[R] NeurIPS 2020 | Probabilistic Approaches for Algorithmic Recourse With Limited Causal Knowledge

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The rise of machine learning (ML) has made centralized decision-making more efficient than ever — while also raising some difficult but important questions. In real-world scenarios such as pre-trial bail, loan approval, or prescribing medications, it is not enough for black-box models to be accurate and robust — an algorithms’ decisions are also expected to be explainable, so their impact in real-world settings can be aligned with socially relevant values such as fairness, privacy and accountability.

Here is a quick read: NeurIPS 2020 | Probabilistic Approaches for Algorithmic Recourse With Limited Causal Knowledge

The paper Algorithmic Recourse Under Imperfect Causal Knowledge: A Probabilistic Approach is on arXiv. This is a NeurIPS 2020 spotlight paper, scheduled for a ten-minute presentation on Wednesday, December 9, at 8:20am PST.

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