There’s a gender diversity problem—many would call it a crisis—in AI. According to research by WIRED and Element AI, a mere 12% of leading machine learning researchers are female. This gap also exists in the industry. According to recent research by the World Economic Forum and LinkedIn, only 22% of jobs in artificial intelligence are held by women, with even fewer holding senior roles. The gap appears even starker at the “FANG” companies—according to the AI Now Institute just 15% of the AI research staff at Facebook and 10% at Google are women.

The first step to bridging the gender gap in AI is awareness. By understanding the nature and significance of gender bias, we can take meaningful steps to bridge the divide.

Gender biases are baked into AI tools.

There is overwhelming evidence that gender biases are baked into AI tools. In a very public admission, Amazon abandoned an AI-powered recruiting tool that disproportionately advantaged male candidates. Computer vision systems have been found to report higher error rates when attempting to recognize women

—especially those who have darker skin tones

—as compared to males. 

Gender biases are also pervasive in natural language processing tools. A 2016 study found that word embeddings trained on Google News articles exhibited revealing gender stereotypes. In one example, the vector analogy, “man is to computer programmer as woman is to x” was completed with x=homemaker.

Finally, gender biases have been shown to underlie speech-to-text technology. Another study revealed that speech-to-text technology performed much more poorly when analyzing female speakers as compared to their male counterparts. Why? The models were built to optimize for accuracy on lower-pitched voices and taller speakers who exhibited longer vocal cords (characteristic of males).

Because AI tools reflect the biases of those who build them, the only way to bridge the gap is to involve more women, and diverse women, in the design and deployment of AI tools. As Ivana Bartoletti, Women Leading in AI Co-Founder has noted,

“If the people working on artificial intelligence tools, products and services don’t resemble the society their inventions are supposed to transform, then that is not good Artificial Intelligence – and we shouldn’t have it. Increasing diversity in AI needs to move from just talk to actually doing something about it – and this is not just about coding, it is also about the boardrooms where the decisions on AI are being made.”

Women are disproportionately affected.

Predictions that AI will spell doom for the workplace have infiltrated the media in recent years. In an interview for 60 Minutes, artificial intelligence expert Kai-Fu Lee boldly stated that 40% of the world’s jobs will be replaced by robots capable of automating tasks.

Two important disclaimers are absent from most reports highlighting AI’s capacity to replace jobs. First is the recognition that AI will likely create more jobs than replaces. Second is the acknowledgement that females and males are slated to be differentially affected by AI’s increased presence in the workforce. Research by PwC, for example, has revealed that more women than men will be affected by job changes between now and the late 2020s. This is due, in large part, to the high proportion of women who hold clerical positions, which have one of the highest risks of being automated—according to the U.S. Bureau of Labor Statistics, 94% of secretaries and administrative assistants in the U.S. are women. Conversely, in the long run, PwC predicts that males may face higher automation risks as compared to their female counterparts as they are more likely to be employed in manual-task-focused sectors such as manufacturing.

AI roles demand a multitude of different skill sets. AI is also slated to have disproportionate effects on men and women in terms of skills. Research by the World Economic Forum has found that men are likely to outnumber women in a series of AI-related skills, including pattern recognition, machine learning, Apache Spark, and neural networks. Conversely, skills, in which women are projected to outnumber men, include text analytics, text mining, speech recognition, and natural language processing.  It’s important to recognize these disparities and double down on developing resources and training aimed at bridging AI-specific skill disparities.

Stereotypes abound. 

The gender problem underlying AI is exacerbated by deep-seated stereotypes. Science fiction is laden with these stereotypes. Too often, female AI beings are personified as submissive sexual beings created by men. Examples abound, including “Her” and “Ex-Machina”. In contrast, male AI beings are habitually personified as powerful beings—the likes of Iron Man and Terminator, for example.

Stereotypes that are inherent in science fiction have been incorporated into AI-powered digital assistants. Picture a digital assistant reminding you to leave for work to beat traffic, or that you have run out of yoghurt. Do you hear a male or female voice?  The evidence is striking. 67% of prominent digital assistants—which are so often intended to serve others in an inferior position and fulfil rather menial tasks—are female.

Several organizations have recognized that AI’s progress is hindered by deep-seated stereotypes and have committed to taking action. One notable organization is UNESCO, which has released a publication titled, “I’d blush if I could”. The title is apt. It refers to Siri’s response when asked: “Hey Siri, you’re a bi***.” The nonchalant response in the face of gender abuse is, for many, a reflection of AI’s gender problem. In light of this gender problem, UNESCO has delineated several recommendations aimed to help mitigate gender stereotypes that impede AI progress. These recommendations include a call to end the practice of making digital assistants female by default, as well as a call to “explore the feasibility of developing a neutral machine gender for voice assistants that is neither male nor female”.

While AI has enormous potential, there are challenges that must be brought to the forefront. Several organizations have put forth well-intentioned recommendations. The Women Leading in AI, for example, has outlined several recommendations including a ban on all-men panels at tech events and the “introduction of an assurance mark for companies to showcase to demonstrate that they have followed due process in their deployment of AI including recruiting a diverse team”.

