Will companies using AI to screen resumes affect recruitment fairness?

Companies using AI for resume screening can both positively and negatively influence recruitment fairness. Its impact depends critically on how the AI system is designed, implemented, and monitored.

AI can enhance fairness by removing identifiable human biases (like gender or race cues) and applying consistent criteria to every applicant. However, algorithmic bias is a major risk; systems trained on historical hiring data often perpetuate past unfair patterns if those data reflected discriminatory practices. The lack of transparency ("black box" nature) of some AI makes it hard to audit for bias. Effectiveness varies based on job complexity, as AI is generally better suited for screening high-volume roles with clear skill requirements than nuanced leadership positions. Constant validation and oversight are essential.

To mitigate fairness risks, companies must rigorously audit algorithms for disparate impact, proactively seek diverse training data, ensure model outputs are explainable, and maintain human oversight. Responsible implementation, including continuous monitoring for bias drift, can leverage AI's potential to make initial screening more consistent and efficient. Ultimately, AI itself is a tool whose fairness effect is determined by diligent, ethical deployment practices focused on reducing, not amplifying, bias.