There is a growing debate about the implications of multiplicity for algorithmic decision-making, with concerns of unfair treatment due to conflicting predictions and explanations on one hand, and the potential to find less discriminatory and more interpretable models on the other. In this tutorial, we aim to increase awareness of different perspectives on multiplicity and relate them to broader discussions in the FAccT community.
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We’ve put together a list of papers related to multiplicity in ML. It began with this literature review, and has gradually grown to include additional papers over time.
The list is not comprehensive, but we try to update it periodically as new papers come to our attention. As the list is compiled and displayed using several automated processes, it is possible we have missed your work or there may be errors in the list. If your work is missing or something looks off, please let us know using this form, and we’ll be happy to make the necessary updates promptly.
Papers are ordered randomly. Download the entire list from here (CSV files can be cached by the browser, so please hard refresh the webpage for the latest version).