Importance of Inclusivity in AI Development

As GenAI becomes more ubiquitous, research alarmingly shows that women are using these tools at lower rates than men across nearly all regions, sectors, and occupations.   A recent paper from researchers at Harvard Business School, Berkeley, and Stanford synthesizes data from 18 studies covering more than 140k individuals worldwide.   Their findings:   • Women are approximately 22% less likely than men to use GenAI tools • Even when controlling for occupation, age, field of study, and location, the gender gap remains • Web traffic analysis shows women represent only 42% of ChatGPT users and 31% of Claude users   Factors Contributing the to Gap:   - Lack of AI Literacy: Multiple studies showed women reporting significantly lower familiarity with and knowledge about generative AI tools as the largest gender gap driver. - Lack of Training & Confidence: Women have lower confidence in their ability to effectively use AI tools and more likely to report needing training before they can benefit from generative AI.   - Ethical Concerns & Fears of Judgement: Women are more likely to perceive AI usage as unethical or equivalent to cheating, particularly in educational or assignment contexts. They’re also more concerned about being judged unfairly for using these tools.   The Potential Impacts: - Widening Pay & Opportunity Gap: Considerably lower AI adoption by women creates further risk of them falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities. - Self-Reinforcing Bias: AI systems trained primarily on male-generated data may evolve to serve women's needs poorly, creating a feedback loop that widens existing gender disparities in technology development and adoption.   As educators and AI literacy advocates, we face an urgent responsibility to close this gap and simply improving access is not enough. We need targeted AI literacy training programs, organizations committed to developing more ethical GenAI, and safe and supportive communities like our Women in AI + Education to help bridge this expanding digital divide.   Link to the full study in the comments. And a link also to learn more or join our Women in AI + Education Community. AI for Education #Equity #GenAI #Ailiteracy #womeninAI

DEI Isn’t About Being “Woke”—It’s About Existing This holiday, I shared a beautiful photo of me and my sister standing in front of a Christmas tree, full of joy and warmth. Curious about AI tools, I used one to describe our photo and generate a “similar” version. But what I got back wasn’t us. The generated image erased everything unique about me and my sister. It replaced our individuality and vibrant presence with generic, stereotyped versions of people who didn’t look like us. This wasn’t just a technical glitch—it was a reminder of the deeply ingrained biases in AI. This experience hit hard. It’s not just about this one tool. It’s about the larger message: without inclusive practices, people like me are literally erased. DEI (diversity, equity, and inclusion) isn’t about being “woke.” It’s about ensuring that all of us—our identities, our experiences, and our existence—are represented and valued. When AI fails to represent people accurately, it highlights a systemic issue: Diversity in AI Development: AI tools must be built with diverse data sets and teams to reflect the richness of humanity. Equity in Representation: It’s not enough for AI to be accurate for some—it must work for all. Inclusion as a Core Value: This is not optional. If systems and practices aren’t inclusive, they exclude. Period. The gap between the original photo of me and my sister and the AI-generated result made it painfully clear: without inclusive practices, some of us are left out entirely. This isn’t about being trendy—it’s about existing in a world that sees us. We need better. We deserve better. #AI #DEI #InclusionMatters #Representation #BiasInTech #DiversityInAI

Artificial intelligence is shaping our world—impacting industries, redefining economies, and influencing the way we live and work. Yet, with such a glaring lack of diversity in the field, the future being built risks excluding the voices, needs, and perspectives of millions. This isn’t just a representation issue; it’s a power issue. Without diverse talent, we risk perpetuating biases in algorithms, inequities in outcomes, and missed opportunities for innovation that reflects the full spectrum of human experience. So how do we change the narrative? Here are three key moves we need to make: 1️⃣ Invest in Talent Pipelines: Programs like AfroTech, Black Girls Code, and AI4ALL are doing critical work to build pathways into tech for Black professionals. But corporate commitments must go deeper: hire, mentor, and sponsor Black talent intentionally at every level, from internships to executive roles. 2️⃣ Demand Transparency in AI Systems: Many of the algorithms shaping our daily lives—from credit scoring to job applications—carry racial bias. Black leaders must push for oversight, ethical AI practices, and systems that prioritize equity from their inception. 3️⃣ Lead Through Ownership: We must shift from being consumers of technology to its creators. This means building and funding AI-driven companies led by Black innovators. With $1.8 trillion in Black buying power, the opportunity is enormous. The solutions we create for our communities could drive widespread change. AI is the future—but we have the power to decide whose future it will be. The next generation of breakthroughs can be led by us, for us—if we step into the opportunity now. What strategies do you believe are essential to increasing Black representation in AI? Let’s discuss below.

