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Connecting the next wave of IoT Devices
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Changing the face of the IoT ecosystem
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Enterprises turn to IoT for sustainability and profits
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AI at the IoT edge
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Operationalizing AI
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AI Vanishes to go Mainstream
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AI Growing Up and Learning Accountability
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Merging Tech
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Quantum computing moving beyond the hype
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Across Informa Tech's Applied Intelligence Group, Analysts and Editors from Omdia, AI Business, IoT World Today, and Enter Quantum have come together in this eBook, to give their predictions on the tech trends that are shaping the digital transformation landscape in 2022 and beyond.
Advancing Gender Equality in Data, Analytics and AI
Applied Intelligence Group
As we reach the halfway point of 2022, we’re now living in a world where global developments mean we rely on the power of transformative technologies such as AI, IoT, and Quantum, to provide solutions more than ever. The adoption and continued progression of these technologies are essential across a wide range of industries and geographies, that need support to overcome challenges faced. The implementation and acceleration of transformative technology means projects can move forward at pace, and this is where we will really start to see a change in the current status quo. Individually both AI and IoT are valuable, but the power of these technologies when they come together is where we will continue to see the biggest impact that will make a real difference. Their confluence will turn what were simple solutions into complex, truly impactful offerings that will alter society and businesses for years to come. Individually both AI and IoT are valuable, but the power of these technologies when they come together is where we will continue to see the biggest impact that will make a real difference. Their confluence will turn what were simple solutions into complex, truly impactful offerings that will alter society and businesses for years to come. It's the continued advancements of each of these technologies that will enhance the other. Omdia, our technology research brand, estimates that by 2030 there will be 75 billion IoT devices around the globe, which will provide an unimaginable amount of data to strengthen AI systems. At the same time, AI allows this vast amount of data to be analyzed and acted upon with unprecedented speed. As we move forward, organizations will be looking at how they can gain competitive advantages through leveraging quantum computing to move beyond the limitations that traditional architecture presents; and increasing investment in AI-driven automation. Alongside this, also ensuring they converge Subject Matter Experts with Data Scientists and Engineers throughout various levels of the process. Whilst there is significant momentum in the space, Omdia predicts there are areas that still need to be addressed across 2022 and beyond. These include interoperability with IT existing systems, industry-wide standardization of AI measurement and success; implementing standardized regulation and governance; and a greater drive to improve diversity within the sector.
Jenalea Howell Vice President, Applied Intelligence Group at Informa Tech
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Introduction Advancing gender equity in AI Where gender equity stands today in AI Developing a more granular understanding of female under-representation Four steps for addressing AI gender equity Summary Remarks Acknowledgements Appendix
Contents
After acknowledging the significance and reality of gender parity in STEM fields and how it only encourages the wellbeing of an organisation's performance and profitability metrics, we have moved on to the next stage of research in our journey to learn and understand more - how to accelerate closing the gender gap. This report and the survey it's based on is distinctive in its methodology as it incorporates perspectives from the majority of women as well as men. Because different viewpoints are a hallmark of the workforce, we wanted to design a research that didn't skew solutions or action points towards a single social group. This research represents an effort to take the risk of believing that we can accomplish this goal without focusing on a singular societal bucket, instead focusing on AI gender equity and inclusion as a shared goal and challenge. Gender, color, ethnicity, and cultural background are all crucial components to bringing fresh perspectives and ideas to historically male-dominated fields. Diversity is a broad notion that embraces all of these factors. The research’s premise is to segment the issue into manageable parts, starting with the context of the issue, discovering the reasons behind the underrepresentation of women in AI, becoming aware of the effects of leadership structure, recognising the long term positive impact of diversity and tackling how we approach gender equity in AI moving forward.
Introduction
One key area to support this movement for change is focused on increasing the presence and eminence of women in the field of AI. Organisations must first really understand what can be done to close the gender gap in various areas (and increase their understanding of why there is a gap) and create actionable programs to accomplish this.
This report not only gives a voice to the underrepresented and unsung heroines in the field of artificial intelligence, but it also provides a framework and direction for businesses, institutions, agencies, governmental organisations and future generations of the evolving society to build a foundation of best practices when it comes to shaping the future, our future.
