Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities

• URL: http://arxiv.org/abs/2112.08268v1
• Date: Wed, 15 Dec 2021 17:02:07 GMT
• Title: Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
• Authors: Eyke H\"ullermeier
• Abstract summary: Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling to its use for prescriptive modeling. prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. To support the data-driven design of decision-making agents that act in a rational but at the same time responsible manner, a rigorous methodological foundation of prescriptive ML is needed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the purpose of prediction (of ground-truth facts) to its use for prescriptive modeling. What is meant by this is the task of learning a model that stipulates appropriate decisions about the right course of action in real-world scenarios: Which medical therapy should be applied? Should this person be hired for the job? As argued in this article, prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. Therefore, to support the data-driven design of decision-making agents that act in a rational but at the same time responsible manner, a rigorous methodological foundation of prescriptive ML is needed. The purpose of this short paper is to elaborate on specific characteristics of prescriptive ML and to highlight some key challenges it implies. Besides, drawing connections to other branches of contemporary AI research, the grounding of prescriptive ML in a (generalized) decision-theoretic framework is advocated.

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