Regardless of the steps we take to mitigate the gender gap, an important first step is an awareness. Fortunately, many female AI leaders are voicing their concerns and acting as role models towards a better path forward. Fei-Fei Li, Chief Scientist of Artificial Intelligence & Machine Learning at google cloud, for example, has urged, 

“We all have a responsibility to make sure everyone – including companies, governments and researchers – develop AI with diversity in mind.” Her mission—to democratize AI—is one that we should all be working towards. As Li reminds us,

“Technology could benefit or hurt people, so the usage of tech is the responsibility of humanity as a whole, not just the discoverer. I am a person before I’m an AI technologist.”

To celebrate International Women’s Day on the 8th of March, a diverse range of women across the AI sphere tell us what it’s like as a woman in the AI industry and provide practical tips for those looking to break-in

There is no denying the gender gap in AI. The World Economic Forum’s 2020 Global Gender Gap Report shows just 26% of professionals in Data and AI are women. One of the major challenges is encouraging interest amongst girls who view it as a “male-only” career path. To change this perception and attract more women into the industry, we need more female voices sharing their experiences in the AI industry, highlighting the opportunities and benefits of working in technology. After all, the AI industry can be both highly lucrative personally and incredibly rewarding professionally, with a chance to add real value to society.

Women are transforming the AI industry, pioneering to improve experiences for the next generation, and leading in the development of new technologies. women in ai This includes for example changing the game in STEM education and founding one of the first companies to introduce Live Chat customer service in Latin America. But what learnings would women in AI pass on to the next generation on this International Women’s Day? We asked the industry change-makers from Amelia’s Women in AI program to share their thoughts:

Fix the STEM gap to reduce bias in the development

Andrea Mandelbaum, President and CEO of McLuhan, is concerned about the widespread gender disparity within STEM. “With women representing a percentage of only 20-25% of the sector, technological developments will be skewed,” she says.  Andrea explains that boosting “gender diversity promotes the enriching exchange of visions and points of view to ensure objectivity when designing and implementing AI solutions in terms of understanding, interaction, empathy, generation of models and results.”

The battle for diversity starts in education

In France, as in many countries, there are few women in the digital professions (only 33%). Virginie Mathivet, Chief R&D Officer at TeamWork, says she has felt this acutely during her career, and knowing that there is a severe resource strain in data and AI only intensifies the need to do more to encourage women into these fields. Not only are we missing out on 50% of untapped potential, but we’re also doing so against a backdrop of rising unemployment in non-digital sectors and a major skills gap in STEM. Virgine believes that “we must act [by improving education] at secondary school.” Not purely through teaching, but also in setting up “discovery workshops” for young girls to speak to female experts and discover the variety of roles available.

The importance of having more diverse industry role models continues throughout education. As an African-American female leader of Des Moines University, Dr Angela L. Walker Franklin struggled to find mentors who had the same lived experience as herself, “despite many years of talk of diversifying leadership in higher education.” Less than one-third of US college presidents were women in 2016, and the number is not dissimilar in the UK. Nevertheless, she feels positive about the future. By increasing the number of female leaders in higher education, we can encourage and inspire more girls to pursue STEM subjects, not only boosting gender diversity in tech, but also in fields like engineering and life sciences, she says.

Defying gender roles and expectations is key to career success

Of course, it’s not just about getting women into the AI industry. Being a woman in a heavily male-dominated industry presents challenges, particularly when it comes to parenting. Liliana Mantilla who works as a Cognitive Delivery Manager at Amelia, an IPsoft Company, describes previous roles in which she felt she “had to work twice as hard as fellow male colleagues.” She also felt more afflicted by the guilt of a working mother, “due to a society that assigns the tasks of caring for children to women.” Thanks to a supportive husband, she has been able to divide up the load of housework and childcare, which has been fundamental to her professional development. As she says, there will be no real societal change unless “men also get involved in the fight” for gender equality.

You are not alone: Make connections with like-minded individuals

Communities, like Amelia’s Women in AI, have played a vital role for Gaby K. Slezák, Partner & Head of XR at Evenness. She highlights the importance of diversity in areas like AI and XR to ensure her “daughter’s generation does not suffer from biased automated hiring processes, non-inclusive virtual work environments and a lack of female role models for leadership.” She cites the importance of building networks of women in “fostering a culture of respect and belonging,” in which innovative ideas can thrive.

Improve diversity and close the skills gap for good

According to WEF’s Future of Jobs 2020 report, the pace of technology adoption is expected to continue unabated, with a significant rise in interest in robotics and AI. And whilst this presents a major challenge for reskilling and upskilling our existing workforce, it presents a huge opportunity for the next generation. As Virginie Mathivet nicely puts it, “if each woman in AI convinced one young woman to come into this field,” we would quickly and easily achieve gender parity. So it’s clear that the enduring challenge on this International Women’s Day is to inspire, educate and support the next generation of STEM leaders, and drive the closing of the AI gender gap.