Aalya Dhawan, Director of Global Communication & Content, Women in AI
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Next
One key area of change is to focus on raising the profile and eminence of women in the field of AI. Organisations must first truly understand why there is a gender gap, what can be done to close it in various areas, and create actionable programs to accomplish this goal.
Advancing gender equality in AI
13k
200
volunteers
150
Omdia, a leading research group, and global media portal AI Business at Informa Tech, are partnering with Women in AI, a non-profit community of 13,000 members, 200 volunteers and 150 countries whose mission is to empower women and minorities to become AI and data experts, innovators and leaders in the advancement of ethical applications and the responsible use of AI.
countries
members
Diversity and Inclusion (D&I) is high on the agenda across most businesses and organisations, and part of bringing this concept to life involves looking at how to create a more diverse work culture.
In recent years, a lot of focus has been placed on understanding how big the gender gap is, where the gaps are, and why they occur. Well-researched topics about this gap are in the following areas:
Gender gaps on boards
Personal characteristics and promotion
Career progression for women
Job gains
Gender gap/quotes in publications – and subsequent bias in solutions
The survey and report are based on responses from CEOs, business leaders and founders in AI, with 50% of respondents being women, over a quarter of those being in their role for more than 10 years (26.7%), and all of the respondents working in the field of AI.
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Women in AI
Following a successful flagship event at the AI Summit New York in 2022, the collective goal of the partners was to further explore, examine and develop solutions for narrowing the gender gap in AI, and to create actionable plans derived from surveys and research.
50%
The goal of the “Advancing gender equality in Data, Analytics and AI" business report is to reveal where and why there is a gender gap in the data, analytics and AI field based on survey results, and research. The report will provide pragmatic actions for closing the gender gap based on both survey findings and research by diving deep into solutions as well as understanding root causes.
“Adancing gender equality in Data, Analytics and AI"
of respondents being women and in their role for more than 10 years
26.7%
and all of the respondents working in the field of AI.
The world of AI is constantly changing. Even in the past few months we have seen the launch of new technology such as ChatGPT that has taken the world by storm. AI is becoming increasingly part of our everyday lives in both business and society. As a result, the nature of both work places and the global work force is changing to adapt to the demands of customers and clients who rely on this technology. Companies need and require not just technical skills and expertise, but a diverse set of knowledge, experiences, personalities, and insights to succeed.
Where gender equity stands today in AI
Diversity and Inclusion (D&I) is high on the agenda now across most businesses and organisations, and part of bringing this practice or concept to life involves looking at how to change and create a more diverse work culture.
Fantastic event that I recommend people register for. Excited to be back for 2023. Great businesses and wonderful people to connect with and learn from. Gets a 10/10 MUST attend from me!"
Founder, AI Journal
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“Research shows that companies with equitable diversity (e.g., 50:50 men and women) on their boards and in their C-suites report on average 10% better financial performance than those that don’t have it.”
Chief Partnership Officer, Women in AI Co-Founder and MD, Dalebrook Media Middle East
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The AI Summit London is back with a bang for 2024. Building on the huge success of last year, the event will return to host strategic leaders, technology pioneers and industry disruptors as they meet face-to-face to network, learn and champion positive digital transformation for business and society. An audience of decision-makers – ranging from tech giants and FTSE boardrooms to policymakers, entrepreneurs and technical leaders – will experience a captivating programme of both inspirational and
practical content, alongside an immersive hub of emerging tech, networking activities and hands-on experiences. The AI Summit London will once again be the flagship AI event at London Tech Week, a week-long festival in June which, in partnership with the UK government, attracts more attendees and businesses than any other tech event in Europe. We have a load of opportunities to participate and benefit your business - can you afford to miss out?
However, the reality of this is not reflected in many organisations. The 2023 survey “Advancing gender equality in Data, Analytics and AI,” conducted by Omdia, AI Business and Women in AI, found that only 4% of male, female and non-gender respondents said that their organisations had achieved 50:50 equity in employment between men and women.
