its use as an early check before fairness analyses.
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 accommodates both this setting and the case with a single permissible proxy. Throughout we use nonparametric hypothesis testing methods that make minimal assumptions on the data-generating process. We illustrate the method in an admissions setting, where the framework establishes discriminant validity with respect to gender but fails to establish discriminant validity with respect to race. This demonstrates how falsification can serve as an early validity check, prior to fairness or robustness analyses. We also provide analysis in a criminal justice setting, where we highlight the limitations of our framework and emphasize the need for complementary approaches to assess other aspects of construct validity and external validity.