Member-only story
Imagine a company launching an AI-powered hiring tool designed to help human resources select the best candidates. Initially, the tool appears effective, helping identify candidates quickly and efficiently.
But soon, some candidates report that certain groups — women, minorities, or those from specific socioeconomic backgrounds — seem to receive fewer interview invitations. The issue raises a pressing question: how can we ensure that AI tools make fair, unbiased decisions?
This scenario highlights a growing concern in AI: bias. AI models often reflect biases present in their training data, leading to outcomes that can unintentionally discriminate against certain groups. As AI continues to make decisions impacting everything from job offers to medical diagnoses, detecting and reducing bias has become essential to building ethical, trustworthy systems.
In this article, we’ll discuss how to implement ethical bias detection in AI models, exploring techniques to identify and mitigate biases and providing practical insights and code to…
