Mind the gap: Securing algorithmic explainability for credit decisions beyond the UK GDPR

The recent amendments to the United Kingdom’s GDPR under the Data (Use and Access) Act 2025 marks a significant divergence from the European Union’s approach to automated decision-making, substantively weakening the ‘right to explanation’ for automated decisions. This paper provides a critical legal analysis of the new regime, arguing that it dismantles crucial protections for individuals. The principal finding is that the legislation creates significant legal lacunas by introducing an ambiguous ‘no meaningful human involvement’ standard and restricting key safeguards to decisions involving ‘special category data’. These changes allow firms to shield opaque models from scrutiny, increasing the risk of algorithmic discrimination, particularly in high-stakes sectors like consumer credit. Drawing on a comparative review of the United States’ technology-neutral adverse action notice requirement, the paper concludes that data protection law is no longer a sufficient safeguard against algorithmic harm in the United Kingdom. It proposes the establishment of a new right to an explanation for any adverse credit decision. This right should be anchored not in data protection law, but in consumer protection law, and be enforced by a specialist regulator, the Financial Conduct Authority. Such a framework would close the new accountability gaps and create market incentives for developing transparent, explainable-by-design systems, better aligning technological innovation with consumer protection.

Institutions

Publication Details

Journal
Computer law & security review
Published
2025-12-03
DOI
https://doi.org/10.1016/j.clsr.2025.106247
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mind the gap: Securing algorithmic explainability for credit decisions beyond the UK GDPR

Computer law & security review
Ethics and Social Impacts of AI
article

Mind the gap: Securing algorithmic explainability for credit decisions beyond the UK GDPR

article en

Abstract

The recent amendments to the United Kingdom’s GDPR under the Data (Use and Access) Act 2025 marks a significant divergence from the European Union’s approach to automated decision-making, substantively weakening the ‘right to explanation’ for automated decisions. This paper provides a critical legal analysis of the new regime, arguing that it dismantles crucial protections for individuals. The principal finding is that the legislation creates significant legal lacunas by introducing an ambiguous ‘no meaningful human involvement’ standard and restricting key safeguards to decisions involving ‘special category data’. These changes allow firms to shield opaque models from scrutiny, increasing the risk of algorithmic discrimination, particularly in high-stakes sectors like consumer credit. Drawing on a comparative review of the United States’ technology-neutral adverse action notice requirement, the paper concludes that data protection law is no longer a sufficient safeguard against algorithmic harm in the United Kingdom. It proposes the establishment of a new right to an explanation for any adverse credit decision. This right should be anchored not in data protection law, but in consumer protection law, and be enforced by a specialist regulator, the Financial Conduct Authority. Such a framework would close the new accountability gaps and create market incentives for developing transparent, explainable-by-design systems, better aligning technological innovation with consumer protection.

Computer law & security reviewVol. 60
University of Cambridge (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 37%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.