Emerging Technologies

3 women in AI who are helping bridge the gender equity gap

women in AI gender bias in AI technology

We need more women in AI to address the technology's inherent gender gap. Image: Unsplash/ Christina@wocintechchat.com

Safaa Khan
Project Design and Communications Specialist, Global AI Action Alliance, World Economic Forum LLC
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Education, Gender and Work

  • Artificial intelligence (AI) is becoming increasingly mainstream across sectors and has great potential to benefit society.
  • But its full potential can only be realised if the technology represents the diversity of the populations it represents.
  • Here are three women in AI who are working to address gender equity in the technology's development.

Artificial intelligence (AI) is rapidly advancing across sectors and industries and while it has great potential to benefit society, this can only be realized if AI truly represents the diversity of the populations it represents.

Gender equity, specifically, is not currently realized in AI development. A technology meant to replicate human functions learns from and relies on the data and teams that put it together and manage it. A lack of representation from women in these spaces creates bias and can make technology untrustworthy.

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Many companies and organizations are looking to increase gender equity within their AI teams to deliver value. While there is much more work to be done, there are more women than ever in leadership roles paving the way in AI.

Here are three women working to bridge the gender equity gap when it comes to AI.

Karine Perset heads the AI Unit of the OECD Division for Digital Economy Policy. She oversees the OECD.AI Policy Observatory and OECD.AI Network of Experts (ONE AI), as well as the newly forming OECD Working Party on AI Governance (AIGO). Perset focuses on opportunities and challenges that AI raises for public policy, on policies to help implement the OECD AI Principles and on trends in AI development.

Dr Brandeis Marshall teaches, speaks, and writes about the impact of data practices on technology and society. Her work contributes to the data engineering, data science, and data/computer science education fields. Through her DataedX Group organization, she guides current tech workers in building data equity skills. Dr Marshall holds a PhD and Master of Science in Computer Science from Rensselaer Polytechnic Institute and a Bachelor of Science in Computer Science from the University of Rochester. Currently, she is a Stanford PACS Practitioner Fellow and Partner Research Fellow at Siegel Family Endowment. Dr Marshall is on sabbatical leave from Spelman College, where she is a Professor of Computer Science.

Sunita Grote leads the Ventures team within UNICEF’s Office of Innovation and co-founded its Venture Fund that provides seed funding in fiat and crypto currencies – the first in the UN system. She steers UNICEF's co-leadership of the Digital Public Goods Alliance, a network of partners who collectively contribute to the discovery, development, and deployment of open-source digital public goods. Grote has a background in innovative financing and previously spent 10 years working in the global HIV and health response, based mostly in the UK and in South Asia. She holds an MBA from INSEAD and lives in New York City with her partner and daughter.

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Why do we need more women in AI?

Karine Perset: More women participating in the development of AI and related fields would mean less bias built into data, algorithms and other AI components. Systems that judge how employees interact with customers might rank men higher if the standards are based on traits like assertiveness or confidence that men have been traditionally taught to adopt. For example, gender imbalance in the AI workforce led to gender biases in multiple AI systems, such as Amazon’s AI-based recruitment tool and Google Translate.

Current tech culture can also feel unwelcoming for people wanting to start a family by perpetuating unreasonable expectations. For example, some companies pressure new mothers to remain available while they are on maternity leave. If there were more women in the field it might help to change that dynamic.

Data available on OECD.AI shows that in 2021, the share of women credited with authoring AI scientific publications was below 20%. Again this can inadvertently lead to bias in research. The more women we have in science, technology, engineering and mathematics (STEM) and AI-related professions, the more role models we have for youth to emulate.

Dr Brandeis Marshall: Women account for about 50% of the global population, yet account for 26% of the data and AI workforce. Most AI-based products don't consider women's needs or address issues concerning women. This imbalance perpetuates disparities and inequities that support a healthy digital society.

Women bring different perspectives, avoid certain pitfalls and fill specific gaps in understanding and application. Women being in AI, contributing to AI, leading AI initiatives and pioneering critiques of AI is what will make our society better and stronger.

Sunita Grote: We need more women in AI to make better AI. AI reflects the quality of the data that is used to train it and the perspectives of those that build the system.

AI solutions and the talent that develops them both face a significant diversity challenge. The development of AI solutions is shaped despite a stark talent gap that exists across geographies and gender; as well as the data and technology gap that still leaves half of the world’s population disconnected from the internet – most of which are women and girls. In places where data is produced, it is often biased and not representative.

We need women — their voices and their needs — reflected in the processes and inputs that build AI-based systems and solutions, so that they can start better serving and responding to half of humanity’s needs and interests.

We need women – their voices and their needs – reflected in the processes that build AI systems

Sunita Grote

We have seen this bring success through solutions like Oky, an open-source mobile period tracker and menstruation education application. The app was co-created with girls, enabling the team to bridge gender digital gaps.

We also know that one way to address this bias and gap is by making sure that the designers and builders of systems are diverse. However, only about a quarter of the data and AI positions in the workforce are held by women.

Globally only 2-9% of venture capital (VC) funding goes to female-led companies, despite compelling data that investing in female-led startups shows stronger return on investment compared to predominantly male-led companies.

At the UNICEF Venture Fund, we are committed to achieving a balanced portfolio with 50% of our investments going to female-founded or -led companies. You can meet some of our female founders scaling AI-based solutions here.

Advice for women looking to enter the AI sector

Karine Perset: This is a very exciting moment in tech and AI. At first there will be fewer women, and that’s okay. There are a lot of benefits to standing out in the crowd and being a pioneer. But make sure you reach out to other women as much as possible to help reinforce each other’s strengths. And finally, remember that no man deserves to be in your position more than you, based on that one single difference.

Dr Brandeis Marshall: My advice for younger women, especially Black women, is simple: don't shrink yourself to make others comfortable, find and stay in communities that support you and always, ALWAYS, negotiate for more money.

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What's the World Economic Forum doing about the gender gap?

Sunita Grote: I don’t believe that women entering the AI space need advice – but rather that we all need to change the systems women are entering. There are still persistent biases that prevent women from entering technical studies, from being considered for jobs and investments, and from being promoted into leadership positions.

Rather than expecting women to change, adjust, or heed advice on how to overcome obstacles, we need to look at addressing the inherent biases that are creating the equity gap in the first place.

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Emerging TechnologiesEquity, Diversity and Inclusion
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