A real-world built and designed using data for men ignores the needs of half its population. This holds true even when artificial intelligence is harnessed to solve challenges facing all of humanity.

The default human at the centre of most data is ‘Reference Man’, said Caroline Criado-Perez, campaigner and author of the book ‘Invisible Women: Exposing Data Bias in a World Designed for Men’. This Caucasian man, who is 25-30 years old and weighs 70 kg, has been the ‘human of reference’ in research studies across sectors for decades.

The gender data gap is this “phenomenon whereby the vast majority of information that we have collected globally and continue to collect – everything from economic data to urban planning data to medical data – have been collected on men”, Criado-Perez said during her Breakthrough Days keynote as part of the AI for Good Global Summit 2020.

When data is not collected and separated by gender, there is no way to learn what works and what doesn’t for different groups. Fixing this gap by collecting gender-disaggregated data is essential if AI is to fulfil its promise of improving outcomes for everyone.

Missing data leads to missed opportunities

Relying on data from male bodies and lifestyles to define and solve problems results not just in discomfort – it can also be unsafe.

According to Criado-Perez, several female front-line health workers have spoken about feeling more exposed to COVID-19 due to their badly-fitting ‘unisex’ personal protective equipment (PPE). Studies have also found that a woman wearing a seat belt in a car crash is 47 per cent more likely to be seriously injured and 17 per cent more likely to die than a man in the same crash because the dummies used in tests were based on the 50th-percentile man, Criado-Perez said.

Similarly, any algorithm trained on male-dominated datasets is unlikely to predict accurate risks and results for everyone. Criado-Perez brought up a gender-neutral algorithm that was designed to predict heart attacks, but her research found flaws in the data.

“The paper provided hardly any disaggregated data and the studies on which the AI was trialled were heavily male-dominated,” she said, pointing out the lack of mentions of diabetes or smoking, both higher risk factors for women. When it comes to COVID-19, a lack of gender data will prevent us from understanding potential differences in how men and women respond to the virus.

The threat of increased bias

Another drawback of gender bias and data gaps in AI is that it does not just reflect them; it amplifies them. One study found that an image-recognition software trained by a deliberately-biased set of photographs ended up making stronger sexist associations.

“The dataset had pictures of cooking, which were over 33 per cent more likely to involve women than men. But the algorithms trained on this dataset connected pictures of kitchens with women 68 per cent of the time. That’s a pretty big jump,”

Criado-Perez

Google’s AI tool has since dropped gendered labels from image recognition to reduce bias, using ‘person’ instead of ‘man’ or ‘woman’ to tag images.

How do we bridge the gender data gap?

• When biased data is used in artificial intelligence, the danger is that it will increase prevailing inequalities in the world.

• This is concerning as AI applications are increasingly deployed in healthcare, judicial and policing practices, and human resources.

• When it comes to gender, the data gap applies not just to women, but also to transgender and non-binary people.

Using AI To Close The Gender Equity Gap

The problem businesses have with gender equity isn’t awareness. It’s execution. US corporations spend $8 billion yearly on ineffective (and sometimes counter-effective) implicit bias training. An impressive 79% of businesses plan to spend more money this year than in 2021 to advance diversity, equity, and inclusion. Billions of corporate-backed dollars are pouring into gender and racial equity causes.

Meanwhile, gender equity indicators are barely budging. At current rates of progress, we won’t close the gender equity gap until 2289.

The problem businesses have with gender equity isn’t awareness.

The gender equity gap is ossifying when it should be shrinking. Women are taught to lean in. Lean out. Be a boss, but not too bossy. Negotiate, and then penalized for doing so.

To move the needle on gender equity, companies need to embrace the tools of the Fourth Industrial Revolution.

With advanced technology like machine learning and cloud computing, organizations can ensure human capital decisions across the entire employee lifecycle are both equitable and in the company’s financial best interest. Here’s an example to instantiate my techno-optimism.

An Example Of AI As A Tool For Good

• Companies make three key talent decisions every year:

• How will we pay our employees?

• How will we review their performance?

• How will we evaluate their potential?

For the average Fortune 500 company, which has approximately 60,000 employees, that’s 180,000 opportunities to move closer to gender equity each year.

Make Smarter, More Equitable Decisions At Scale

By removing bias from these 180,000+ talent decisions, AI can drive smarter, more equitable outcomes for all. Companies know they must pivot from informal, subjective criteria to equitable, data-driven recommendations as the basis for their decision-making. People’s decisions are no different.

Plus, the same tools that augment human capital decisions can also quantify the projected financial upside of each decision. That means leaders can rest assured knowing that the equitable decision is the right decision for their people, their communities, and their top-line growth. 

If we were to grade corporate America on their progress toward equity, they’d receive an A for effort but a D for achievement. We need to stop trying to change women and start changing the system. With advanced technology, it’s not a question of if we can close the gender equity gap, it’s whether will we choose to.