What is Algorithmic Bias in AI?

Summary: The video discusses the impact of algorithmic bias in AI algorithms used for patent discovery and decision-making, emphasizing that the origins of this bias lie in data collection and coding practices rather than the algorithms themselves. It highlights the importance of training data quality and presents examples of biased AI applications, while outlining strategies for combating algorithmic bias through AI governance and inclusive practices.

Keypoints:

• AI algorithms are increasingly used in patent discovery and decision-making.
• Algorithmic bias often arises from the quality of training data, not the algorithms themselves.
• Non-representative or incorrectly classified data can create reinforcing feedback loops of bias.
• Examples include resumé screening tools that discriminate against women and financial algorithms that impose higher rates on minority borrowers.
• To combat algorithmic bias, it’s essential to implement AI governance measures.
• Strategies include representative data collection, biased detection with human oversight, transparent AI systems, and inclusive development teams.