Research and analysis

Enabling responsible access to demographic data to make AI systems fairer

The CDEI has published a report on approaches to accessing demographic data for bias detection and mitigation.

Documents

Annex 1: Deltapoll public attitudes report

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Annex 2: Frazer Nash technical study

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Details

Over the last year, CDEI has been exploring the challenges around access to demographic data for detecting and mitigating bias in AI systems, and the potential of novel solutions to address these challenges. Organisations who use artificial intelligence (AI) systems should monitor the outcomes of these systems to ensure they are fair. However, many techniques for detecting and mitigating bias in AI systems rely on access to data about the demographic traits of service users, and many service providers struggle to access the data they need. In a period where algorithmic bias has been a major focus in academia and industry, approaches to data access have received relatively little attention, despite often being highlighted as a major constraint.

This report sets out the main barriers service providers face when seeking to collect demographic data for bias detection and mitigation, and explores two promising groups of novel approaches to addressing some of them: data intermediaries and proxies.

This report has been informed by the work that CDEI has conducted over the last year, including a landscape review, a public attitudes study commissioned from Deltapoll, a technical study commissioned from Frazer Nash, and four workshops with legal and ethical experts. We are grateful for all those who have contributed to this work.

Next steps

This report has been published alongside the announcement of CDEI’s Fairness Innovation Challenge. This will support organisations in their efforts to implement the proposed fairness principle set out in the UK government’s AI White Paper. The challenge will provide an opportunity to test new ideas for addressing AI fairness challenges in collaboration with government and regulators. We hope that it will generate innovative new approaches to addressing some of the data access challenges described in this report.

Updates to this page

Published 14 June 2023

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