The side-effects of machine-learning within the walled gardens of online platforms are problematic enough, but they become positively pathological when the technology is used in the offline world by companies, government, local authorities, police forces, health services and other public bodies to make decisions that affect the lives of citizens.
It was deterministic in the sense that it did only one thing, and the logic that it implemented – and the kinds of output it would produce – could be understood and predicted by any competent technical expert who was allowed to inspect the code. (In that context, it’s interesting that the Royal Statistical Society offered to help with the algorithm but withdrew because it regarded the non-disclosure agreement it would have had to sign as unduly restrictive.
The nature of algorithms is changing, for one thing; their penetration into everyday life has deepened; and whereas the Ofqual algorithm’s grades affected the life chances of an entire generation of young people, the impact of the dominant algorithms in our unregulated future will be felt by isolated individuals in private, making collective responses less likely.
That list has been curated by a machine-learning algorithm that has learned what has interested you in the past, and also knows how long you’ve spent during those previous viewings (using time spent as a proxy for level of interest).
This means that public protests against the personalised inhumanity of the technology are much less likely – which is why last month’s demonstrations against the output of the Ofqual algorithm could be a one-off.
In the end the question we have to ask is: why is the Gadarene rush of the tech industry (and its boosters within government) to deploy machine-learning technology – and particularly its facial-recognition capabilities – not a major public policy issue?
From viral conspiracies to exam fiascos, algorithms come with serious side effects https://t.co/FOgltFlGrH— Guardian Tech (@guardiantech) September 6, 2020
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