4%
of male, female and non-gender respondents said that their organizations had achieved
Thirty-nine percent said that their companies would take 3 to 5 years to achieve a 50:50 parity, and 54% didn’t think that their companies would achieve 50:50 male/female employment equity for 5 years or more.
were CEOs, company founders, C-level executives or middle managers, those most likely to know how far along the path of male-female employment equity their companies have come. Yet 47% of them said they didn’t know what their companies were doing in the way of achieving equity between male and female workers.
gender equity
2/3
of the survey respondents
It is critical for everyone to increase the influence and leadership of women and other under-represented groups in the AI field because diverse workforces reduce the risk of biased AI.
equality
Developing a more granular understanding of female under-representation
“There is a gap in access to AI employment. It’s a very specific area, so we need people who are of high calibre and can think critically. However, because there aren’t many women in leadership positions in AI companies, it’s often difficult for women to identify with role models or career paths.”
Aalya Dhawan, Director of Global Communication and Global Content for Women in AI
Significant progress has been made in developing awareness that there is, indeed, a gender equity gap between men and women when it comes to equivalent numbers of male to female employees, and equivalent opportunities for promotions and advancement. Accordingly, 57% of survey respondents indicated that their organisations have made or are taking steps to achieve gender diversity, and 52% responding that diversity is an organisational priority. However, AI companies still need a more granular understanding of diversity and equity challenges in order to progress further.
How do companies do this?
Understand why women are under-represented in AI Female under-representation is systemic in the technology industry and is not just limited to AI. For example, only 1 of 4 employees at Google, Apple, Facebook, Amazon and Microsoft are female. Only 31.4% of female employees at Apple and 25.5% of employees at Google are in leadership roles, and only 37% of technology startups have women on their boards of directors. Achieving gender equity is also a challenge in education and in corporate advancement opportunities.
Understand the culture of your organisation
Gender cultural barriers may be embedded in organisations that limit women’s advances, and managers and recruiters may not even be aware of such biases. This manager went on to say that the CEO of the company was male, as are most of the management team. She believes they sincerely desire to diversify the workforce, but upper management had no idea how to go about it. In the meantime, senior management felt comfortable about the corporate culture. This scenario plays out in many AI companies. Leaders want to diversity, but they don’t really know or accept that there might be cultural impediments in the organisation that prevent them from doing so. The problem begins with the recruiting process, where many HR systems and practices show gender bias. For example, a Proceedings of the National Academy of Sciences (PNAS) study concluded that both men and women recruiters in companies associated women with lower math and science skills than men. Companies also struggle with equity between men and women in areas of career advancement.
In a 2018 study conducted by the University at Buffalo School of Management, co-authors Katie Badura and Emily Grijalva reviewed 59 years of research and concluded that societal pressures contributed to gender differences in personality traits. “Men tend to be more assertive and dominant, whereas women tend to be more communal, cooperative and nurturing,” they wrote. Grijalva added, "Showing sensitivity and concern for others -- stereotypically feminine traits -- made someone less likely to be seen as a leader. However, it's those same characteristics that make leaders effective. Thus, because of this unconscious bias against communal traits, organisations may unintentionally select the wrong people for leadership roles, choosing individuals who are loud and confident but lack the ability to support their followers' development and success.”
“Women are more likely than men to occupy a job associated with less status and pay in the data and AI talent pool, usually within analytics, data preparation and exploration, rather than the more prestigious jobs in engineering and machine learning,” stated Erin Young, Judy Wajcman and Leila Sprejer in the Alan Turing Institute’s “Who are the Women? Mapping the Gender Gap in AI” report.
In 2020-2021, for example, Statista reported that in the U.S., men received more than half a million STEM certifications in science, technology, engineering and mathematics, while women earned only about 276,500 certifications. Those women who made careers in AI were over-educated for their positions and held positions in companies that were of less status and received lower compensation than those of their male counterparts.
are women in leadership roles, and only 37% of technology startups have women on their boards of directors.
31.4%
25.5%
Google
Apple
“I have repeatedly asked to be part of a diversity task force in my company, and my company said it is embracing diversity,” shared a female marketing manager who wished to remain anonymous. “But now it has been 18 months, and I’m seeing nothing change.”
“Research shows that, when going back in large companies and performance appraisals, men are consistently rated higher than women for promotion potential,” said Botha, “This is even if the women’s’ performance is rated higher than men in their current position.“
Understand the implications of your leadership structure
AI companies produce algorithms and products that analyse data and generate recommendations. If diverse minds aren’t working on these algorithms, the probability of algorithmic bias heightens and becomes a board level risk management issue. Amazon’s 2015 hiring algorithm is a classic example. The company wanted to narrow the job applicant funnel and chose to rely on 10 years of internal hiring data that represented its largely male workforce. Unsurprisingly, the algorithm excluded nearly 50% of the population (women) that Amazon could have included in its job application pool. In 2023, there are still job search engines that reduce applicant pools for job seekers who describe themselves as “female,” and AI applications that shoehorn women with soft skills into less technically oriented AI positions with fewer salary and advancement opportunities.
