Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
This addresses the need for more effective human-AI collaboration in organizational settings, though it appears incremental as it builds on existing practices of seeking second opinions.
The paper tackles the problem of monolithic algorithmic recommendations in human-AI collaboration by proposing a method to identify and recommend experts likely to provide complementary opinions, aiming to leverage productive disagreement.
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.