AI is the future
For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women.
That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017. It suggests the group supposedly charting society's future is even less inclusive than the broader tech industry, which has its own well-known diversity problems.
At Google, 21 percent of technical roles are filled by women, according to company figures released in June. When WIRED reviewed Google's AI research pages earlier this month, they listed 641 people working on "machine intelligence," of whom only 10 percent were women. Facebook said last month that 22 percent of its technical workers are women. Pages for the company's AI research group listed 115 people earlier this month, of whom 15 percent were women.
A Google spokesperson told WIRED that the company's research page lists only people who have authored research papers, not everyone who implements or researches AI technology, but declined to provide more information. Facebook also declined to provide details on the diversity of its AI teams. Joelle Pineau, who leads the Montreal branch of Facebook's AI lab, said counting the research team's publicly listed staff was "reasonable," but that the group is small relative to everyone at Facebook involved in AI, and growing and changing through hiring.
Pineau is part of a faction in AI research trying to improve the field's diversity-motivated in part by fears that failing to do so increases the chance AI systems have harmful effects on the world. "We have more of a scientific responsibility to act than other fields because we're developing technology that affects a large proportion of the population," Pineau says.
Companies and governments are betting on AI because of its potential to let computers make decisions and take action in the world, in areas such as health care and policing. Facebook is counting on machine learning to help it fight fake news in places with very different demographics to its AI research lab, such as Myanmar, where rumors on the company's platform led to violence. Anima Anand kumar, a professor at the California Institute of Technology who previously worked on AI at Amazon, says the risks AI systems will cause harm to certain groups are higher when research teams are homogenous. "Diverse teams are more likely to flag problems that could have negative social consequences before a product has been launched," she says. Research has also shown diverse teams are more productive.
Corporate and academic AI teams have already-inadvertently-released data and systems biased against people poorly represented among the high priests of AI. Last year, researchers at the universities of Virginia and Washington showed that two large image collections used in machine learning research, including one backed by Microsoft and Facebook, teach algorithms a skewed view of gender. Images of people shopping and washing are mostly linked to women, for example.
Anandkumar and others also say that the AI community needs better representation of ethnic minorities. In February, researchers from MIT and Microsoft found that facial analysis services that IBM and Microsoft offered to businesses were less accurate for darker skin tones. The companies' algorithms were near perfect at identifying the gender of men with lighter skin, but frequently erred when presented with photos of women with dark skin. IBM and Microsoft both say they have improved their services. The original, flawed, versions were on the market for more than a year.
The scarcity of women among machine learning researchers is hardly surprising. The wider field of computer science is well documented as being dominated by men. Government figures show that the proportion of women awarded bachelor's degrees in computing in the US has slid significantly over the past thirty years, the opposite of the trend in physical and biological sciences.
Share of bachelor's degrees earned by women in the US. Source: NCES
Little demographic data has been gathered on the people advancing machine learning. WIRED approached Element about doing that after the company published figures on the global AI talent pool. The company compiled a list of the names and affiliations of everyone who had papers or other work accepted at three top academic machine learning conferences-NIPS, ICLR, and ICML-in 2017. The once obscure events now feature corporate parties and armies of corporate recruiters and researchers. Element's list comprised 3,825 names, of which 17 percent were affiliated with industry. The company counted men and women by asking workers on a crowdsourcing service to research people on the list online. Each name was sent to three workers independently, for consistency. WIRED checked a sample of the data, and excluded six entries that came back incomplete.
AI's lack of diversity and efforts to address it have won more attention in recent years. Thomas, Anandkumar, and Pineau have all been involved with Women in ML, or WiML, a workshop that runs alongside NIPS, currently the hottest conference in AI.
Despite the growth of such programs, few people in AI expect the proportion of women or ethnic minorities in their field to grow very swiftly.
Diversity campaigns at companies such as Google have failed to significantly shift the predominance of white and Asian men in their technical workforces. NegarRostamzadeh, a research scientist at Element, says AI has its own version of a problem well documented in tech companies whereby women are more likely than men to leave the field, and less likely to be gain promotions. "Working to have good representation of women and minorities is positive, but we also want them to be able to advance," Rostamzadeh says.
Source: WIRED, Casey Chin