Understand the risks of non-diversity to your organisation
When women and other marginalized groups are excluded from AI algorithm development and machine learning model creation, AI products risk becoming biased. Examples are seat belt, headrest and airbag designs in vehicles that have been developed from data primarily collected from men, and that don’t account for pregnancy or other anatomical differences that woman have; or in cardio-vascular AI models that are constructed based upon common symptoms that males experience, and that may miss signs of cardio disease in women because the symptoms are different.
Moody’s Analytics estimates that “closing the gender gap in labour force participation and the gender gap in OECD (Organisation for Economic Cooperation and Development) countries can raise economic activity by about 7% or about $7 trillion in today’s dollars.” That’s a lot of economic growth to leave on the table, and it makes sense for AI companies to capitalise on the talents and skills of the total labor force, not just half of it.
Four steps for addressing AI gender equity
What’s the best way to go about it?
AI companies can improve their ability to attract top talent by screening for bias the rulesets and algorithms that their automated job application screening systems use. If biased criteria are found, they can be amended. Companies can also work with colleges and universities by providing internships that students use for course credit, and by hiring the best and brightest of these interns for permanent positions — including women. New recruits also want to join companies offering them career paths
“How we are encouraging more women to pursue STEM careers is one of the most pressing concerns and questions we must address today,” said Aayla Dhawan.“We still need to make STEM education more accessible to women and engage them in a significant manner. This begins well before the point at which they implement what they learn in the classroom to their jobs.”
Engage more women in STEM programs early
STEM (science, technology, engineering and mathematics) is an education initiative designed to encourage youngsters from elementary grades through college to enter the technology, engineering, mathematics and science fields. Today, STEM programs operate in Australia, China, France, South Korea, Taiwan, the UK and the US. In assessing STEM progress, the AAUW (American Association of University Women) reported that men vastly outnumbered women majoring in college STEM fields.” The AAUW cited causative factors such as gender stereotypes (STEM fields are generally viewed as masculine), male-dominated STEM cultures, few female role models and early socialisation of girls that generated anxiety and a lack of confidence in mathematics skills.
“Research shows that companies with equitable diversity (e.g., 50:50 men and women) on their boards and in their C-suites report on average 10% better financial performance than those that don’t have it,”
Revisit corporate recruitment practices
The average corporate job opening receives 250 applications. To cope with the influx, HR departments use automated “funnel” systems that trim down job applications based upon a set of criteria that produces a smaller field of candidates. The Amazon algorithmic experiment that favored men over women because of a past employment history of hiring men continues to play out in many HR “funnel” systems because a majority of technology and AI companies have histories of successfully hiring men, and the systems operate based upon this history.
Gender-related STEM perceptions and socialisation indeed start well before college and joining the labour force. A Microsoft-KRC study of females from ages 10 to 30 concluded that “despite the high priority that is placed on STEM in schools, efforts to expand female interest and employment in STEM and computer science are not working as well as intended.” Some of the reasons given were a need to engage girls in school STEM programs in more “real world” projects that made STEM more of a reality for them, as well as peer pressure, lack of support from teachers and parents, a lack of female role models and the need for inclusive classrooms that valued female opinions.
Women AI professionals can also get involved—by providing guidance to teachers and schools in curriculum development, serving on school boards and committees, and working as mentors and role models for young girls and women as they pursue STEM studies.
“Transparency is necessary to understand how systems work and why they produce certain outputs, and to carry out research to understand the current and potential impact of AI systems on women,” wrote Celestine Gillette, Gina Neff and Livia Gouvea in the paper, “The Effects of AI on the Working Lives of Women,”12 for the Inter-American Development Bank.
The effort should start early in K-12 education, and even earlier than that, such as when training future teachers in universities. The courses that future teachers take should include anti-bias and sensitisation training that can help men and women develop awareness of their own as well as outside biases that could adversely reflect on students by gender, race, etc. School districts can continue to build this awareness by incorporating inclusiveness in the goals and performance reviews that they use for teachers and their performance in classrooms.
