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