‘Algorithmic bias’ is a thing and we need to challenge it before it’s too late!
Recently, I read an HR industry article stating how “adding A.I. to the mix gives the tool an important advantage: rooting out bias.” In theory, AI has the potential to produce something humans are not capable of, recruiting candidates with no unconscious biases.
However, what if I tell you that this is not happening, ironically, because of humans.
Currently, in order for AI to run it needs data and code. Numerous studies have found that machine learning algorithms are picking up deeply ingrained race and gender prejudices concealed within the patterns of language use. Even Microsoft’s latest venture, GitHub, found gender bias in their open source code.
HireVue, a company that develops recruiting tools used by such multinationals as Unilever, uses video interviewing to screen candidates for its clients by applying a combination of facial analysis and AI that measures and examines such behaviors as word choice, gestures, and voice inflections. If the engineers developing these tools commit certain kinds of errors or oversights in gathering their input data or building machine learning systems, unintentional bias could be the direct result. Technicians could, for instance, introduce selection bias in gathering input data, or unnecessarily narrow options for certain people, thus leaving them short of the economic opportunities afforded to others.
Therefore, before we radically implement AI into every step of the recruitment process, we must first define what equality is and then teach the ‘machines’ - Startups like Knowhere and W&B are tackling this.
It’s important to note, AI has incredible power to improve people’s lives by matching them to the right jobs. Consequently, I urge people to read more about the technology and find ways to bring equality into the new world – just like Isis Anchalee, who launched the campaign #ILookLikeAnEngineer.
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Luca Feser, Adecco CEO For One Month Switzerland
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