Unfortunately, current practice still shows that men get a disproportionate number of career advancement opportunities in AI, whether this is due to informal means of networking that men have, more favorable perceptions of male leadership traits, or simply the fact that men occupy most of the high prestige AI and ML development jobs and are promoted as a result. AI companies would be well served to revisit their career advancement policies and to consider a dual-path job promotion structure that rewards both technical and management excellence. If companies have female candidates who bring value and talent to the technical side of the firm, a conscious effort should also be made to eliminate bias that can cause these candidates to be overlooked. This is confirmed by the Omdia, AI Business and Women in AI Women survey: 52% of respondents said that fostering more diversity at all levels of the organization would encourage more women to climb the career ladder. 39% said that their companies were improving performance reviews to be more comprehensive, while 36% said efforts were underway to improve workforce diversity. “The most pivotal aspect to enable more women to enter and succeed in the AI field is to retain companies’ existing women talent, and to create career trajectories,” said Women in AI’s Dhawan. To attract and retain women in AI, companies should provide what female job applicants (and others) are seeking. When asked what the biggest attraction is in pursuing an AI job, 26.8% of survey respondents said they were most interested in an AI position when it matched their skillset; 22.7% wanted an opportunity for advancement; 20.6% wanted opportunities to work remotely; and 14.4% wanted job that provided life-work balance. The same group of survey respondents said that lack of the right skillset for a particular job was the biggest detractor from applying (36%), followed by negative work-life balance (21%) and negative perceptions during recruitment (12%).
Make gender equity a priority in your work environment
For AI companies, the easiest metric to achieve for gender equity is hiring and attaining equal numbers of female and male employees. Other equity indicators, such as equal access to promotional and earning opportunities, are more elusive. The takeaway for AI companies is that it’s time to review job descriptions and past promotion decisions to determine if the full spectrum of management and advancement traits is being considered in promotion decisions. These criteria can be revised where needed.
In the ‘Advancing gender equality in Data, Analytics and AI’ survey, 69% of both male and female respondents felt that a more equitable workplace correlated with an improved workplace culture. Pivotal to achieving a diverse workforce is attaining equity between men and women in numbers, support and promotional opportunities. Twelve percent of respondents said that their companies had already achieved 50:50 parity between the numbers of male and female employees in their departments, although not necessarily in their companies as a whole. Eight percent said that they had more women than male employees in their departments, and 73% said that men outnumbered women in their departments. Collectively, 52% of survey respondents said that their organizations still had room to improve fostering equity on all levels of the organization.
Other areas that companies should review are internal support resources and informal networking. “There are typically two ways to get things done professionally,” said Brenda F. Wensil and Kathryn Heath in the Harvard Business Review “One way is explicit, established, and formalised: the job-specific mode we use to get our work accomplished every day. Job descriptions, agenda items, expertise, and hierarchy dictate how this work is done and how formal decisions are made. The other way is informal, highly nuanced, and relationship based. It involves leveraging human connections, corporate maneuvering, physical proximity to decision makers, and personal and professional influence inside the office and outside at informal gatherings,” they wrote. “While both ways are important, we have seen in our work coaching women executives that they overwhelmingly struggle more than men to take advantage of informal networking situations. Part of the problem is systemic: When men go out together after work, women often are not invited. Eighty-one percent of women say they feel this type of social exclusion in work situations.” Men and women in AI can improve equity and inclusion by developing informal networking and support mechanisms that include everyone. This is not a pipe dream. In April, 2021, CNBC conducted a survey that revealed that 80% of workers wanted to work for a company that valued diversity, equity and inclusion. Many of these survey respondents were men.
Provide advocacy that advances gender equity in educational, corporate and government settings
In a best case scenario for all, women in AI would have role models and mentors to assist them as they develop their skills and careers. To date, they don’t have access to this help as readily as men do. For these women, who might feel isolated or excluded in their organizations, outside organizations, conferences and digital coaching can help. In our own survey results, 53% of respondents said they were mentoring at least one person and 41% said they were mentoring more than one person. This is a positive start for mentoring in companies that can be expanded to providing mentoring, guidance, guest lectures and role models to young girls and women in school and college classrooms.
