This year has seen a wave of new research of the unintended consequences of an Artificial Intelligence industry dominated by middle-class white men, teaching itself with unchecked and unregulated data sources.
New research published by AI Now Institute, New York University in April 2019 concluded that AI is in the midst of a ‘diversity crisis’ that needs urgent attention:  “Recent studies found only 18% of authors at leading AI conferences are women, and more than 80% of AI professors are men. This disparity is extreme in the AI industry: Women comprise only 15% of AI research staff at Facebook and 10% at Google […]  For black workers, the picture is even worse. For example, only 2.5% of Google’s workforce (in AI) is black, while Facebook and Microsoft are each at 4%”
But the levels of gender and racial representation of those working in the industry is only part of the challenge –  there is an increasing body of evidence that suggests that AI algorithms themselves are unhealthily influenced by discrimination and biases of the past.
Let’s take three examples: Firstly, Amazon had to take an automated recruitment robot out of service, after it was found to be favouring male CVs over female for technical jobs. Google, had to adjust an algorithm that  was defaulting its translations to the masculine pronoun.  Our third example adds racial rather than gender bias:  Joy Buolamwini, a researcher from Massachusetts Institute of Technology found that a facial analysis tool, sold by Amazon, would not recognise her unless she held up a white mask to her face.
An AI algorithm will diligently go about its task of recognising patterns in data more efficiently than any human – but the old adage “garbage in, garbage out” still applies in the digital world. Or, to update it clumsily for the 21st century:  

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