Last week, as I was excited to head to #Afrotech, I participated in the viral challenge where people ask #ChatGPT to create a picture of them based on what it knows. The first result? A white woman. As a Black woman, this moment hit hard—it was a clear reminder of just how far AI systems still need to go to truly reflect the diversity of humanity. It took FOUR iterations for the AI to get my picture right. Each incorrect attempt underscored the importance of intentional inclusion and the dangers of relying on systems that don’t account for everyone. I shared this experience with my MBA class on Innovation Through Inclusion this week. Their reaction mirrored mine: shock and concern. It reminded us of other glaring examples of #AIbias— like the soap dispensers that fail to detect darker skin tones, leaving many of us without access to something as basic as hand soap. These aren’t just technical oversights; they reflect who is (and isn’t) at the table when AI is designed. AI has immense power to transform our lives, but if it’s not inclusive, it risks amplifying the very biases we seek to dismantle. 💡 3 Ways You Can Encourage More Responsible AI in Your Industry: 1️⃣ Diverse Teams Matter: Advocate for diversity in the teams designing and testing AI technologies. Representation leads to innovation and reduces blind spots. 2️⃣ Bias Audits: Push for regular AI audits to identify and address inequities. Ask: Who is the AI working for—and who is it failing? 3️⃣ Inclusive Training Data: Insist that the data used to train AI reflects the full spectrum of human diversity, ensuring that systems work equitably for everyone. This isn’t just about fixing mistakes; it’s about building a future where technology serves us all equally. Let’s commit to making responsible AI a priority in our workplaces, industries, and communities. Have you encountered issues like this in your field? Let’s talk about what we can do to push for change. ⬇️ #ResponsibleAI #Inclusion #DiversityInTech #Leadership #InnovationThroughInclusion

The World Economic Forum blueprint for equitable AI that was recently shared at Davos is a significant step forward in ensuring that the benefits of AI are shared broadly, however we can't lose site of the work we still need to do. As I've written about recently, AI is reshaping the global workforce, with jobs emerging in areas like sustainable AI infrastructure, data governance, new data centers and AI ethics. Looking to the jobs of the future, without intentional efforts, women—especially women of color and other underrepresented groups will again be left behind. Currently, women hold only about 25% of data and analytics roles in the U.S., and most of these are entry-level positions. As we prepare for the workforce demands of an AI-driven future, we must reimagine how women access and advance in these careers. The creation of new, AI-driven roles offers a chance to recheck our assumptions and imagine our preferred future. If we focus on targeted recruitment and training programs designed for women—especially those from underserved communities—these opportunities could be transformative. For example, single mothers, who head more than 80% of single-parent households in the U.S., often face systemic barriers to financial security. Providing accessible pathways to well-paying, high-growth AI roles could help close the economic gap, support families, and foster thriving communities. We must not lose site of the knowledge that simply creating these pathways is not enough. We must also consider the infrastructure and work environment. Are these opportunities being created in places where women and families can thrive? Do they include community supports like affordable childcare, housing, and transportation? How might we create a work culture that supports women? Systems are only as good as the people who build them. Ensuring women are represented at every level, from entry-level to leadership, isn’t just an equity issue; it’s a necessity for creating inclusive, ethical AI systems that are good for people and the planet. How might we, as educators, policymakers, and industry leaders, work together to ensure women are not just participants but leaders in the AI workforce of the future? #FutureOfWork #ResponsibleAI #WomenInTech #WomenInSTEM #EquityInAI #AIJobs #FutureSkills

