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Trends and Trajectories for Explainable, Accountable and Intelligible Systems
- Source :
- CHI, Abdul, A, Vermeulen, J, Wang, D, Lim, B Y & Kankanhalli, M 2018, Trends and Trajectories for Explainable, Accountable and Intelligible Systems : An HCI Research Agenda . in R Mandryk & M Hancock (eds), CHI 2018-Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems : Engage with CHI . Association for Computing Machinery, ACM New York, NY, CHI '18, pp. 582:1-582:18, ACM SIGCHI Conference on Human Factors in Computing Systems, Montreal, Canada, 21/04/2018 . https://doi.org/10.1145/3173574.3174156
- Publication Year :
- 2018
- Publisher :
- ACM, 2018.
-
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 impli-cations and directions for future research towards this goal.
- Subjects :
- Topic model
Explanations
Interpretable machine learning
Learnability
Computer science
business.industry
05 social sciences
020207 software engineering
02 engineering and technology
Data science
Software
explainable artificial intelli-gence, explanations, intelligibility, interpretable machine learning
Accountability
0202 electrical engineering, electronic engineering, information engineering
0501 psychology and cognitive sciences
business
Intelligibility
Explainable artificial intelli-gence
050107 human factors
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Accession number :
- edsair.doi.dedup.....b1b8771a80b950ff282f035761e561bf