Is Your Model Predicting the Past?

2023

Conference Paper

sf

When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and normative arguments. At the center of our proposal is a family of simple and efficient statistical tests, called backward baselines, that demonstrate if, and to what extent, a model recounts the past. Our statistical theory provides guidance for interpreting backward baselines, establishing equivalences between different baselines and familiar statistical concepts. Concretely, we derive a meaningful backward baseline for auditing a prediction system as a black box, given only background variables and the system’s predictions. Empirically, we evaluate the framework on different prediction tasks derived from longitudinal panel surveys, demonstrating the ease and effectiveness of incorporating backward baselines into the practice of machine learning.

@inproceedings{10.1145/3617694.3623225,
  title = {Is Your Model Predicting the Past?},
  author = {Hardt, Moritz and Kim, Michael P.},
  booktitle = {Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO)},
  publisher = {Association for Computing Machinery},
  month = oct,
  year = {2023},
  doi = {},
  url = {https://doi.org/10.1145/3617694.3623225},
  month_numeric = {10}
}