For a few years now, AI has been proffered as the future of cost-effective and efficient recruitment, allowing users to screen millions of CVs in a matter of seconds. More interestingly, there are claims that it allows users to pinpoint the biases which exist in their overarching hiring process or even within the job listing itself. Studies of recruitment diversity have shown that more masculine words can dissuade female candidates from applying, and it is true AI could detect and replace this language with something more gender neutral. So could AI be a silver bullet for killing off hiring prejudices?
Recent attention has centred on the potential for unconscious algorithmic bias. That is, if you have biased data – no matter how much of it – the output is going to be biased. For example, a reliance on postcodes and schools will inevitably intersect with race and class. And if machine learning draws inferences from an already homogenous group of people,