AI companies can also relieve unique stresses women experience by providing maternity/paternity leave, flexible hours and affordable childcare. “We frequently overlook the crucial role that women play in supporting their families,” said Dhawan. “We must develop programs that can assist women in bridging both the knowledge gaps and their “return to work” programs in order to keep up.” Finally, AI companies and organisations can advocate and collaborate with governmental entities to improve the participation of women in STEM programs, and to create more opportunities for women in AI.
“They should work with national and international organisations to initiate research and advocacy programs, such as the Inclusive Data Charter (IDC), which promotes more granular data to understand the needs and experiences of the most marginalised in society,” wrote Erin Young, Judy Wacjman and Leila Sprejer, in a report for The Alan Turing Institute.15
Survey respondents acknowledged that government action toward combating AI bias was still in early stages, with 36.2% saying they didn’t believe their governments were doing much to eradicate AI bias, and 46.8% weren’t sure. Only 17% said that their governments were taking positive steps in combating AI bias.
Respondents want to be matched to jobs based upon the skills that they bring to the table, and they want to be rewarded and promoted for the work that they do. They also want a flexible work environment that allows for remote work. What they don’t want is a mismatch between a particular job and their skills, an inability to achieve a healthy life-work balance, or feeling negative perceptions as soon as they walk in the door to an interview.
Summary
The AI (and technology) diversity and inclusion issue begins much earlier than when women apply for their first AI job. It begins in the elementary and secondary grades and extends through college as women opt (or don’t opt) for STEM careers. Young women and girls may be discouraged from pursuing STEM studies because of peer pressures, socialisation pressures (“this is boys’ work”), parental pressures, and even unconscious biases that are demonstrated by both male and female teachers. These biases are further promoted because many young women and girls see few women who are highly successful in AI and technology and could serve as role models. Women AI professionals can help by volunteering and participating on school boards, in curriculum development, in guest lectures and in mentoring and providing internships. Educational institutions that train future teachers should deliver diversity awareness and sensitivity training as part of the education that all future teachers must take and pass; and school systems can include diversity and inclusiveness in the performance reviews that they administer to teachers.
“We've made progress, but we still aren't where we want to be,” said Dhawan. “Bias may sometimes emanate from both the top down and the company culture, which is why it's important to support more efforts and dialogues with women. We want men to actively support women as well, so it's not just about women helping women. It's a deeply ingrained social system that we must address now more than ever before.” Findings from the Omdia, AI Business and Women in AI survey uncovered several key areas where work can be done to improve gender equity in the AI industry:
There is more work to be done within AI companies if gender equity is to be achieved. Company leaders know this, but for many,“diversity” is still an abstract term, and they don’t really know how to go about achieving gender equity. Tangible steps that companies can take include:
Active advocacy in government, educational and corporate settings can help shrink the gender equity gap. Engage in advocacy efforts through women AI professionals sitting on boards, participating in curriculum- building and mentorship in education, and joining government agencies and other representatives to raise awareness of the gender equity gap and advances. In many cases the systems that are in place and those who are in charge of them aren’t aware of the gender equity gap; if they are, they may not know what do about it. This is where creating straightforward, actionable initiatives aimed at reducing the gender gap can benefit everyone. We would like to emphasise the continuing work that Omdia, AI Business and Women in AI have done on exploring the gender gap in the field of Data, AI and Analytics with this report, building on the preliminary research conducted by Women in AI on ‘Shaping the Future of Work for Women in AI’. The aim of this report is to provide ideas for actionable plans that can be created and implemented in organisations to recognise any disparity that exists, and to accelerate closing the gender gap.
The AI industry is expanding, and AI companies can’t afford to overlook the potential of half of the population.
KEY AREA ONE
KEY AREA TWO
KEY AREA THREE
Evaluating HR funnel systems and rulesets to assure that these automated screening systems aren’t inadvertently screening out female talent, or other talent from marginalized groups. Make sure corporate recruiters themselves come from diverse backgrounds, since they tend to be disproportionately male, and also ensure that they take diversity and bias training. Reviewing promotion history and the performance review process, to ensure that all employees, regardless of gender or ethnicity, are being evaluated fairly and comprehensively. Making the work environment ”gender sensitive” by providing opportunities for remote work, maternity leave, affordable child care, etc., that women need in order to achieve work-life balance, and to continue their careers without interruption. Promoting more deserving women into leadership roles in middle and senior management. These women can serve as role models and mentors, and can pave the way for more open, informal networking in companies that includes both men and women.
