Harnessing Explanations to Bridge AI and Humans

By Vivian Lai, Samuel Carton, Chenhao Tan. 2020

6 citations recorded by the source corpus.

Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive power. In these applications, however, full automation is often not desired due to ethical and legal concerns. The research community has thus ventured into developing interpretable methods that explain machine predictions. While these explanations are meant to assist humans in understanding machine predictions and thereby allowing humans to make better decisions, this hypothesis is not supported in many recent studies. To improve human decision-making with AI assistance, we propose future directions for closing the gap between the efficacy of explanations and improvement in human performance.

Subfields: Artificial Intelligence, Political Science and International Relations, Safety, Robustness & Interpretability

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Citations in the corpus

Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021, background): “And while utility is best understood in the context of real-world applications (Lai et al., 2020), we nonethe-less aim to identify vacuous explanations that do not provide new information.”