AI’s Blind Spot: The Need for Women and Older Innovators in The Technology Industry

Women make up half the UK population, but only 22% of AI and data professionals and 18% of users across the largest online global data science platforms. The Alan Turin Institute

I’ve worked in technology for over 30 years, starting at Xerox, where we were at the forefront of global innovation. Out of a sales team of more than 50, only two of us were women. At Minolta, I was the only female sales executive, and later at Canon, I remained in the minority. I earned my place as the top sales executive by mastering the technical details of our products and putting in long hours. While the tech industry has evolved, I can’t help but question if we’re slipping back with AI.

Today, as I look at podcasts, conferences, and industry events, it’s still largely male-dominated, and although some men advocate for greater diversity, women and older professionals are often underrepresented in these discussions.

AI is hailed as the future of technology, set to transform industries and reshape the workforce. Yet, despite its promise, the AI field faces a persistent issue: the perception that it’s a man’s domain. This limits not only who feels they belong in AI but also who gets heard in its discourse.

While many men do advocate for women to have an equal voice in this field, we need to extend this advocacy to include older professionals as well. Without diverse voices, we risk an industry where certain perspectives and experiences are once again sidelined.

This article seeks to challenge that notion and provoke a rethinking of the narrative surrounding AI.

Past research shows how gendered divisions are naturalized and reproduced through technology. To begin with, technology often gets equated with “men’s power,” while women and girls are portrayed as less technologically skilled and less interested than their male counterparts. Such stereotypes can contribute to the gender gap in women’s participation in related fields.

What drives the perception that AI is a male-dominated field

The perception of AI as male-dominated stems from several interconnected factors that have shaped the tech industry for decades:

Historical Dominance of Men in Tech

Tech has been male-dominated since its inception. From the early days of computing, when tech companies were led by men, to today’s major AI companies, there has been a consistent lack of female representation in leadership roles. This has created an image of AI as an extension of an already male-driven field.

Lack of Diverse Role Models

In tech and AI, most public figures and industry leaders are men. This lack of visible female or older role models can discourage women and underrepresented groups from seeing AI as an accessible career path. When the leaders, speakers, and “faces” of AI are predominantly male, it reinforces a narrative that AI is “for men.”

Bias in Education and Training

The pipeline for AI careers often begins in STEM (science, technology, engineering, and mathematics) fields, where women have been historically underrepresented due to biases and a lack of encouragement for girls to pursue these areas. This results in fewer women pursuing careers in AI and fewer voices to challenge the perception of AI as a male domain.

AI’s Image as a Young, Male-Driven Field

AI and advanced tech fields are often portrayed as industries for young innovators. Coupled with the male-dominated perception, this creates an exclusive image, where women and older professionals might feel they don’t “fit” the profile of an AI expert.

Implicit Bias in Hiring and Networking

AI and tech companies sometimes replicate the biases of their hiring teams, resulting in a predominantly male workforce. Networking opportunities, mentorship, and other support systems tend to favour those already in the field, often making it difficult for women and older professionals to enter and advance.

The Self-Fulfilling Cycle of Bias in AI Products

AI products themselves can perpetuate these biases. Male-dominated teams may unconsciously create AI solutions that align with their own worldview, inadvertently sidelining the needs or perspectives of underrepresented groups. This perpetuates a cycle where AI reinforces its own male-dominated image.

By recognising and challenging these systemic issues, there’s an opportunity to reshape AI into a truly inclusive field, drawing on the diverse perspectives that will make it relevant and ethical for everyone.

Lets Change The Legacy of Exclusion

The perception that AI belongs to men is rooted in the broader history of technology, where men have historically dominated the field. This trend has continued into the AI space, reinforcing the idea that innovation, particularly in advanced technology, is primarily the purview of young, male engineers.

But this narrow view ignores the contributions of women who were pioneers in computing. Ada Lovelace, widely recognised as the world’s first computer programmer, laid the groundwork for what would later evolve into AI. Grace Hopper developed key computing languages that form the backbone of modern programming. These women were not anomalies but essential figures in the story of technology. And yet, today, we are still wrestling with the same skewed gender dynamics in AI.

The dominance of men in AI research and leadership roles perpetuates a cycle: men design AI systems, and those systems often reflect the biases of their creators. Without diverse input from women, older professionals, and people from other underrepresented groups, AI risks becoming another tool that reinforces the status quo, rather than breaking new ground in fairness and innovation.

Bias Built into AI

One of the more insidious aspects of AI is its capacity to amplify the biases of its creators. AI systems, no matter how advanced, are only as objective as the data they are trained on and the people who design them. If predominantly male teams are behind the algorithms that govern hiring practices, financial decisions, and healthcare outcomes, can we trust these systems to be free of gender and age bias?

