In 2018, Amazon scrapped its AI-powered hiring tool after discovering it systematically discriminated against women. This incident exposed the hidden dangers of algorithmic bias in recruitment systems. Let's explore the key issues:
Training Data Bias
The algorithm was trained on resumes submitted over a 10-year period, which reflected historical gender imbalances in tech. As shown in this study, the system penalized resumes containing words like "women's" or all-female colleges.Feedback Loop
When the system flagged female candidates, recruiters manually corrected it, reinforcing male-dominated patterns. This created a self-perpetuating cycle of bias.Opaque Decision-Making
The black-box nature of AI models made it difficult to identify and correct biases. As noted in our article on AI transparency, this lack of explainability is a major concern.
🚨 Key Takeaway: Algorithms mirror the biases of their creators. For deeper insights, check our exploration of AI ethics in hiring.