Closing the AI gender gap: Legal, ethical and practical considerations

One of the most significant challenges is gender bias. AI outputs are shaped by the data on which they are trained, the assumptions embedded in their design and the decisions made throughout their development and deployment. As AI becomes increasingly influential in business and public life, organisations need to consider not only what AI can do, but also whether it does so fairly.

Gender bias and the data gap in AI

Gender bias in AI refers to systematic outputs that disadvantage individuals based on gender. In most cases, this bias isn’t intentional. Instead, it emerges because AI systems learn from historical datasets and patterns that may already contain unequal treatment, underrepresentation or societal stereotypes.

Across many fields, data has historically been collected, analysed and interpreted in ways that fail to fully capture the experiences, behaviours and needs of women or other under-represented persons, groups, cultures or communities. Where datasets are incomplete or unbalanced, AI systems may inherit and reinforce those limitations.

Generative AI presents additional challenges in the gender context. Large language models are trained using vast quantities of publicly available content drawn from the internet, including material that reflects cultural assumptions and biases. Research has shown that such systems can reproduce gender stereotypes relating to leadership, competence, professional roles and decision-making authority. Without appropriate safeguards, these assumptions can influence AI-generated outputs and recommendations.

Development in male-dominated environments

Bias is not only a data problem, it’s also a design problem.

Despite progress, women remain underrepresented in many areas of the technology sector, particularly in senior technical, engineering and leadership positions. This matters because the people designing systems make decisions (whether consciously or not) about what problems are prioritised, which variables are considered important and what risks are acceptable.

A lack of diversity during development can create blind spots. Issues that affect particular groups may be overlooked and assumptions that appear reasonable to developers may not reflect the experiences of the broader population. As a result, AI systems can appear neutral while producing uneven outcomes in practice.

Why this matters across sectors

The impact of gender bias in AI extends far beyond any single industry.

Organisations are increasingly deploying AI in areas such as recruitment, performance assessment, customer service, marketing, lending, insurance, risk management, procurement and business operations. AI systems may unintentionally favour certain groups while disadvantaging others.

For example, recruitment algorithms trained on historical hiring data may reflect past hiring patterns rather than objective assessments of talent. Customer-facing AI systems may respond differently to individuals based on gendered assumptions embedded within training data. Predictive systems used to support business decisions may produce outcomes that disproportionately affect particular groups, even where no discriminatory intent exists.

The challenge is often the accumulation of small, possibly unconscious, biases that become embedded and repeated at scale.

AI as a risk amplifier

AI systems don’t exercise independent judgement. They identify patterns and generate outputs based on the data and parameters provided to them.

This means AI can act as a risk amplifier. The apparent objectivity of technology can make these outcomes harder to identify, challenge or correct.

As organisations increase their reliance on AI, biased outputs can create legal, operational, reputational and commercial risks. Issues that might once have affected only individual decisions can become systemic when automated processes are applied across an organisation.

AI governance therefore requires active oversight and not just passive adoption.

Legal and regulatory considerations

The legal landscape for AI continues to evolve. In many jurisdictions there’s no standalone ‘AI law’ that comprehensively regulates all uses of AI. Instead, organisations must navigate existing legal frameworks depending on how AI is used.

Key considerations include:

• Data protection: AI systems frequently process personal data. Compliance with UK GDPR and other applicable privacy laws remains essential, including transparency, lawful processing and appropriate governance.
• Equality and discrimination law: Organisations may face legal risks where AI systems create discriminatory outcomes, whether directly or indirectly.
• Consumer protection: AI-generated outputs that affect customers may engage consumer rights and fair treatment obligations.
• Employment law: The use of AI in recruitment, workforce management and performance assessment raises important fairness and transparency considerations.
• Liability and accountability: AI tools are not legal persons. Responsibility for decisions influenced by AI continues to rest with the organisations and individuals deploying them, but those persons, and where liability falls, aren’t always easy to identify.

Regulators globally are increasingly scrutinising AI-related risks, particularly concerning transparency, fairness, safety and accountability. Organisations should expect regulatory expectations to continue developing as AI adoption expands.

Practical steps

Organisations can take various practical steps now to reduce the risk of gender bias in AI:

• Scrutinise AI systems before procurement or deployment, including understanding training data, testing methods and known limitations.
• Seek evidence of bias testing and ongoing monitoring.
• Prefer explainable systems where AI outputs may influence significant decisions.
• Maintain meaningful, proactive human oversight and clear accountability structures.
• Train employees to understand that AI outputs should be assessed critically rather than accepted automatically.
• Regularly monitor real-world performance to identify unintended disparities or discriminatory outcomes.
• Establish governance frameworks that assign responsibility for AI risk management and oversight.
• More generally, consider other areas of potential bias, beyond gender, to identify and mitigate potential AI bias.

Addressing bias at source

Addressing gender bias requires action throughout the entire AI lifecycle.

This includes improving data quality, increasing representation within datasets, strengthening testing processes and integrating equality considerations into system design from the outset. It also requires broader diversity within development teams, governance bodies and decision-making processes.

Technical solutions alone are unlikely to eliminate bias. Sustainable progress depends on organisational culture, leadership commitment and a willingness to challenge assumptions embedded in both data and decision-making processes.

Organisations can help effect meaningful change through their purchasing and contracting decisions.

How we can help businesses combat AI bias

As organisations increasingly adopt AI tools across their operations, managing the risks associated with bias requires a legal, technical and governance expertise. We support clients throughout the AI lifecycle, from procurement and implementation, to ongoing governance and regulatory compliance. The firm’s Technology & Digital team advises on AI-related contractual, intellectual property, data protection, regulatory and compliance issues, helping organisations use AI confidently while managing legal and commercial risk.

Specifically, we can help clients by:

• Developing AI governance frameworks, including policies, accountability structures and risk management processes designed to support responsible AI adoption.
• Reviewing AI procurement and supplier arrangements, ensuring contracts address issues such as bias testing, transparency, liability, audit rights and regulatory compliance.
• Advising on data protection and privacy obligations, including UK GDPR compliance, data governance and impact assessments where AI systems process personal data.
• Assessing equality and discrimination risks, helping organisations identify and mitigate the potential for biased outcomes in AI-assisted decision-making.
• Supporting regulatory compliance, including preparedness for evolving UK and international AI regulation and sector-specific requirements.
• Providing training for boards, senior leaders and operational teams on AI risk, governance and responsible use.
• Advising on liability and dispute risk, including the allocation of responsibility between organisations, suppliers and users where AI-related issues arise.

By combining expertise across technology, data protection, regulatory compliance, commercial contracts, employment and dispute resolution, we can help organisations move beyond viewing AI bias as a technical issue and address it as part of a broader growth, governance and risk management strategy.

Please contact Ryan Doodson for more information.