Doubting AI Predictions: Influence-Driven Second Opinion Recommendation

• URL: http://arxiv.org/abs/2205.00072v1
• Date: Fri, 29 Apr 2022 20:35:07 GMT
• Title: Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
• Authors: Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski
• Abstract summary: We propose a way to augment human-AI collaboration by building on a common organizational practice: identifying experts who are likely to provide complementary opinions. The proposed approach aims to leverage productive disagreement by identifying whether some experts are likely to disagree with an algorithmic assessment.
• Score: 92.30805227803688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this paper, we propose a way to augment human-AI collaboration by building on a common organizational practice: identifying experts who are likely to provide complementary opinions. When machine learning algorithms are trained to predict human-generated assessments, experts' rich multitude of perspectives is frequently lost in monolithic algorithmic recommendations. The proposed approach aims to leverage productive disagreement by (1) identifying whether some experts are likely to disagree with an algorithmic assessment and, if so, (2) recommend an expert to request a second opinion from.

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