In Episode 34 of '100 Days of Data,' hosts Jonas and Amy dive into the complex issue of bias in AI. They explore how algorithms can inherit societal prejudices through skewed data, flawed design choices, and unmonitored deployment. Through real-world examples from healthcare, finance, and insurance, they demonstrate the serious impact biased models can have on people’s lives — from unequal loan approvals to racial disparities in medical care. The duo discusses fairness as a multifaceted goal in AI, the challenges of achieving it, and practical tools like bias mitigation techniques and explainability frameworks. They emphasize that addressing bias is not just a technical challenge but a social responsibility involving diverse teams, continual monitoring, and transparency. Tune in for an essential conversation about making AI systems more just and trustworthy in real-world applications.