Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment
Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as fully-automated approaches are unlikely to be perfectly safe and reliable. However, most existing XAI techniques are not informed by the understandings of task-specific needs of humans for explanations. Thus, we took a first step toward understanding what forms of XAI humans require in damage detection tasks. We conducted an online crowdsourced study to understand how people explain their own assessments, when evaluating the severity of building damage based on satellite imagery. Through the study with 60 crowdworkers, we surfaced six major strategies that humans utilize to explain their visual damage assessments. We present implications of our findings for the design of XAI methods for such visual detection contexts, and discuss opportunities for future research.
- Pub Date:
- November 2021
- Computer Science - Human-Computer Interaction;
- Computer Science - Artificial Intelligence
- Accepted at NeurIPS 2021 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response (AI+HADR 2021)