Jurisdiction(s):
United Kingdom

Employment AI safeguards compared: why local explainability, not human oversight, is key to fair, contestable and defensible decisions

Published on: 29 January 2024

Published by a LexisNexis Employment expert

Legal News

Article summary

We contend that, within employment, person-specific explainability is essential. Our review of the main types of AI safeguards, alongside a case study on how local explainability operates in reality—see: here, shows that such a measure can both build confidence in AI and harness its capacity to detect and mitigate bias and discrimination. In employment settings, such personalised transparency is central to automated decision-making in this context.

The evolution of AI regulation

AI’s march into workplace decision-making has been checked by worries about safety and, notably, the risk of bias and discrimination. Used well, AI can enable swifter, more efficient and even higher-quality determinations. Yet it can also entrench bias and discrimination within decisions—see: Discrimination and bias in AI recruitment—a case study. Governments and policymakers worldwide are examining a range of safeguards to bolster AI safety. Existing and draft frameworks tend to favour different tools aimed at shielding individuals. Striking the right balance between, on the one hand, spurring innovation and remaining an attractive base for business and, on the other, fostering trust in automated choices while securing suitable protections for individuals, is challenging. The real challenge is calibrating safeguards so useful innovation is not curtailed.

What about existing regulation?

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