Trends and Trajectories for Explainable, Accountable and Intelligible Systems

TLDR

Advances in AI, sensors, and big data are reshaping society, making it crucial that people understand and control increasingly autonomous systems, especially as HCI must embed intelligible interfaces. The study examines how HCI researchers can foster accountable systems through a literature analysis of 289 core papers on explanations and 12,412 citing works. The authors mapped the research landscape using topic modeling, co‑occurrence, and network analysis across domains such as algorithmic accountability, interpretable machine learning, context‑awareness, cognitive psychology, and software learnability. The analysis uncovers fading and emerging trends in explainable systems, highlights closely connected versus isolated domains, and suggests future research directions.

Abstract

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 implique-cations and directions for future research towards this goal.