Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the …
AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on …
While a vast collection of explainable AI (XAI) algorithms has been developed in recent years, they have been criticized for significant gaps with how humans produce and consume …
In many practical applications of AI, an AI model is used as a decision aid for human users. The AI provides advice that a human (sometimes) incorporates into their decision-making …
Prior work has identified a resilient phenomenon that threatens the performance of human- AI decision-making teams: overreliance, when people agree with an AI, even when it is …
Decisions made by human-AI teams (eg., AI-advised humans) are increasingly common in high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team …
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations …
R Paleja, M Ghuy… - Advances in neural …, 2021 - proceedings.neurips.cc
Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to enable humans to gain insight into the decision-making of machine learning models …
Despite the proliferation of explainable AI (XAI) methods, little is understood about end- users' explainability needs and behaviors around XAI explanations. To address this gap and …