BriefGPT.xyz
May, 2022
利用偏好获取用户感知的算法性干预
Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences
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Giovanni De Toni, Paolo Viappiani, Bruno Lepri, Andrea Passerini
TL;DR
本研究提出了一种新的范式,通过引入首个基于人类偏好征集的人在环路方法,将用户视为过程的主动参与者,结合蒙特卡洛树搜索的强化学习智能体以提供个性化干预以实现算法回溯。
Abstract
counterfactual interventions
are a powerful tool to explain the decisions of a black-box decision process, and to enable
algorithmic recourse
. They are a sequence of actions that, if performed by a user, can over
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