CDEI publishes “Review into bias in algorithmic decision-making”
In the 92-page nicely designed and worded document, the UK Centre for Data Ethics and Innovation woes to address “bias” without providing any definition of what a ‘bias’ is.
Originally published on the 6th Dec, updated 13th Dec.
My attention was recently caught by a post on LinkedIn announcing that CDEI published a review into bias in algorithmic decision-making.
And the performance doesn’t disappoint: in the 92-page nicely designed and worded document, the Centre for Data Ethics and Innovation woes to address bias but doesn’t provide any single definition and scientific research survey on what a bias is – authentic.
It is true that the CDEI published in 2019 a paper in which the tone is given from the last sentence: ” As our societies become increasingly dependent on algorithms, so we are seeing our age-old prejudices, biases and implicit assumptions reflected back at us in digital form. But the algorithmic systems we use also have the potential to amplify, accentuate and systemise our biases on an unprecedented scale, all while presenting the appearance of objective, neutral arbiters.”.
This baseless statement, moreover in a public document from the government, bows to woke ideologues and wants readers and the UK to chest-beating: how biased we are!
The first chapter starts with “Understanding Algorithmic bias”, which confirms the above assumption, and there is no reflection at all as to whether talking about bias makes any ontological or philosophical sense, and if so in which metaphysical systems?
Such regrettable, pathetic rhetoric leaves just any mind attached to facts, research and (critical) thinking speechless. And some scholars appear evidently having sold their eminent status and the future of algorithmic research across the UK (with, as a consequence their biases inoculating future policy-making) for, we imagine but can’t say definitely, a better place in hell.
Any receiver of such a document could expect a definition of fundamental concepts and definitions used, i.e. both ‘bias’ and ‘algorithmic bias’. Something scientific, if it is not too much to ask in 2020.
Nothing like that. And the bias of talking recurrently about a not-defined ‘bias’ becomes, unfortunately, a standard: you can’t find what a bias is but much-opinionated self-proclaimed experts will patronise you to reckon you are biased or help you tackle all these biases around you (because you might not know it yet, we are all surrounded by biases!).
Since the publication of this article, news informed that Timnit Gebru, a renowned AI expert who has written about algorithmic bias, left Google following issues related to her last article, with complex views and definitions attempts around these difficult matters. Even there, assumptions made about what a bias or algorithmic bias is, are still discussed and may surely not be any god’s word.
Please believe me, and I invite you to (re-)think this if you don’t: should you manage to get around a fairly good definition of what a bias is or can be, you will be still far away from grasping all the complexity resulting from understanding what an ‘algorithmic bias’ can be.
But even though, I want to read from any reader that has written an ‘unbiased algorithm’ or what a world without “bias” can look like.
And I know I won’t get any as it is impossible. But please, let’s get the challenge.
Yet, another initiative that unfortunately misses an occasion of addressing properly an interesting and possibly important subject to businesses and, more broadly, professionals.
The CDEI’s review author, contacted twice on LinkedIn about providing a definition of ‘bias’ didn’t reply at the moment we publish this article (6 Dec 2020). We will update this section should anything change but it is very unlikely to happen.
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