Cited by 25Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Explainable, Normative, and Justified Agency

Open access
Langley, Pat·2019·Proceedings of the AAAI Conference on Artificial Intelligence

Abstract

In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area.

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Crossref · 10.1609/aaai.v33i01.33019775

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  • The framework introduced in [51] relies on norms (called 'principles') inferred from ethical judgments using inductive logic programming.

    “For example, the framework introduced in [51] relies on norms (called 'principles') inferred from ethical judgments using inductive logic programming.”
  • A lot of work has been done on argumentation and dialog games, but the focus in these areas is generally the logical structure of the framework to express and to relate arguments or the protocol to exchange arguments.

    “A lot of work has also been done on argumentation and dialog games [6, 11, 51, 53] but the focus in these areas is generally the logical structure of the framework to express and to relate arguments or the protocol to exchange arguments.”

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Research works printed but not linked: 3

  1. Colaco, Z., and Sridharan, M. 2015. What Happened and Why? A Mixed Architecture for Planning and Explanation Generation in Robotics. Australasian Conference on Robotics and Automation . Canberra, Australia.
  2. Erwig, M.; Fern, A.; Murali, M.; and Koul, A. 2018. Explaining Deep Adaptive Programs via Reward Decomposition. Proceedings of the 2018 IJCAI Workshop on Explainable Artificial Intelligence , 40-44. Stockholm.
  3. Langley, P.; Barley, M.; Meadows, B.; Choi, D.; and Katz, E. P. 2016. Goals, Utilities, and Mental Simulation in Continuous Planning. Proceedings of the Fourth Annual Conference on Cognitive Systems . Evanston, IL.

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Authors
Langley, P.
Venue
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2019-07-17
Volume
33
Issue
01
Pages
9775-9779
ISSN
  • 2159-5399 (ISSN-L)
  • 2374-3468
OpenAlex
W2966649526
Cited by
25
References
23
Citations by year
2026: 3 · 2025: 2 · 2024: 1 · 2022: 3 · 2021: 6 · 2020: 6 · 2019: 4
FWCI
1.82
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
  • AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Funders
Office of Naval Research (N00014-17-1-2434, N00014-17-1, N00014-15-1-2517, N00014)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
Keywords
explainable AI, normative behavior, social norms, decision explanation, AI agents, testbed

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