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How Leaders Can Mitigate AI Bias for Women - an Interview with Sarah Lloyd Favaro, Senior Solutions Director, Office of Responsible AI and Governance at HCLTech

 

In the latest Fearless Female Leadership interview, I had the honor of talking with Sarah Lloyd Favaro, Senior Solutions Director, Office of Responsible AI and Governance at HCLTech, about one of the most urgent and misunderstood leadership topics today: how leaders can mitigate AI bias for women.

Sarah’s career has always lived at the intersection of technology and learning. Long before generative AI swept into the mainstream, she was exploring how tech could enhance human capability (not replace it.) But with the rapid rise of AI tools, Sarah doubled down on understanding how these systems work, why bias appears, and how leaders can prepare their organizations for a future where AI is woven into every workflow.

What makes Sarah’s perspective so powerful is her blended expertise: she understands both the practical magic of AI and the very real risks. She believes strongly that if organizations benefit from AI’s productivity and innovation, they must also ensure equitable, responsibl...

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Safety Measures for Bias and AI - an Interview with Dr. Emily Barnes

 

I am thrilled to have had the opportunity to speak with Dr. Emily Barnes, Chief Digital Learning Officer, Lindenwood University, who discussed the critical issue of bias in artificial intelligence, particularly in academia, research, and tech.

Dr. Barnes shared her motivation for specializing in this field and explains the significance of mitigating AI bias to prevent adverse effects now and on future generations.

Real-world examples illustrate how biases in algorithms can disadvantage certain groups in ways that we may not have thought about.

Dr. Barnes also suggests strategies for companies to combat AI bias, such as creating diverse teams and demanding data transparency.

Key Takeaways:

  • The safety measures for bias and AI (00:00:01)
    Dr. Barnes shares the importance of safety measures for bias in AI and its impact on society.
  • Real-life example of bias in AI (00:06:37)
    An example of Amazon's algorithm favoring male applicants due to biased historical data is discussed.
  • Actions for comp
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