Show Notes
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As AI continues to shape decisions in hiring, finance, healthcare, and beyond, there's a growing concern about algorithmic bias—when machines learn from flawed data and end up making unfair or discriminatory decisions. In this episode, we break down: What algorithmic bias really means
How biased training data, flawed design, and proxy attributes contribute
Shocking real-world examples—from biased resume screening to discriminatory mortgage rates
Actionable strategies to reduce bias in AI systems
We’ll also explore the importance of diverse datasets, human oversight, and inclusive development teams in building fair, transparent, and responsible AI.
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Watch Video on YouTube 🎙️ Welcome back to The Rest is AI!
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