AI is increasingly used in human resources, making decisions about recruitment and talent management. However, studies have shown that AI tools can perpetuate bias, favouring male candidates or disadvantaging older applicants. The input from diverse voices in the design of these systems is not just a nice-to-have; it’s a necessity if we are to create fair and just AI tools.

Where Are the Role Models?

The lack of visible women and older professionals in AI further cements the perception that this is not their field. When women and older individuals do not see themselves represented in AI leadership, research, or even in the media coverage of AI advancements, it sends a message that their contributions are not valued—or worse, not possible. This exclusion from the spotlight creates a cycle of invisibility, where future generations of women and older workers are discouraged from entering or advancing in AI-related fields.

But this couldn’t be further from the truth. Women and older professionals bring invaluable perspectives to AI development, grounded in different life experiences and approaches to problem-solving. AI solutions are only as innovative and effective as the diversity of thought that goes into them.

Gender bias in AI can occur at various stages: in the algorithm's development process; in the training of datasets; and in AI-generated decision-making.

The Business Case for Inclusivity

Diverse teams create better AI. This isn’t just an aspirational statement; it’s a proven fact. Research shows that companies with more diverse leadership perform better financially and are more innovative. AI thrives on varied datasets and different viewpoints. When AI developers come from homogenous backgrounds, the algorithms they create are more likely to reflect a narrow slice of human experience. This leads to AI systems that may work well for some, but fail to meet the needs of all.

If we are to harness the full potential of AI, we must break down the barriers that keep women and older professionals from contributing. It’s time to rewrite the story of AI as not just a technological revolution, but a social one too—one that values and integrates diverse experiences into the fabric of its development.

Breaking the Cycle

There is an urgent need to dismantle the myth that AI is the domain of young men. This involves proactive steps: encouraging more women and older professionals into AI through inclusive education and mentorship programmes, reshaping recruitment strategies to target underrepresented groups, and ensuring that AI development itself reflects a diversity of perspectives.

Governments and organisations also have a role to play by creating policies that promote inclusion in AI research and development. Whether through funding diverse AI initiatives or fostering partnerships that elevate women and older professionals in the tech space, the message should be clear: AI is for everyone, and the future of AI will be brighter with all hands on deck.

AI appears neutral, but it's made by humans, which means it internalizes all the same bias as we have - including gender bias. AI is a mirror of ourselves.

A New Narrative

As AI continues to shape the future of work, society, and even human identity, we must ask ourselves: who is shaping AI? The power of AI lies in its potential to improve lives, streamline processes, and solve some of humanity’s most complex problems. But to do so, it must be designed with humanity in mind, by people who represent all of humanity.

The narrative that AI is a man’s domain, or a young person’s expertise, is both outdated and dangerous. It limits our capacity for true innovation by excluding half the population and a wealth of experience from older generations. By rethinking this narrative, we can ensure that AI serves all of us, not just a select few.

It’s time for society to embrace the fact that women, older professionals, and people from diverse backgrounds have a critical role to play in AI. The future of AI will be inclusive—or it won’t live up to its potential. Let’s start by breaking down the barriers today and building a more equitable tomorrow.

Helpful Resources

Women in Data Science & AI - The Alan Turing Institute - The Women in Data Science and AI theme sits within the Public Policy Programme at The Alan Turing Institute. We work alongside policy makers and industry stakeholders, offering actionable insights and recommendations to tackle the multifaceted ethical, economic and governance-related issues stemming from inequalities in AI.

The IPI Global Observatory - The tendency to feminize AI tools mimics, and reinforces, the structural hierarchies and stereotypes in society, which is premised on preassigned gender roles. The gendering of AI can occur in multiple ways—through voice, appearance, or the use of female names or pronouns. Home-based virtual assistants such as Amazon’s Alexa, Microsoft’s Cortana, and Apple’s Siri were given default feminine voices?(Apple and Google have since offered alternativesaimed at “diversification” or “neutrality.”) As UNESCO points out, these devices were designed to have “submissive personalities” and stereotypically feminine attributes, such as being “helpful, intelligent, intuitive.” However, as evident from the case of IBM’s Watson, which used a masculine voice while working with physicians on cancer treatment, male voices have been preferred for tasks that involved teaching and instruction, as they were perceived to be “authoritarian and assertive.” Among these applications, Google Assistant is the only one that did not bear a “gendered name,” however its default voice is female.

A study by the Berkeley Haas Center for Equity, Gender and Leadership analysed 133 AI systems across different industries and found that about 44 per cent of them showed gender bias, and 25 per cent exhibited both gender and racial bias.

World Economic Forum - Technologists believe generative AI is turning into a whole new industry unto itself. But women haven’t been as eager as men to embrace it. According to research by the Oliver Wyman Forum of 25,000 working adults surveyed, 59% of male workers aged 18-65 around the world say they use generative AI tools at least once a week, while only 51% of women say the same. These disparities persist across age groups and the 16 geographies studied.