Why Diversity in AI Makes Better AI for All: The Case for Inclusivity and Innovation

The rapid expansion of artificial intelligence has brought significant advancements, but it has also highlighted a pressing issue: the lack of diversity, particularly a lack of women, in AI development. As AI increasingly shapes industries and everyday life, ensuring that the technology is built with diverse perspectives isn’t just a moral imperative—it’s a technical necessity. Without intentional inclusion, AI systems risk perpetuating biases, limiting innovation, and leaving entire communities behind.

Last week in Palo Alto, Calif., I attended the Fem.AI summit, an event created by electronics and system design company Cadence that focused precisely on addressing this challenge. Bringing together leaders from the technology, policy, venture capital, and advocacy fields, Fem.AI explored how diversity drives better AI outcomes. The summit promoted strategic partnerships, supported women-led ventures, and championed initiatives designed to foster inclusive AI systems. The event’s mission is clear: to reshape the future of AI by ensuring it reflects and serves all of society. 

“Achieving true innovation in the current AI revolution requires the full participation of talented individuals, and too many women are slipping through the cracks of the AI pipeline,” said Anirudh Devgan, Ph.D., president and CEO of Cadence, in calling for gender equity in the AI workforce and beyond.

The recurring theme of the summit was simple: Diversity in AI isn’t just about ethics—it’s essential for creating more innovative and effective systems. If AI is to serve all of society equitably, it must be built by and for everyone. 

Why Diversity Matters in AI 

Diversity in AI development is essential for three critical reasons: It helps avoid bias, improves systems’ capabilities, and ensures broader user representation. 

Avoiding Bias in AI Systems 

One of the biggest risks in AI is the replication of existing societal biases. AI systems are only as good as the data they are trained on, and if that data reflects biased or incomplete worldviews, then AI’s outputs will follow suit. A Fem.AI panel titled “Nothing Changes Without Intentionality” explored how diverse teams are better equipped to identify and mitigate these biases, ensuring that AI systems are fairer and more just. Chelsea Clinton, Ph.D., from the Clinton Foundation, Reshma Saujani from Girls Who Code, and Nicole Johnson from the Cadence Foundation highlighted the importance of having women and other underrepresented groups actively involved in the design and implementation of AI to prevent the amplification of societal inequalities. 

Building a pipeline of diverse AI professionals is essential if we are to create equitable AI systems. Programs such as Girls Who Code and other STEM initiatives were highlighted throughout the Fem.AI summit as critical tools for increasing women’s representation in AI. Saujani spoke passionately about the need for continued investment in these programs to ensure that the next generation of AI leaders is more representative of the global population.

Paula Goldman, Ph.D., Salesforce’s chief ethical and humane use officer, expanded on this during the “Why Should We Care About AI Policy” panel. She emphasized that AI literacy is crucial for policymakers and developers alike. Goldman stressed that AI governance frameworks must focus on curbing bias and protecting public trust. She pointed to the importance of regulations, such as the EU Artificial Intelligence Act (EU AI Act), as vital steps in ensuring fairness and transparency. AI isn’t developed in a vacuum—regulations are necessary to ensure systems serve all users equitably. 

Goldman’s comments highlighted the role of international standards in creating cohesive governance structures. She pointed to voluntary commitments, including those made by companies under the EU AI Act, which encourage ethical AI practices. However, she stressed that without global regulatory consistency, there is a risk of AI development becoming fragmented and less effective in addressing broader societal challenges. Regulations aren’t about stifling innovation but about ensuring that AI develops in ways that are safe, fair, and transparent for all users.

Improving AI’s Capabilities  

Diverse teams don’t just prevent problems—they enhance AI’s potential by bringing fresh perspectives and varied problem-solving approaches to the table. As Sarah Ittelson from Accel pointed out during the “Venture Perspective” panel, AI has the capability to unpack and address biases that humans often miss. When people from different backgrounds contribute to AI development, they introduce new angles, challenge assumptions, and ultimately lead to more robust, comprehensive AI systems that perform better across a range of applications.

Joy Buolamwini, Ph.D., renowned for her work on algorithmic bias and AI ethics, echoed this sentiment. At Fem.AI, she emphasized that AI must reflect the diversity of its users to be effective. She introduced the concept of the “excoded”—people who are unfairly impacted by biased AI systems. This includes individuals screened out of job opportunities or facing discrimination in health care decisions. Buolamwini’s work illustrates that diverse representation in AI development is critical to creating technologies that work for everyone, not just a privileged few. 

Broader User Representation 

AI technologies are increasingly embedded in critical areas such as health care, education, and employment, and when these systems are designed by homogeneous teams, they risk leaving certain populations underserved—or worse, actively harmed. Having diverse teams in AI development ensures that these technologies are more inclusive, accessible, and equitable across demographics. This was a key point made by Maria Colacurcio, CEO of Syndio, who spoke to the dangers of AI being optimized for certain demographics and leaving others behind. 

The Business Case for Diversity in AI 

Diversity isn’t just good for society—it’s good for business. Companies that prioritize inclusivity often find themselves at the forefront of innovation, with clear competitive advantages. 

Innovation, Competitiveness, and Building Trust in AI 

Cadence’s Devgan emphasized this point during his keynote address at the Fem.AI summit. As the leader of a company valued at $72 billion, Devgan has witnessed firsthand how diversity fuels success. He shared that Cadence’s focus on building diverse teams has been integral to the company’s achievements, particularly in AI, where creative problem-solving and innovation are paramount. 

Devgan highlighted how diversity allows companies like Cadence to approach challenges from multiple angles, leading to breakthrough solutions. These teams challenge assumptions, offering fresh perspectives that spark innovation and enable AI products to better serve global markets. For Cadence, this strategy has been invaluable, driving both product excellence and market leadership.

Devgan also emphasized that, in addition to driving innovation, it is important to build trust and transparency as companies integrate AI into their operations. By creating emotionally and psychologically safe environments, companies can foster trust, allowing AI to support—not threaten—employees. At Cadence, this includes measuring psychological safety through employee surveys and transparent communication about AI’s role within the company. This approach demonstrates how diversity, trust, and psychological safety are interconnected, and how inclusive workplaces are better equipped to succeed in an AI-driven future. 

Devgan also noted that diversity is key to scaling AI solutions across industries. AI systems are increasingly embedded in critical sectors, including health care and engineering, and ensuring these technologies are developed with diverse perspectives is essential for their effectiveness.

My favorite “aha” moment was when Accel’s Ittelson shared how the company leverages AI to audit decision-making processes within their firm. This AI-powered “knowledge agent” reviews past conversations and decisions to identify potential biases, particularly in investment opportunities. Ittelson’s insight highlights AI’s potential as a tool for creating fairer, more equitable workplaces by identifying unconscious biases in real time, ensuring decisions are more reflective of diverse perspectives. 

Conclusion

The Fem.AI summit underscored the multifaceted benefits of diversity in AI. From reducing bias to driving innovation, inclusivity is not just a box to check—it is the foundation upon which better, more ethical, and more powerful AI systems are built. As part of its commitment, Cadence has funded the core Fem.AI Alliance with $20 million—enough to aid the initiative for the next 10 years—to ensure progress continues.” to ensure progress continues. As AI continues to transform industries, it’s essential that the tech sector, policymakers, educational institutions, and underrepresented communities collaborate to ensure AI serves all of humanity. Only through intentional, inclusive actions can we build a future where AI benefits everyone, regardless of their race, gender, or background. 

My overall analysis: The Fem.AI initiative is a great step in the right direction.