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Bias in AI: Impact, Challenges, and Opportunities

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Manage episode 302995081 series 2487640
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute 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://ppacc.player.fm/legal.

In this podcast from the Carnegie Mellon University Software Engineering Institute, Carol Smith, a senior research scientist in human-machine interaction, and Jonathan Spring, a senior vulnerability researcher, discuss the hidden sources of bias in artificial intelligence (AI) systems and how systems developers can raise their awareness of bias, mitigate consequences, and reduce risks.

  continue reading

416 episodes

Artwork
iconShare
 
Manage episode 302995081 series 2487640
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute 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://ppacc.player.fm/legal.

In this podcast from the Carnegie Mellon University Software Engineering Institute, Carol Smith, a senior research scientist in human-machine interaction, and Jonathan Spring, a senior vulnerability researcher, discuss the hidden sources of bias in artificial intelligence (AI) systems and how systems developers can raise their awareness of bias, mitigate consequences, and reduce risks.

  continue reading

416 episodes

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