The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Explainable Agency for Intelligent Autonomous Systems
Open accessAbstract
As intelligent agents become more autonomous, sophisti- cated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player’s path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more selfreliant, people will require that they explain their behaviors on demand. The more impressive these agents’ abilities, the more essential that we be able to understand them.
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“some contributions [50, 59] include the ability of a model to be interactive with the user as one of the goals targeted by an explainable ML model.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [59]
Citations
Cites
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,886
Building Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523
Explanation in Second Generation Expert Systems
Swartout, W. R., & Moore, J. D. (1993) · Second Generation Expert Systems · doi:10.1007/978-3-642-77927-5_24 · Cited by 160
Agents that Learn to Explain Themselves
Johnson, W. L. (1994) · Cited by 93
Plan Explainability and Predictability for Cobots.Preprint
Zhang, Y., Zhuo, H. H., & Subbarao Kambhampati, B. (2015) · arXiv (Cornell University) · Cited by 5
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598
Metadata
- Authors
- Langley, P., Meadows, B., Sridharan, M., & Choi, D.
- Affiliations
- University of Auckland (NZ)
- University of Kansas (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-11
- Volume
- 31
- Issue
- 2
- Pages
- 4762-4763
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2604602969
- Cited by
- 156
- References
- 5
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 10 · 2023: 14 · 2022: 15 · 2021: 25 · 2020: 28 · 2019: 31 · 2018: 11 · 2017: 4
- FWCI
- 9.23
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- explainable AI, autonomous agents, decision explanation, human-robot interaction, trust in autonomous systems, autonomous vehicles, model interpretability
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