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Learn more about the work we do at Informa Tech on Diversity and Inclusion, email (add in) Receive the latest news, insights and updates from the AI Business team, which works with the global AI community (subscribe form). Access your AI research solutions by speaking with the experts at Omdia AI (link to form)
Acknowledgements
Debbie Botha Chief Partnership Officer, Women in AI Co-Founder and MD, Dalebrook Media Middle East . Aalya Dhawan Director of Global Communication and Global Content for Women in AI. Rose Elcock Founder and HR Business Partner,· Virtue HR Solutions Omdia Research Team Deep market knowledge and actionable insights AI Business Team Global media outlet for the AI community
This research report was made possible from the invaluable thought leadership, contributions, insights and personal attention of the following individuals and organisations. To each of them, we extend our thanks and gratitude.
Appendix: Demographic Data
The ‘Advancing gender equality ’ survey interviewed 101 respondents, all of whom worked in the AI industry. Half of respondents were women; 40% were men; and 10% were of non-binary or undeclared gender. Organisationally, 49% of respondents were from organizations with under 500 employees while 51% came from organisations with more than 500 employees. The industry breakout was 42% working in technology; followed by 11% in banking/finance; 9% in education; 7% in marketing; 6% each in government and telecom; 5% in healthcare; 4% each in manufacturing/engineering and utilities; and the remainder spread across agriculture, oil and gas, entertainment/leisure and venture capital. Respondents came from all corners of the world, with 36% of respondents from the U.S.; 29% from Western Europe; 13% from Asia-Pacific (including Australia); 11% from Eastern Europe; and 3% each from Africa, the Middle East, Canada, and Central/South America (including Mexico and the Caribbean). (SLIDE Q4). 58% of respondents were between the ages of 26-44; 29% were between the ages of 45-64; 6% were age 18-25; and 4% were age 65-74. Of these respondents, 25% have been in their jobs 3-5 years; 21% have a tenure of 6–10 years in their jobs; 9% have 11-15 years; 18% have more than 15 years; and 28% have been in their jobs for two or fewer years. (SLIDE Q3). 21% were founders, presidents or CEOS of their organisations; 11% were in Business Intelligence; 8% each were in consulting, digital transformation/innovation, product/project management; 7% were in marketing; 6% were in engineering; 5% were in R&D; 18% were in the IT field; 4% were in operations; 3% were in sales; and 2% were in AI Ethics. (SLIDE Q6). 73% of respondents held Bachelor’s or Master’s degrees and 12% had Ph.D.s 63% held leadership positions (ranging from project managers to CEO/Founders/C-level) in their organisations; and 21% of respondents were members of Women in AI. Survey aims and driving factors When this survey was launched it was understood that there already was a recognition of the male/female gender gap in most AI organizations. Give this understanding, one major aim was to see how far along companies had progressed in taking action to eliminate the gender equity disparity. In the survey, 30% of respondents said their organisations were taking steps to make gender equity a priority in their organisations; 27% said they had already made significant progress; and 20% said they were already doing well. This was positive news for closing the gender equity gap. Slightly more than half (52%) said making gender diversity an organisational priority was a strategy they were using; 43% said they were reviewing and researching what motivates women in the workplace; 31% said they were developing equity policies; 31% said they were setting up a system of checks and balances; and 21% said they were reviewing paternity (and likely maternity) leave policies. (SLIDE Q13). These actions aim to address some of the major reasons why women feel limited, and in some cases, compelled to leave the workplace so they can attend to other work-life balance priorities they are responsible for (such as maternity leave and child care). The fact that companies are working on initiatives in these areas is a positive step. Nevertheless, the task of facilitating gender equity in the AI workplace is one of continuous improvement. This is also reflected in our survey. Less than half (47%) of survey respondents, many of them in senior roles at their companies, didn’t know when their companies would achieve equal representation of male and female employees; while 18% thought it would take 3-5 years; and 14% said more than 5 years. Over half (52%) of respondents felt it was necessary to foster greater diversity at all levels of their organizations in order to encourage and promote more women to advanced positions in their companies; and 41% felt it would help to have more opportunities for women to be mentored or serve as mentors. It is in the understanding of these details that we can pursue a set of more specific actions that will address and diminish the gender gap in AI.
