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Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly.
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J. Fjeld et. al., “Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for ai,” Berkman Klein Center Research Publication , no. 2020-1, 2020
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W. Chu, “A decentralized approach towards responsible ai in social ecosystems,” 2021
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A. Lavaei, B. Zhong, M. Caccamo, and M. Zamani, “Towards trustworthy ai: safe-visor architecture for uncertified controllers in stochastic cyber-physical systems,” in Proceedings of CAADCPS’21 , 2021, pp. 7–8
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J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, and W. Luo, “Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” IEEE Transactions on Dependable and Secure Computing , vol. 18, no. 5, pp. 2438–2455, 2021
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I. Naja, M. Markovic, P. Edwards, and C. Cottrill, “A semantic framework to support ai system accountability and audit,” in The Semantic Web , 2021, pp. 160–176
2021
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B. S. Miguel, A. Naseer, and H. Inakoshi, “Putting accountability of ai systems into practice,” in IJCAI’21 , 2021, pp. 5276–5278
2021
Later among the works it cites.
Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Douglas, and C. Sanderson, “Software engineering for responsible ai: An empirical study and operationalised patterns,” in ICSE-SEIP’22 . IEEE, 2022, pp. 241–242
2022
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I. Barclay, A. Preece, I. Taylor, S. K. Radha, and J. Nabrzyski, “Providing assurance and scrutability on shared data and machine learning models with verifiable credentials,” CCPE , p. e6997, 2022
2022
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2022
Closest in time.
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