Artificial intelligence is at risk of reinforcing existing gender inequalities unless women are meaningfully involved in its design, governance, and deployment, according to new research from the Women and Work APPG.
Drawing on evidence from a year of roundtables between 2024-2025, the report shows how AI systems trained on historically biased data can replicate and scale discrimination in recruitment, progression, and the evaluation in the workplace.
The downgrading of women’s LinkedIn posts, Amazon’s withdrawn AI recruitment tool that showed bias against female candidates, and wider concerns about large language models demonstrate how AI can learn from and reinforce existing inequalities. Without diverse training data, robust guardrails, and stronger oversight, these biases risk becoming embedded at scale.
Women, who remain underrepresented in AI development yet overrepresented in roles most exposed to automation, face a dual risk of exclusion from shaping new technologies while being disproportionately affected by them.
Baroness Karen Brady, Co-Chair of Women and Work APPG commented: “The rapid acceleration of artificial intelligence and emerging technologies is reshaping the world of work. The enduring gender pay gap and the continued lack of parity within the technology sector make clear that meaningful progress remains unfinished and that urgent action is still required.”
Women were also affected by the first wave of AI-driven automation, particularly in sectors like administration, healthcare, education, and social care. At the same time, women remain significantly underrepresented in AI design, development and leadership, increasing the risk of biased systems and unequal outcomes.
Linda Benjamin, VP from AND Digital commented: “AI is shaped by the data it’s built on, the questions it’s asked, and the people who design it. When historical data implicitly reflects gender imbalances, pay disparities, or other systemic biases, AI can learn and replicate those patterns, amplifying inequality at speed and scale”
“Improving outcomes for women in the age of AI therefore starts upstream, with more representative data, rigorous auditing for bias, and inclusive teams designing and testing these technologies. We also need practical action to support women to participate fully in this transformation, clear pathways into AI and digital careers, accessible reskilling, stronger encouragement for girls to study STEM subjects, and workplace policies such as affordable childcare and flexible training to provide women with the opportunities to define the future of work.”
Older women, particularly those aged over 55, are frequently excluded from digital skills training, increasing their vulnerability to redundancy as workplaces adopt AI processes. Public sector reforms fail to deliver against this challenge with the absence of AI considerations in upcoming gender pay gap action plans, and the risk of AI-powered productivity monitoring creating punitive workplace cultures.
Sheila Flavell CBE, COO of FDM Group commented: “Upskilling and reskilling women in digital skills must be a priority. From supporting women through early education to providing clear pathways into technical and leadership roles, businesses and government need to work together to invest in training that equips women with in-demand digital and AI skills. This should also include dedicated pathways for women returners looking to re-enter the workforce after a career break, ensuring experienced talent is not lost to the tech sector.”
Despite evidence that female-led businesses deliver strong returns, all-female founding teams received just 1.8% of UK venture capital in early 2024, and women account for only 15% of investment committee members. High childcare costs and limited access to tailored financial support further constrain women’s ability to start and scale businesses.
Many female entrepreneurs underpay themselves or forgo benefits such as maternity pay, highlighting gaps in current entrepreneurial support frameworks and the need for more integrated childcare and financial safety nets.
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