How AI can be used to fight gender discrimination
Diversity ambitions are being downgraded – especially after President Donald Trump scrapping DEI policies. However, AI could help governments assess the potential gender impacts of proposed laws and help prevent gender discrimination and inequality. AI is tracking gender representation in leadership roles and can encourage the use of gender quotas to address inequalities, according to Zinnya del Villar, director at Data-Pop Alliance, a think tank created by Harvard Humanitarian Initiative, MIT Connection Science and ODI Global.
“It can also assist in analysing and drafting gender-sensitive laws by identifying patterns of gender discrimination and proposing reforms”, she says in an interview with UN Women.
World Economic Forum says gender parity will take another 134 years to reach. Underrepresentation of women in sectors with higher paying jobs, including technology and infrastructure, is one of the reasons for the gender pay gap.
In 2024, women made up 28.2% of the STEM (science, technology, engineering and mathematics) workforce, but the picture is improving for AI Engineering talent, World Economic Forum says in a report.
Although men still outnumber women, over the past four years the share of female AI talent has increased significantly, the report says:
“These findings underscore the need for targeted interventions to bridge this gap and ensure equitable access to emerging technological competencies, particularly since generative AI is a fast-growing technology with the potential to enable tailored learning experiences fitting the needs of diverse learner populations.”
Del Villar stresses that artificial Intelligence mirrors the biases that are present in our society on the basis of gender, age, race, and many other factors.
“To reduce gender bias in AI, it’s crucial that the data used to train AI systems is diverse and represents all genders, races, and communities,” she says.
“This means actively selecting data that reflects different social backgrounds, cultures and roles, while removing historical biases, such as those that associate specific jobs or traits with one gender.”
“Additionally, AI systems should be created by diverse development teams made up of people from different genders, races, and cultural backgrounds. This helps bring different perspectives into the process and reduces blind spots that can lead to biased AI systems.”
UN Women says that although AI-generated data carries risks of gender bias, it also holds significant potential for identifying and addressing gender inequalities across sectors.
As an example, the organisation mentions that AI has helped analyse large amounts of data to find gender pay gaps in the workforce, with tools like Glassdoor showing differences in salaries based on gender.
“While technology-facilitated violence against women and girls online and offline is a growing concern, there are many promising advancements in AI offering innovative solutions to address digital abuse and protect survivors.”
Examples mentioned are mobile apps like bSafe provides safety alerts to protect women, Canada-based Botler.ai that helps victims understand if sexual harassment incidents they experienced violate the US criminal code or Canadian law.
Chatbots like “Sophia” by Spring ACT and “rAInbow” by AI for Good provide anonymous support and connect survivors with legal services and other resources.
“AI-powered algorithms can also be used to make the digital space safe for everyone by detecting and removing harmful, discriminatory content and by stopping the spread of non-consensual intimate images,” adds Villar.
AI systems, learning from data filled with stereotypes, often reflect and reinforce gender biases,” says Zinnya del Villar.
“These biases can limit opportunities and diversity, especially in areas like decision-making, hiring, loan approvals, and legal judgments.”
“In critical areas like healthcare, AI may focus more on male symptoms, leading to misdiagnosis or inadequate treatment for women”.
del Villar is collaborating with UN Women on researching the gendered impacts of the war in Ukraine and on enhancing gender data literacy in the region, including through the Making Every Woman and Girl Count programme.
Her recommendations to make AI inclusive:
- Using diverse and representative data sets to train AI systems
- Improving the transparency of algorithms in AI systems
- Making sure AI development and research teams are diverse and inclusive to avoid blind spots
- Adopting strong ethical frameworks for AI systems
- Integrating gender-responsive policies in developing AI systems
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