HomeNewsOpinionThe Future is Equal: Strategies to close the gender gap in AI

The Future is Equal: Strategies to close the gender gap in AI

India leads globally in women STEM graduates but faces barriers in workforce participation, leadership roles, and academia. To advance AI, diversity is essential. Addressing biases, work-life balance, and support initiatives is crucial for inclusivity

April 15, 2025 / 09:01 IST
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artificial intelligence
Generative AI technology is still at its nascent stage. There is a huge demand for professionals in AI research, AI engineering, and AI Product managers, who can bridge the gap between technology and business objectives.

By Lipika Dey 

During a visit to Glasgow University, about a decade back, a female faculty member from the university confided in me that the western society was still not favourable to girls studying science. I was happy to share that it was not so in India! According to data published by Ministry of Education in 2022, India still leads the globe with women accounting for 42.7% of STEM graduates. However, women account for only 27% of the STEM workforce in India, against a global average of 29.2%. Further, only 7 – 10% of leadership roles in tech industry are held by women. In academia, only 16.6 % of STEM faculty across 100 top universities of the country are women. The leaky pipeline does not definitely augur well for India’s AI mission that promotes ethical AI practices. Team diversity is the foundational principle for building systems that are fair, trustable and accountable.

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The Ethical AI challenges

Along with the euphoria, generative AI applications have also been accused of bias and misrepresentation. Systematic explorations by researchers of MIT Media Lab, revealed biases in face recognition systems, that worked fine for light-skinned men, but failed to recognize dark-skinned women. The error could be traced to underrepresented samples in the training and test data. These systems, if deployed commercially, could lead to denial of services for the underrepresented categories. Omissions like the above can be extremely damaging to the society. An AI system trained with biased data can amplify digital divide by echoing the bias. That men and women perceive the world, think and act differently is well established by behavioural, cognitive and brain science. Brining in cultural contexts to decision making is also important.