In Episode 20, Bias in AI: Hidden Patterns and Their Impact, Jonathan Kyle Hobson continues his deep dive into the world of cognitive biases, this time focusing on how these biases not only shape human thinking but also influence AI systems. Building on the insights from Episode 19, this episode unpacks how biases—such as anchoring, Dunning-Kruger, curse of knowledge, and the framing effect—can subtly creep into AI models, affecting their outputs in profound ways.

Listeners will explore the mechanics of these cognitive shortcuts and how they manifest in AI, despite the perception that machines are objective. Hobson takes us through real-world examples, showing how AI systems inherit human biases through data and decision-making processes, leading to skewed results in areas like recommendation algorithms, search engines, and even healthcare systems.

But it’s not all doom and gloom—this episode also dives into solutions, including adversarial training and other techniques that can help detect and mitigate bias in AI, ensuring a more equitable and fair use of technology in the future. With AI playing an increasingly vital role in decision-making across industries, understanding and addressing these biases is critical to creating systems that are both intelligent and just.

Join us as we navigate the complexities of AI bias and explore how awareness and action can help build a better, more inclusive technological future.

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