When an AI Hiring Tool Learned the Wrong Lessons

At first, the new AI looked perfect. It scanned thousands of CVs in seconds and promised fast, efficient, accurate candidate screening. Weeks after deployment, the tool kept recommending the same profiles. Kemi discovered the cause: the training data was biased toward a narrow group of past hires. The system simply copied those patterns. She paused the system and expanded the dataset. The team refocused the model on skills and performance instead of background. After retraining, recommendations became fairer and more accurate. “AI is only as good as the data we feed it,” Kemi reminded her team.

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