“When Adam delved and Eve span, Who was then the gentleman?”
In 2018, Amazon pulled the plug on its AI-based recruitment tool for bias against female candidates. The tool was trained on a dataset containing resumes in the experience range of up to 10 years. No prizes for guessing, most of the resumes were from men.
The incident is a telling example of how poor representation leads to far-reaching consequences. The gender gap in science and emerging technologies such as AI shows no sign of closing.
Gender imbalance is a serious problem in AI. World Economic Forum data suggests women account for only 22 percent of the global AI jobs. While the jobs in AI have increased over the past few years, women participation still remains dismally low.
Fair women participation is necessary for organisations to accelerate their AI maturity, and avert some of the gravest problems the industry is facing right now, especially the selection bias.
A case for inclusion
AI systems faithfully reflect the intents and biases of the people who are developing them. In other words, the tech is as good and fair as its developer. Ipso facto, the lack of women in AI has eroded the quality of the product (less inclusive) and turned away potential customers.
Google chief scientist of AI and machine learning, Fei-Fei Li, said, “If we don’t get women and people of colour at the table — real technologists doing the real work — we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible. This is the time to get women and diverse voices in so that we build it properly, right? And it can be great. It’s going to be ubiquitous. It’s going to be awesome. But we have to have people at the table.”
UNESCO’s 2019 report, I’d Blush if I Could, and several other studies have shown that gender biases found in the AI training data sets and algorithms can perpetuate harmful gender stereotypes leading to further marginalization of women. “Women make up a fraction of the artificial intelligence workforce. We need diversity of thought and creativity in AI to realise its boundless potential and lead different ideas, innovations, and outcomes. Different perspectives are essential, and so are the different lived experiences, priorities, and worldviews to have an intersectional approach to AI programming,” said Megha Gambhir, CEO, Stupa Sports Analytics.
“AI is the most pervasive technology today, and it’s defining the rules of how the world will run. Imagine the consequence of having perspectives and outcomes that only represent half of humanity! This is the significance of women to AI enterprise maturity levels. Suppose companies want to eliminate serious business issues related to ML techniques, such as selection bias. In that case, women must be mobilised on a large scale and given an equal seat in all artificial intelligence endeavours. There’s a long way to go. And we all need to double down to make sure AI teams are diverse, with women at the epicentre,” said Madhurima Agarwal, Director – Engineering Programs & Leader – NetApp Excellerator.
What can be done
- Equal pay and growth opportunities for women will encourage many qualified and experienced women to enter the domain
- Greater awareness of roles and opportunities is a deciding factor for most women seeking to join this field.
- Greater access to domain-specific education and knowledge-building opportunities
- Mentoring support right from the school and college and up to workplaces can put a lot of their anxieties and doubts about this field to rest.
- Seeing one of their kind in leadership roles would also positively impact the participation of women.
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I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my heart’s content.