评估人类解释策略对人机视觉决策的影响

人工智能正越来越多地被部署在灾难救援和放射学等高风险领域，以辅助从业者在图像解读过程中进行决策。可解释人工智能技术已被开发并部署，以向用户提供人工智能做出特定预测的原因。然而 recent research suggests that these techniques may confuse or mislead users. We conducted a series of two studies to uncover strategies human use to explain decisions and then understand how those explanation strategies impact visual decision-making. In our first study, we elicit explanations from humans when assessing and localizing damaged buildings after natural disasters from satellite imagery and identify four core explanation strategies that humans employed. We then follow-up by studying the impact of these explanation strategies by framing explanations from Study 1 as if they were generated by AI and showing them to a different set of decision-makers performing the same task. We provide initial insights on how causal explanation strategies improve humans' accuracy and calibrate humans' reliance on AI when the AI is incorrect. However, we also find that causal explanation strategies may lead to incorrect rationalizations when the AI presents a correct assessment with incorrect localization. We explore the implications of our findings for the design of human-centered explainable AI and address directions for future work.