AI for recruiting is the application of artificial intelligence (such as machine learning, natural language processing, and sentiment analysis) to the recruitment function.
On the one hand AI screening can mitigate some intrinsic human racial bias in decision making, by removing the unconscious bias humans bring to evaluating candidates and CVs. AI models can be tasked with ensuring that job application outcomes are fairer and not based on data correlated with protected demographic variables such as race and gender. The idea is that computers can assess data points objectively – free from the assumptions, biases, and mental fatigue to which humans are susceptible.
In reality, because historical recruitment data is often used to train the machine learning algorithm, it can still cause and even amplify past bias. This could result in locking ethnic minorities out of employment or, at least, heavily hinder their possibilities to be considered for jobs that they are qualified for.
In 2018, Amazon’s use of AI for hiring was discovered to favour male job candidates, because its algorithms had been trained on 10 years’ worth of internal data that heavily skewed male. The algorithm was trained, in effect, to believe that male candidates were better than female candidates.
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This video argues that hiring is largely analogue and broken. This leads to major problems such as inefficiency, ineffectiveness (50% of first-year hires fail), poor candidate experience, and lack of diversity. The hiring process is plagued by gender bias, age bias, socioeconomic bias, and racial bias. Pymetrics intentionally audits algorithms to weed out unconscious human […]
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