Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningDecision MakingEthicsSimilar Papers 제목 키워드 기반
PresAIse, A Prescriptive AI Solution for Enterprises
Prescriptive AI represents a transformative shift in decision-making, offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption often faces several challenges. The first cha…
Causal InferenceDecision MakingDomain Adaptable Prescriptive AI Agent for Enterprise
Despite advancements in causal inference and prescriptive AI, its adoption in enterprise settings remains hindered primarily due to its technical complexity. Many users lack the necessary knowledge and appropriate tools …
AI AgentCausal InferenceDecision MakingBootstrap Robust Prescriptive Analytics
We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations …
Decision MakingPredictive machine learning for prescriptive applications: a coupled training-validating approach
In this research we propose a new method for training predictive machine learning models for prescriptive applications. This approach, which we refer to as coupled validation, is based on tweaking the validation step in …
BIG-bench Machine LearningDecision MakingStochastic OptimizationA Theory of LLM Sampling: Part Descriptive and Part Prescriptive
Large Language Models (LLMs) are increasingly utilized in autonomous decision-making, where they sample options from vast action spaces. However, the heuristics that guide this sampling process remain under-explored. We …
Decision MakingDescriptive