Special Guest Jeff Shelton - Chief Product Officer at Revenue.io, joins the AI Chicks to discuss the realities of AI and bias. The episode unravels the implications of biased AI on employment, finance, and other key areas, providing compelling first-hand examples. With a candid exploration of data privacy, the episode navigates the delicate balance between personalization and privacy in the digital landscape. Where AI bias is more likely to be problematic? Where is the potential to empower and streamline human endeavors? Listen to this episode to find out these insights and more!
The Truth About Bias & AI with Expert Jeff Shelton
Show Notes
SUMMARY ON EPISODE GUEST:
Jeff is a keen observer of the intersection between technology and bias, with a particular focus on how it can unknowingly infiltrate hiring processes. Over the year, Jeff has identified a critical flaw and watched as the models, which were designed to eliminate the tedium of manual resume reviews, unwittingly perpetuated gender bias - such as favoring male applicants over female.
Jeff's insights into these technological biases have shed light on the shortcomings of AI based system. Through his work, Jeff continues to advocate for more balanced and unbiased practices that allow truly qualified individuals to shine at their res, regardless of their background.
EPISODE COVERS:
1. Identifying biases in AI
2. How bias in data affects AI outputs
3. Discussion of ways to reduce bias in AI
4. AI's role in hiring and recruitment discrimination
5.. Ethical considerations in AI technology
6.. Anticipated advancements in AI technology in the next 5-10 years
7. Privacy concerns and personal data protection
8. Implementation of personal assistance and learning from AI
9. Explanation of cookies and data collection
10. Importance of improving the transparency of AI decision-making
TIMESTAMPS:
00:00 AI based on biased data has consequences.
06:37 AI revolution's current issues and future improvements.
10:17 Amazon's recruiting models favored men over women.
12:15 Insurance, finance, home loans and biases.
15:20 Embracing personalization but wary of excessive targeting.
17:42 AI models evolve to personalize and assist.
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