Closing the AI gender gap in retirement and healthcare

Retirement living and care is a predominately female sector, in terms of its residents, workforce and leadership. In this context, gender bias in AI is not a theoretical concern but a practical one, engaging the legal, ethical and operational responsibilities of organisations providing care. Careful attention is needed to ensure that the design, selection and use of AI tools support fair outcomes and comply with applicable regulatory obligations.

Gender bias and the data gap in AI

Gender bias in AI describes systematic outputs that disadvantage people based on gender. It’s rarely intentional, instead arising because AI systems are trained on data shaped by historic inequality and incomplete representation.

A fundamental challenge is the gender data gap in healthcare. Clinical research has historically focused on male bodies, frequently excluding women from trials or treating male physiology as the default. As a result, many datasets lack sex‑disaggregated information about disease presentation, symptom progression and treatment efficacy in women. AI systems trained on this data inevitably inherit these limitations.

Generative AI compounds this problem. Large language models are trained on vast quantities of publicly available internet data, much of which reflects embedded cultural stereotypes. Research shows these systems can reproduce assumptions about women’s roles, credibility and needs, which may then influence care‑related decisions when deployed in practice.

Development in male-dominated environments

Bias isn’t only a data issue; it’s also a design issue. The AI sector remains male‑dominated, particularly in senior technical and leadership roles. This matters because early design decisions shape what problems are prioritised, what variables are included, and what risks are deemed acceptable.

A lack of diverse perspectives can result in blind spots. For example, if development teams are unfamiliar with gendered differences in pain reporting, ageing, or caring patterns, those differences may not be recognised as clinically or operationally significant. As a result, AI systems can appear neutral while performing unevenly in practice.

Why this is important in retirement living and care

Retirement living and care occupy a unique position at the intersection of healthcare, ageing and gender. Women live longer on average, are more likely to reside in retirement communities or care homes and make up most of the care workforce. Any bias embedded in AI tools therefore disproportionately affects women.

AI is already used in this sector in tangible ways. If the systems are trained on biased or incomplete data, the consequences may include under‑recognition of women’s pain, mis-prioritisation of care needs, or framing women’s health issues as less severe.

A recent LSE study found that Google’s AI model ‘Gemma’, used to summarise social care records, produced markedly different outputs depending solely on the gender identifier. Identical case notes describing an older person living alone with mobility issues were summarised as indicating “complex” needs when framed as male, but resilience and independence when framed as female. In a care context, such differences could influence assessments of need, funding decisions, or staffing ratios.

AI as a risk amplifier

AI systems don’t exercise judgment. They detect patterns and reproduce them at scale. Where underlying data reflects inequality, AI can act as a risk amplifier, embedding bias into routine processes that shape care delivery.

This is particularly concerning in healthcare, where similar dynamics have already been identified. AI‑driven diagnostic tools may perform less accurately for women if trained primarily on male datasets. Precision‑medicine models used to inform dosing recommendations have been shown to produce less reliable outputs for women when based on pharmacokinetic data derived largely from male participants. This demonstrates potential implications for safety and efficacy.

These are not hypothetical risks. They underscore why AI governance requires active management rather than passive adoption.

Legal and Regulatory considerations

The legal framework in England doesn’t yet include any standalone AI legislation. Instead, existing regimes apply to AI according to function and context.

In retirement living and care, key considerations include:

• Medical device regulation: Many AI tools used for diagnosis, monitoring or treatment qualify as software (or AI) as a medical device and are regulated by the MHRA.
• Data protection: AI often involves extensive processing of sensitive personal data. Robust compliance with UK GDPR is essential, including transparency, lawful basis, data minimisation and the completion of data protection impact assessments.
• Liability and duty of care: AI systems are not legal persons. Responsibility for decisions informed by AI remains with providers and professionals, raising complex questions where harm occurs.
• Equality law: The Equality Act 2010 creates risk where AI systems produce discriminatory outcomes, including indirect discrimination.
• Human rights law: In the so-called ‘Swiss grannies’ case, the European Court of Human Rights found that Switzerland’s climate action was insufficient to protect the human rights of an association that demonstrated that the impact of climate change disproportionately affects older women. The case has already prompted similar legal action in the UK. It’s not a stretch to anticipate the same human rights arguments in the context of AI in care.

Recognising these challenges, the MHRA established the National Commission into the Regulation of AI in Healthcare in 2025, with a dedicated regulatory framework expected in 2026. This reflects the growing acknowledgment that AI requires sector-specific governance to address safety, bias, and trust.

Practical steps for organisations

Organisations operating in retirement living and care can take pragmatic steps now by:

• Scrutinising AI tools before procurement: Seek clarity on data sources, sex‑disaggregation and bias testing.
• Avoiding opaque systems: Use explainable AI, particularly where outputs affect care decisions.
• Maintaining human oversight: AI should support, not replace, professional judgment, with clear accountability.
• Training staff appropriately: Ensure users understand AI outputs can be questioned and are not inherently objective.
• Monitoring real‑world performance: Review systems post‑deployment to identify any unintended gendered impacts.

Addressing bias at source

Mitigating gender bias in AI ultimately requires action across the full lifecycle of development and deployment. This includes improving data quality, collecting sex‑disaggregated healthcare data, and embedding equality considerations into clinical research and digital health systems.

It also requires broader representation of women in AI development teams, governance frameworks and procurement decisions. Bias can’t be corrected solely through technical fixes; it must be addressed through cultural and structural change.

Recent UK investment in AI research, including the creation of a national AI research lab, signals an intention to rethink how AI systems are built rather than simply scaling existing models. Whether these initiatives succeed in narrowing the gender gap will depend on the extent to which equality and inclusion are treated as core design requirements, rather than peripheral concerns.

A Governance issue, not a technology problem

For retirement living, AI shouldn’t be framed as either a solution or a threat. It is a tool whose impact will be shaped by the choices made now about data, governance, accountability and representation.

A sector grounded in care, dignity and community has both an opportunity and a responsibility to ensure that AI supports, rather than undermines, equitable outcomes. Closing the gender gap in AI isn’t just a technological challenge; it’s a matter of good governance and lawful, ethical care.