Beyond bias: shaping AI for a fairer future for women

The potential of AI to revolutionise industries is enormous, but so is its potential to amplify societal biases and reinforce stereotypes, particularly those related to gender. They learn from vast datasets, but these datasets often reflect historical inequalities.

In finance, proprietary algorithms may assign lower credit limits to women with identical financial profiles as men, perpetuating the gender credit gap. In healthcare, AI tools have sometimes failed to diagnose women-specific conditions accurately, owing to limited gender-disaggregated health data. Recruitment algorithms can inadvertently disadvantage female candidates, reinforcing stereotypes about roles traditionally associated with women and men.

However, there is a silver lining. Algorithmic discrimination, while problematic, is often easier to detect and correct than human bias. But better data quality, greater transparency and public scrutiny are needed to make the proper adjustments and minimise historical biases. Much still needs to be done before we can broadly address these biases with effective human oversight.

Financial inclusion: AI’s potential to correct gender imbalances 

Financial inclusion illustrates clearly the dual potential of the benefits and challenges of AI. On the positive side, AI can provide scale, automatise repetitive tasks and reduce financial intermediation costs. By automating processes such as customer onboarding and fraud detection, financial institutions can reduce operational expenses. This will enable them to issue more small loans and onboard more clients at lower costs than was previously possible.

Yet, challenges remain. To begin, we have to overcome the gender bias in data. Many AI tools lack robust training data. These datasets first need to be created by preferably diverse and skilled workers who understand the specific biases affecting distinct population groups that have, so far, been left behind

Moreover, financial inclusion goes beyond providing access to the financial system. Women face unique barriers in utilising technology for financial services. Tailoring products to gender preferences, in accordance with client protection principles, is essential. Gender differences in willingness to adopt new technologies account for much of the gender gap in digital finance.

Labour market: growing women’s resilience with AI

AI-driven automation is rapidly reshaping labour markets at different levels. It is replacing routine tasks, resulting in workers’ lay-offs in sectors where automation can work and widening wage gaps across companies. AI-integration will favour large companies that have the capital to invest in advanced AI systems, risking greater inequality.

The potential employment effects, through automating, vary widely across country income groups, due to different occupational structures. In low-income countries, only 0.4 per cent of total employment is potentially exposed to automation effects, whereas in high-income countries the share rises to 5.5 per cent. These effects are highly gendered, with more than double the share of women potentially affected by automation. Women are overrepresented in vulnerable sectors, such as administration, customer service, basic accounting, which could be partly or fully done by AI.

To mitigate these effects, considering that women are less prone to use AI, companies should focus on investing in adult learning and upskilling women to make them feel more comfortable with AI. That way, women can collaborate with AI instead of being replaced. If replacement occurs, unemployment protection and retraining must be provided to make women feel more confident in working with AI. Inclusive AI frameworks require comprehensive policies that address gender sensitivity, rather than implementing isolated policies that are gender-neutral.

Building AI systems that work for women

The path to equitable AI lies in embedding diversity, adapting education and skills to address its tools critically, and having proper governance and regulation fit for the purpose.

• Embedding diversity in education and skills A population better educated on AI, empowers individuals to design, use and demand fairer systems, while keeping human oversight central to decision-making. Since limited knowledge about AI emerges as the most important driver of the AI gender gap, educating women to design and use AI is critical.  Education not only means increasing the number of women students that follow Science, Technology, Engineering, and Mathematics studies (STEM). In the past few years women that have followed these studies are struggling to stay in the workforce. Workplace culture has been a far more important incentive to support entry, retention and advancement, broadening the diverse representation needed in AI. Companies that are sensitive to these issues will create AI systems that are better in creating less biased feedback loops.
• Diversity in developer hiring Tech-related companies are increasing their efforts to hire for diversity, to broaden the range of perspectives among developers. This to reduce blind spots and produce more balanced outcomes. Women’s perspectives are essential to the creation of AI-powered services that address hiring biases, health disparities and other imbalances. Yet, women currently comprise less than a third of AI professionals and only 18% of AI researchers globally. This is a stark disparity that must be addressed through targeted recruitment, mentorship and capacity-building programmes.
• Governance and regulation At the basis, legal frameworks must uphold human rights, the rule of law and non-discrimination. International treaties and national legislation can promote diverse workforces, provide remedies for biased outcomes and support capacity-building initiatives. More specifically, sustainable adoption of AI necessitates an ecosystem of intentionally designed principles, guidelines and practices, collectively referred to as “responsible AI”, to effectively govern societal expectations with institutional support. There are numerous international and national initiatives that are serving this purpose. Most recommend continuous feedback from society and agile governance to help ensure that AI systems remain socially responsible.

AI can be a powerful tool for social progress, as well as productivity. The challenge is to ensure that AI does not simply reproduce the status quo, but actively works to dismantle gendered barriers and create more inclusive opportunities. Ensuring women's involvement in AI development, along with effective oversight, is crucial for both mitigating risks and unlocking new possibilities. Developing targeted strategies to prevent AI from exacerbating gender inequalities will take time, but coordinated international efforts can help avoid disjointed solutions and accelerate advancement toward equity.