AI can be a powerful tool—but only if you feel comfortable using it. According to a recent report from the Initiative for a Competitive Inner City and Intuit, 89% of small businesses are using AI—primarily to automate routine tasks, increase productivity, and boost job satisfaction. But the data also reveals something troubling: 🔹 Men report feeling more comfortable using AI than women, nonbinary, or gender-nonconforming business owners. 🔹 Minority-owned businesses report low comfort with AI tools at nearly twice the rate of non-minority-owned businesses. As a consultant, this matters to me. The technology is here, but equity in access and confidence is not. If we want AI to empower, not divide, we need to create inclusive paths forward. Here are 3 examples of the ways we can do that: 💡Make education accessible. Think bite-sized learning: short videos, podcasts, live demos—not just long webinars or whitepapers. 💡Create safe learning spaces. Workshops, peer groups, or guided sessions led by trusted facilitators—spaces where questions are welcome and curiosity is encouraged. 💡Demystify with relevance. Don’t just talk about AI in theory, but demonstrate how it solves real problems in YOUR business. Professional communities that create amazing spaces for learning, support, and encouragement, like #SheLeadsAI Society (link in comments) for women in AI, offer great opportunities for all three idea above. AI isn’t (and shouldn’t be) about replacing people—it’s about freeing them up to do more of what they love. Will roles change and evolve? Yes. Do skills need to grow, too? YES. Every business owner, regardless of identity, should have the tools and confidence to explore what’s possible - let's ensure that reality. (Link to original article in comments.)

👩💻🧠 What if the key to building better AI… is hiring more neurodivergent people to build it? Neurodivergent professionals—autistic, ADHD, dyslexic, and others—bring exceptional strengths to the AI and machine learning world: 🔹 Pattern recognition 🔹 Systems-level thinking 🔹 Unconventional problem-solving 🔹 Hyperfocus 🔹 High sensitivity to fairness and bias 💡These aren’t just “soft benefits”—they’re mission-critical for ethical, inclusive, and innovative AI. 📊 Research backs this up: • Autistic individuals can outperform neurotypicals in pattern detection tasks by up to 40% (Baron-Cohen et al., 2009). • ADHD is associated with greater creativity and divergent thinking, especially in tasks requiring flexibility and novelty (White & Shah, 2006). • Dyslexic people often show enhanced spatial reasoning and holistic processing, key in AI architecture and data visualization (Eide & Eide, 2011). 🧩 And yet, only 22% of autistic adults are employed in the U.S. workforce (Bureau of Labor Statistics, 2023). That’s not just a talent gap—it’s an opportunity gap. AI needs people who challenge assumptions. People who see things differently. People who won’t just automate old systems, but reimagine them. It’s time we stop thinking of neurodiversity as a checkbox—and start recognizing it as a strategic advantage. #Neurodiversity #AI #Inclusion #TechForGood #EthicalAI #Accessibility #AutismInTech #NeurodivergentLeadership

“What happens when the technology we build doesn't see us—and we refuse to be invisible?” Meet Dr. Joy Buolamwini — Educator. Change Maker. Barrier Breaker. DAY 23: #WHM25 Born in Edmonton, Alberta, Canada, to Ghanaian parents, Dr. Joy Buolamwini is a poet of code who uses art and research to illuminate the social implications of artificial intelligence (AI). She founded the Algorithmic Justice League to create a world with more equitable and accountable technology. 3 Little Known Facts: 1. Fulbright Fellow: In 2013, Buolamwini worked with local computer scientists in Zambia to help Zambian youth become technology creators. 2. Artistic Flair: She is also a spoken word artist—her TED Talk blends poetry and technology in a way rarely seen in academic circles. 3. Documentary Feature: Her work and personal story are central to the documentary film "Coded Bias," which premiered at the Sundance Film Festival, highlighting the real-world implications of AI bias. Quantified Accomplishments: Influential Research: Her study on facial recognition tech influenced major companies like Microsoft, Amazon, and IBM to pause or change their AI programs. TED Talk Impact: Her TED Talk on algorithmic bias has garnered over 1.7 million views, sparking global conversations about AI ethics. Accolades: Named one of BBC's 100 Women and Forbes' 30 Under 30 for her innovation and impact in technology and social justice. Her Impact: Dr. Joy's work has compelled tech giants and policymakers to confront the invisible harms embedded within "neutral" systems. She has highlighted that bias isn't just a human issue—it's a data issue, leading to governments rewriting laws and corporations overhauling their code to foster a more equitable digital world. Why This Matters on LinkedIn: In our increasingly digital, AI-driven professional landscape, we must ask: Who is being left out of the systems we trust? Dr. Joy's work is a call to action for ethical leadership, inclusive innovation, and accountability in technology. Her legacy serves as both a mirror and a roadmap for founders, DEI leaders, tech teams, and corporate boards. Her Contribution to Women's History Month: In a field often dominated by homogeneity, Dr. Joy stands as a beacon. She created space where there was none and insisted that visibility is power. Her story reminds us that representation isn't merely a metric—it's a mandate. Did you learn something new today? Type WHM25 in the comments if Dr. Joy's story expanded your perspective—and tag a leader in tech or DEI who needs to hear this. #WHM25 #JoyBuolamwini #AlgorithmicJustice #AIethics #WomenInTech #BlackWomenInSTEM #LeadershipMatters #TechForGood #InclusiveInnovation

