Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis

DOI10.1007/s10994-022-06293-7arXiv2201.07135OpenAlexW4318948367MaRDI QIDQ6161200

Bruno Lepri, Andrea Passerini, Giovanni De Toni

Publication date: 27 June 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.07135

zbMATH Keywords

machine learningexplainable AIalgorithmic recoursecounterfactuals examples

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Explanation in artificial intelligence: insights from the social sciences
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
• Unnamed Item

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