Context matters for fairness -- a case study on the effect of spatial distribution shifts

• URL: http://arxiv.org/abs/2206.11436v2
• Date: Fri, 24 Jun 2022 21:09:45 GMT
• Title: Context matters for fairness -- a case study on the effect of spatial distribution shifts
• Authors: Siamak Ghodsi, Harith Alani, and Eirini Ntoutsi
• Abstract summary: We present a case study on the newly released American Census datasets. We show how remarkably can spatial distribution shifts affect predictive- and fairness-related performance of a model. Our study suggests that robustness to distribution shifts is necessary before deploying a model to another context.
• Score: 10.351739012146378
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon. However, an important and often challenging aspect in utilizing such systems is to distinguish validity for the range of their application especially under distribution shifts, i.e., when a model is deployed on data with different distribution than the training set. In this paper, we present a case study on the newly released American Census datasets, a reconstruction of the popular Adult dataset, to illustrate the importance of context for fairness and show how remarkably can spatial distribution shifts affect predictive- and fairness-related performance of a model. The problem persists for fairness-aware learning models with the effects of context-specific fairness interventions differing across the states and different population groups. Our study suggests that robustness to distribution shifts is necessary before deploying a model to another context.

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