Go offline with the Player FM app!
Explainable AI and Trust Issues
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on February 26, 2024 22:32 ()
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 342830781 series 3355997
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.
20 episodes
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on February 26, 2024 22:32 ()
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 342830781 series 3355997
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.
20 episodes
All episodes
×Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.