Artwork
iconShare
 
Manage episode 492250717 series 3485568
Content provided by Rick Spair. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Rick Spair 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.

Send us a text

Overview of Explainable AI (XAI), a field dedicated to making AI systems more transparent, interpretable, and trustworthy. It begins by defining the "black box" problem in AI, distinguishing between systems that are intentionally opaque (e.g., proprietary algorithms) and those that become so due to their inherent complexity (e.g., deep neural networks). The document then details the multifaceted objectives of XAI, which include fostering trust, ensuring accountability, enabling auditability, promoting fairness, improving models, and empowering users. It further categorizes various XAI methodologies into intrinsic (white box models like decision trees) and post-hoc techniques (like LIME and SHAP), which are applied after a model is trained. Finally, the text explores XAI's critical applications across high-stakes domains such as healthcare, finance, and autonomous systems, highlighting its role in mitigating risks, addressing regulatory demands, and navigating the evolving ethical and legal landscape.

  continue reading

131 episodes