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Investigating Causally Augmented Sparse Learning as a Tool for Meaningful Classification
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Investigating Causally Augmented Sparse Learning as a Tool for Meaningful Classification

Andrew O'Brien, Edward Kim and Rosina Weber
IEEE ... International Conference on Artificial Intelligence and Knowledge Engineering (AIKE ...) (Online), pp 33-37
01 Jan 2023
DOI: https://doi.org/10.1109/AIKE59827.2023.00013

Abstract

Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science, Theory & Methods Science & Technology Computer Science Technology
Scientists and policy makers have become interested in ways to limit the harm caused by machine learning methods. Algorithmic recourse attempts to limit harm done by making action recommendations that change the undesirable output from a machine learning model. A major challenge with recourse recommendations is their tendency to suggest actions that change the model output without changing the target being predicted. Recourse recommendations that improve both the model prediction and the underlying target are referred to as meaningful. In this paper, we use a technique from knowledge discovery as the basis for the first machine learning algorithm designed so that recourse recommendations generated for its prediction will be meaningful. We empirically compare our algorithm to other common classification algorithms and show it has significantly better performance in terms of meaningful recourse without sacrificing accuracy.

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