This week, I learned about a new kind of bias—one that can impact underrepresented people when they use AI tools. 🤯 New research by Oguz A. Acar, PhD et al. found that when members of stereotyped groups—such as women in tech or older workers in youth-dominated fields—use AI, it can backfire. Instead of being seen as strategic and efficient, their AI use is framed as “proof” that they can’t do the work on their own. (https://lnkd.in/gEFu2a9b) In the study, participants reviewed identical code snippets. The only difference? Some were told the engineer wrote it with AI assistance. When they thought AI was involved, they rated the engineer’s competence 9% lower on average. And here’s the kicker: that _competence penalty_ was twice as high for women engineers. AI-assisted code from a man got a 6% drop in perceived competence. The same code from a woman? A 13% drop. Follow-up surveys revealed that many engineers anticipated this penalty and avoided using AI to protect their reputations. The people most likely to fear competence penalties? Disproportionately women and older engineers. And they were also the least likely to adopt AI tools. And I’m concerned this bias extends beyond engineering roles. If your organization is encouraging AI adoption, consider the hidden costs to marginalized and underestimated colleagues. Could they face extra scrutiny? Harsher performance reviews? Fewer opportunities? In this week's 5 Ally Actions newsletter, I'll explore ideas for combatting this bias and creating more meritocratic and inclusive workplaces in this new world of AI. Subscribe and read the full edition on Friday at https://lnkd.in/gQiRseCb #BetterAllies #Allyship #InclusionMatters #Inclusion #Belonging #Allies #AI 🙏

I recently saw a picture of an “#InclusiveAI”, team, but all the members were white. While it's great to see companies striving for inclusivity, it's important to remember that diversity & inclusion goes beyond just gender and includes race, ethnicity, age, ability, culture, and backgrounds. Having a diverse team when building #AI systems is crucial for several reasons. As someone who possesses multiple identities that are usually excluded when building these types of innovations, I care even more so. (ofc you shouldn’t only care when affected!). 🌻Why is true #InclusiveAI important? Firstly, it helps to uncover problems and make data connections that might be missed by a homogenous group. A truly representative team brings a range of skills, experience, and expertise to the table, which can drive superior AI by bringing diverse thought to projects. This can maximize a project’s chance of success. Secondly, diversity in AI development is important in combating against AI bias. AI learns only what people show it, so if the data used to train AI systems is skewed or biased, the resulting AI will also be biased. This can have major consequences. For example, if a #generativeAI model is fed photos of mostly white/light-skinned people to learn what a face looks like, then brown/dark-skinned faces will be difficult to generate—if generated at all. 💡A lack of diversity in AI development could increase discriminatory issues within AI technology. The lack of diversity in race and ethnicity, gender identity, and sexual orientation not only risks creating an uneven distribution of power in the workforce but also reinforces existing inequalities generated by AI systems. This reduces the scope of individuals and organizations for whom these systems work and contributes to unjust outcomes. In conclusion, it's imperative for diverse peoples to be part of inclusive AI teams. Building AI without ttue representation, without insistent diversity can result in flawed systems that perpetuate extreme biases on all fronts. By striving for true inclusion in AI development, we can ensure that future technology benefits all people and not just a homogenous group. 💭 Keen to know your thoughts on this topic, please share in the comments below. #theBOLDjourney #AITools #AI #marketing