Adaptive incentive engineering in citizen-centric AI
Adaptive incentive engineering in citizen-centric AI
Adaptive incentives are a valuable tool shown to improve the efficiency of complex multiagent systems and could produce win-win situations for all stakeholders. However, their application usage is very limited, partly due to a significant gap between the literature and practice. We argue that overcoming this gap requires addressing four open research challenges. First, the dynamic, volatile and uncertain nature of environments needs to be fully considered. Second, social factors including user acceptance, fairness, ethical considerations and trust have to match end users' expectations and needs. Third, the evaluation of mechanisms and systems has to be robust and focused on real-world outcomes and stakeholder requirements. Finally, all this has to be built on a reliable theoretical foundation. In order to overcome these open challenges in adaptive incentive engineering, tools from the fields of mechanism design and game theory can be used. This will help to achieve the opportunities adaptive incentives can provide to real-world practical environments, producing better AI systems for the benefit of all.
Citizen-Centric AI Systems, Artificial Intelligence, Multiagent Systems, Incentive Engineering, Mechanism Design, Explainability, Explainable AI, AI Ethics, AI regulation
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Koohy, Behrad
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Buermann, Jan
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Briggs, Pamela
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Pschierer-Barnfather, Paul
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Yazdanpanah, Vahid
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Gerding, Enrico
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Stein, Sebastian
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6 May 2024
Koohy, Behrad
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Buermann, Jan
46ae30cc-34e3-4a39-8b11-4cbb413e615f
Briggs, Pamela
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Pschierer-Barnfather, Paul
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Yazdanpanah, Vahid
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Gerding, Enrico
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Stein, Sebastian
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Koohy, Behrad, Buermann, Jan, Briggs, Pamela, Pschierer-Barnfather, Paul, Yazdanpanah, Vahid, Gerding, Enrico and Stein, Sebastian
(2024)
Adaptive incentive engineering in citizen-centric AI.
Alechina, N., Dignum, V., Dastani, M. and Sichman, J.S.
(eds.)
In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024).
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
6 pp
.
(doi:10.5555/3635637.3663258).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Adaptive incentives are a valuable tool shown to improve the efficiency of complex multiagent systems and could produce win-win situations for all stakeholders. However, their application usage is very limited, partly due to a significant gap between the literature and practice. We argue that overcoming this gap requires addressing four open research challenges. First, the dynamic, volatile and uncertain nature of environments needs to be fully considered. Second, social factors including user acceptance, fairness, ethical considerations and trust have to match end users' expectations and needs. Third, the evaluation of mechanisms and systems has to be robust and focused on real-world outcomes and stakeholder requirements. Finally, all this has to be built on a reliable theoretical foundation. In order to overcome these open challenges in adaptive incentive engineering, tools from the fields of mechanism design and game theory can be used. This will help to achieve the opportunities adaptive incentives can provide to real-world practical environments, producing better AI systems for the benefit of all.
Text
AAMAS24_Blue_Sky_Paper-8
- Accepted Manuscript
Text
Adaptive Incentive Engineering in Citizen-Centric AI - AAMAS 2024
- Version of Record
More information
Published date: 6 May 2024
Venue - Dates:
The 23rd International Conference on Autonomous Agents and Multi-Agent Systems, Cordis Hotel, Auckland, New Zealand, 2024-05-06 - 2024-05-10
Keywords:
Citizen-Centric AI Systems, Artificial Intelligence, Multiagent Systems, Incentive Engineering, Mechanism Design, Explainability, Explainable AI, AI Ethics, AI regulation
Identifiers
Local EPrints ID: 487550
URI: http://eprints.soton.ac.uk/id/eprint/487550
PURE UUID: a5e2e1b3-0160-471d-932e-3c654669c8f7
Catalogue record
Date deposited: 23 Feb 2024 17:34
Last modified: 30 Nov 2024 03:05
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Contributors
Author:
Behrad Koohy
Author:
Jan Buermann
Author:
Pamela Briggs
Author:
Paul Pschierer-Barnfather
Author:
Vahid Yazdanpanah
Author:
Enrico Gerding
Author:
Sebastian Stein
Editor:
N. Alechina
Editor:
V. Dignum
Editor:
M. Dastani
Editor:
J.S. Sichman
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