Women Are Avoiding AI. Will Their Careers Suffer? | Working Knowledge – Baker Library

This post was originally published on this site.

As more businesses seek to harness artificial intelligence, bolstered by studies showing its potential to boost worker productivity, new research reveals that women are significantly more reluctant to use the technology than men.

“There is always a stark gender disparity hiding in the back of these papers,” says Harvard Business School Associate Professor Rembrand Koning, who has also noticed that fewer women use the generative AI tools that he and his colleagues at the Digital Data Design Institute at Harvard have created for entrepreneurs around the world.

In fact, Koning’s research shows women are adopting AI tools at a 25 percent lower rate than men on average “despite the fact that it seems the benefits of AI would apply equally to men and women,” he says.

Why? In many cases, the research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them, Koning says in a recent working paper, “Global Evidence on Gender Gaps and Generative AI.” Koning coauthored the paper with Nicholas G. Otis and Solène Delecourt of the University of California, Berkeley, and Katelyn Cranney of Stanford University.

Businesses could miss out on major productivity gains if women continue to shun generative AI, and women might fall behind in building valuable skills they need to succeed. That could widen the persistent gender gap in wages and career opportunities, Koning says.

“It’s important to create an environment in which everybody feels they can participate and try these tools and won’t be judged for [using them],” he says.

Women consistently show lower adoption rates

To compare how women and men use generative AI tools like ChatGPT and Claude, the researchers examined 18 studies involving more than 140,000 college students and workers, including business owners, data analysts, software developers, and executives from countries including the United States, Sweden, Mexico, China, and Morocco. A 2024 survey by the Federal Reserve Bank of New York, for example, found that half of men used generative AI in the previous 12 months, compared with about a third of women.

Across most of the studies, the share of women adopting AI tools was 10 to 40 percent smaller than the share of men.

“When we aggregate them, our best estimate is that there is a 25 percent gap,” says Koning, the Mary V. and Mark A. Stevens Associate Professor of Business Administration.

Only one study—a Boston Consulting Group survey of San Francisco-area tech workers—found that women were 3 percent more likely to use AI than men.

“Women in tech may have had more exposure to these tools and are more comfortable using them,” Koning explains.

In addition, the researchers studied AI users by gender and found that between November 2022 and May 2024, women made up only 42 percent of the 200 million average monthly users of ChatGPT’s website. In terms of smartphone app usage, the gender gap was even larger, with 27 percent of total ChatGPT application downloads coming from women.

Women express concerns about AI

To explore whether women were more likely to avoid AI tools because they were less familiar with them than men, Koning and his colleagues also surveyed some 17,000 male and female entrepreneurs in Kenya, inviting them to use ChatGPT and providing information about how to use the technology. They still found the gender gap persisted, with women about 13 percent less likely to try the tool.

The results show that access alone may not be enough to close the gap. “Even when the opportunity to use ChatGPT was equalized, women were less likely to engage with the tool, which we think is pretty shocking,” Koning says.

Based on conversations with managers and other studies of work and gender, Koning says in some cases, women appear to be worried about the potential costs of relying on computer-generated information, particularly if it’s perceived as unethical or “cheating.”

“Women face greater penalties in being judged as not having expertise in different fields,” Koning says. “They might be worried that someone would think even though they got the answer right, they ‘cheated’ by using ChatGPT.”

Why gender matters in AI use

Koning says if the AI gender gap persists, it could have three major ramifications:

Women may struggle to advance in their careers. If female workers aren’t using a technology that increases productivity, they risk falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities.

Businesses and the economy might lose potential growth. Productivity as a whole could suffer, undermining the economy. “These gaps are bad for women because they’re not being as productive as they could be,” Koning says, “but they’re also bad for the economy because we’re losing out on economic growth we could have had.”

AI could miss out on input from women. The large language models that underpin generative AI improve as they gain new information, not only from data sources but also from users’ prompts. A lack of input from women could result in AI systems that reinforce gender stereotypes and ignore the inequities women face in everything from pay to childcare.

“If it is learning predominantly from men, does that cause these tools to potentially respond differently or be biased in ways that could have long-term effects?” Koning asks.

How to encourage widespread use

Koning recommends that companies go beyond providing equal access to generative AI and make a concerted effort to invite all employees to experiment with the tools.

“I’d love to see trainings around AI that everyone has to go through in order to make them comfortable,” Koning says. Drawing from research on psychological safety by his colleague Amy Edmondson, the Novartis Professor of Leadership and Management, Koning urges companies to create a culture in which using AI is not only normalized but encouraged.

“In talking with companies about using generative AI, places where it seems gaps are the smallest are those where leaders are saying, ‘We want everyone to adopt these tools. Some stuff’s going to work and some stuff’s not, and that’s OK,’” he says. “If you do that, you are going to go a long way toward closing the gap in adoption.”

Image created with assets from AdobeStock.