Trends and Trajectories for Explainable, Accountable and Intelligible Systems

Advances in artificial intelligence, sensors and big data man-agement have far-reaching societal impacts. As these sys-tems augment our everyday lives, it becomes increasingly important for people to understand them and remain in con-trol. We investigate how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explainable systems, as well as 12,412 citing papers. Using topic modeling, co-oc-currence and network analysis, we mapped the research space from diverse domains, such as algorithmic accounta-bility, interpretable machine learning, context-awareness, cognitive psychology, and software learnability. We reveal fading and burgeoning trends in explainable systems, and identify domains that are closely connected or mostly iso-lated. The time is ripe for the HCI community to ensure that the powerful new autonomous systems have intelligible in-terfaces built-in. From our results, we propose several impli-cations and directions for future research towards this goal.