The issue of bias in AI has become a focal point in recent discussions, both in the academia and amongst practitioners and policymakers. I observe a lot of confusion and diffusion in those discussions. At the risk of seeming patronizing, my advice is to engage only with the understanding of the specific jargon which is used, and particularly how it’s used in this context. Misunderstandings create confusion and blur the path forward.
Here is a negative, yet typical example:
In artificial intelligence (AI)-based predictive models, bias – defined as unfair systematic error – is a growing source of concern1.
This post tries to direct those important discussions to the right avenues, providing some clarifications, examples for common pitfalls, and some qualified advice from experts in the field on how to approach this topic. If nothing else, I hope you find this piece thought-provoking.