Algorithmic Recourse in Mental Healthcare

Abstract

This paper explores using algorithmic recourse as a tool in mental healthcare. Algorithmic recourse provides explanations and recommendations to individuals who want to reverse a machine learning prediction and has been widely used in various domains such as finance and marketing. However, its application in mental healthcare has been restricted. This paper addresses this by examining the potential benefits and challenges of using algorithmic recourse in mental healthcare, specifically in how changing one's behavior may affect their quality of life and well-being. The paper proposes a new classification-based framework for algorithmic recourse in mental healthcare. The proposed framework considers both observed and latent variables to account for the individuality of individuals and provides a more comprehensive understanding of mental health outcomes. The research results can provide valuable insights for future work and help bridge the gap between machine learning and mental healthcare.

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