An interesting question that arises is to what degree new technologies within the brave new world of AI-based hiring tools could help us reduce error, noise, and bias in our talent identification processes. And yet there have been salient news stories recently indicating that AI may actually contribute to even more bias and adverse impact against women–and that when algorithms are trained to emulate human recruiters, they may not just reproduce human biases, but also exacerbate them, engaging in a much more efficient form of discrimination. Of course, if AI is trained with biased data — for instance, if we teach it to predict which candidates will be rated positively by human interviewers — it will not just emulate, but also exacerbate, human bias: augmenting it and making it far more efficient. Consider that the average human interviewer will never even admit to making a hiring mistake, for they will indulge in confirmation bias to see the candidates they personally hired in a positive vein once they are tasked with rating their performance. Imagine an ethical, well-meaning, and open-minded human who has every intention of being fair in their hiring practices and is therefore determined to avoid gender bias in his — let’s assume he is male — hiring process.
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