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Explainable AI and Trust Issues

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Content provided by Metaculus and Metaculus Inc.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Metaculus and Metaculus Inc. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

https://www.metaculus.com/notebooks/9613/explainable-ai-and-trust-issues/

AI researchers exploring ways to increase trust in AI recognize that one barrier to trust, often, is a lack of explanation. This recognition has led to the development of the field of Explainable Artificial Intelligence (XAI). In their paper Formalizing Trust in Artificial Intelligence, Jacovi et al. classify an AI system as trustworthy to a contract if it is capable of maintaining this contract: A recommender algorithm might be trusted to make good recommendations, and a classification algorithm might be trusted to classify things appropriately. When a classification algorithm makes grossly inappropriate classifications, we feel betrayed, and the algorithm loses our trust. (Of course, a system may be untrustworthy even as we continue to place trust in it.) This essay explores current legal implementations of XAI as they relate to explanation, trust, and human data subjects (e.g. users of Google or Facebook)—while forecasting outcomes relevant to XAI.

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20 episodes

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Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on February 26, 2024 22:32 (1+ y ago)

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Manage episode 342830781 series 3355997
Content provided by Metaculus and Metaculus Inc.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Metaculus and Metaculus Inc. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

https://www.metaculus.com/notebooks/9613/explainable-ai-and-trust-issues/

AI researchers exploring ways to increase trust in AI recognize that one barrier to trust, often, is a lack of explanation. This recognition has led to the development of the field of Explainable Artificial Intelligence (XAI). In their paper Formalizing Trust in Artificial Intelligence, Jacovi et al. classify an AI system as trustworthy to a contract if it is capable of maintaining this contract: A recommender algorithm might be trusted to make good recommendations, and a classification algorithm might be trusted to classify things appropriately. When a classification algorithm makes grossly inappropriate classifications, we feel betrayed, and the algorithm loses our trust. (Of course, a system may be untrustworthy even as we continue to place trust in it.) This essay explores current legal implementations of XAI as they relate to explanation, trust, and human data subjects (e.g. users of Google or Facebook)—while forecasting outcomes relevant to XAI.

  continue reading

20 episodes

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