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As AI continues to shape decisions in hiring, finance, healthcare, and beyond, there's a growing concern about algorithmic biasโwhen 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 examplesโfrom biased resume screening to discriminatory mortgage rates
Actionable strategies to reduce bias in AI systems
Weโll also explore the importance of diverse datasets, human oversight, and inclusive development teams in building fair, transparent, and responsible AI.
๐ Website: https://www.shreyshukla.com/
๐ผ LinkedIn: https://www.linkedin.com/in/shreyas768/
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