Using Role-Playing Scenarios to Identify Bias in LLMs

Software Engineering Institute (SEI) Podcast Series

Contenu fourni par Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

2M ago 45:07

MP3•Maison d'episode

Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on October 11, 2024 16:18 (1M ago)

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.

Harmful biases in large language models (LLMs) make AI less trustworthy and secure. Auditing for biases can help identify potential solutions and develop better guardrails to make AI safer. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Katie Robinson and Violet Turri, researchers in the SEI’s AI Division, discuss their recent work using role-playing game scenarios to identify biases in LLMs.

429 episodes

#Science #Tech #Carnegie Mellon University Software Engineering Institute #Members of Technical Staff at the Software Engineering Institute #Cybersecurity #Artificialintelligenceengineering #Futuretech #Softwareengineering