Strategies for Promoting Diversity in AI Development

👩💻🧠 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

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

As a former staffing leader at Google and Nest Labs, I’m proud to have led the hiring of top engineers for Devices & Services while advocating for diversity. Diversity in hiring isn’t about filling quotas—it’s about expanding valuable talent pools, strengthening teams, and improving products to maximize impact. At Google, we set ambitious hiring goals, not to lower the bar, but to push ourselves beyond the usual pipelines (Apple, Meta, Amazon, etc.). This led us to uncover top talent from new universities, industries, and overlooked technical domains, including veterans with unique problem-solving experience. These efforts led us to rexamine our interview process. Were we assessing real skills or just looking for familiarity with our existing team? Case example: Product Design interview questions were unintentionally biased toward consumer electronics backgrounds, filtering out exceptional engineers from automotive, aerospace, and defense. By refocusing on essential skills, we unlocked a new talent pool—engineers who introduced fresh techniques that strengthened our hardware. It’s frustrating to see diversity efforts reframed as unmeritocratic. These programs weren’t about lowering standards; they were about raising them—ensuring Google truly hired the best. With AI advancing rapidly, diverse perspectives are more critical than ever. Has Google already forgotten the lessons of its 2015 photo app scandal? The cost of failing to build diverse teams will only grow in the AI era. How have diversity efforts shaped innovation in your industry? https://lnkd.in/ggkmEkJ5