@inproceedings{86a64a380544431293df1253d8541ad5,
title = "Model Multiplicity: Opportunities, Concerns, and Solutions",
abstract = "Recent scholarship has brought attention to the fact that there often exist multiple models for a given prediction task with equal accuracy that differ in their individual-level predictions or aggregate properties. This phenomenon - which we call model multiplicity - can introduce a good deal of flexibility into the model selection process, creating a range of exciting opportunities. By demonstrating that there are many different ways of making equally accurate predictions, multiplicity gives model developers the freedom to prioritize other values in their model selection process without having to abandon their commitment to maximizing accuracy. However, multiplicity also brings to light a concerning truth: model selection on the basis of accuracy alone - the default procedure in many deployment scenarios - fails to consider what might be meaningful differences between equally accurate models with respect to other criteria such as fairness, robustness, and interpretability. Unless these criteria are taken into account explicitly, developers might end up making unnecessary trade-offs or could even mask intentional discrimination.",
keywords = "arbitrariness, discrimination, fairness, Model multiplicity, predictive multiplicity, procedural multiplicity, recourse",
author = "Emily Black and Manish Raghavan and Solon Barocas",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 ; Conference date: 21-06-2022 Through 24-06-2022",
year = "2022",
month = jun,
day = "21",
doi = "10.1145/3531146.3533149",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "850--863",
booktitle = "Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022",
}