AI Bias: Can We Teach Fairness?

By Joel Leslie

Season 2, Episode 2

Duration: 00:07:51

Episode summary

If AI learns from us, and we’re biased, can an algorithm ever be fair? In this second episode of Season 2, we decode bias in AI, where it comes from, how it shows up in our daily lives, and what industry and academia are doing to fix it. From facial recognition failures and gendered hiring tools to the frameworks that now test AI for fairness, this episode explores how bias reflects our humanity, and how we can design accountability around it. What we discuss: Everyday examples of bias in AI you’ve already encountered The difference between historical, design, and context bias The rise of bias auditing frameworks from NIST, OECD, and Australia’s AI Ethics Principles The new roles shaping the future of responsible AI, from bias engineers to AI auditors Simple actions you can take to spot and challenge bias in the AI tools you use Because fairness in AI isn’t automatic. It’s intentional. Season 2 of Decoded: AI for Everyone is powered by Strategen AI, Where Research Meets Execution. RESOURCES Learn more and explore the tools featured in this episode: Podcast Website : decoded-podcast.com Prompts & Tools : promptengineeringcookbook.com AI Strategy & Research : strategen-ai.com LinkedIn : linkedin.com/company/decoded-ai-for-everyone

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About Decoded: AI for Everyone

Decoded: AI for Everyone is Joel Leslie's non-technical podcast about artificial intelligence and its impact on work, creativity, decision-making, trust and everyday life. The series helps listeners understand AI without hype, jargon or fear, connecting practical examples to broader questions about technology, people and society.

Joel Leslie is an Australian AI, data and digital transformation leader specialising in safe AI adoption, AI governance, AI ethics and data governance.

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