Fast, Flawed, and Risky: How AI ‘Efficiency’ Nearly Undermined Fairness in Social Care Decision

Recent reporting has highlighted a significant risk in the use of AI tools by English local authorities, particularly where generative AI is being used to summarise case notes or draft care plans.

Investigations found councils experimenting with unregulated AI systems to speed up assessments and reporting, often without formal governance, bias testing or transparency about how outputs are generated. While framed as productivity tools, these systems are being used in high-stakes environments where nuance, professional judgement and safeguarding are critical.

A key concern is systematic bias and distortion of need. Academic research cited in the reporting showed that when identical social care case notes were entered into AI tools with only gender changed, the outputs consistently downplayed women’s health and care needs compared with men’s. This creates a real risk that AI-generated summaries could influence thresholds, eligibility decisions or care packages in ways that are structurally unequal, even where practitioners believe the tool is neutral. The issue is not malicious intent, but over-trust in outputs that sound authoritative and concise.

For commissioners, this represents a risk, and a near-miss. AI is already influencing practice, often informally, ahead of policy, regulation or commissioning frameworks catching up. The lesson is clear: AI cannot be treated as a benign efficiency gain. Any use in commissioning or care pathways needs explicit boundaries, bias testing, audit trails, and human override, particularly where it may shape demand, prioritisation or resource allocation.

credit: The Guardian – Warning over use in UK of unregulated AI chatbots to create social care plans