This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
The Hidden Bias in AI Hiring Systems: A Threat to Diversity and Inclusion
As AI-driven hiring systems continue to revolutionize the recruitment process, a pressing concern emerges: the risk of perpetuating socioeconomic biases. By 2026, a staggering 75% of AI-driven hiring systems are likely to unintentionally prioritize candidates with higher socioeconomic profiles due to algorithmic overfitting to biased online profiles.
The Root Cause: Algorithmic Overfitting
Algorithmic overfitting occurs when a machine learning model is too closely tailored to the training data, leading to biased predictions on unseen data. In the context of hiring, this means that AI systems might be "learning" to favor candidates with higher socioeconomic profiles, simply because they are overrepresented in the training data. This phenomenon is often referred to as "data drift" or "concept drift," where the underlying dynamics of the data change over time, rendering the model's predictions less acc...
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This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez | Sciencx (2025-10-09T20:24:45+00:00) **The Hidden Bias in AI Hiring Systems: A Threat to Diversit. Retrieved from https://www.scien.cx/2025/10/09/the-hidden-bias-in-ai-hiring-systems-a-threat-to-diversit/
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