Tag: cultural bias in AI

Leading with Equity: How Organizations Are Making AI Inclusive and What You Can Learn 

Posted on by Javier Encinas

In Part 1 of this series, we explored AI’s cultural blind spots and how tools can miss the mark when they’re built without the full spectrum of human experience in mind. In Part 2, we dug into the role of inclusive leadership and why human judgment is essential for steering AI toward equitable outcomes. 


Together, these conversations point to a simple truth. AI reflects the choices, values, and perspectives of the people who build it. And when those perspectives aren’t diverse, the technology isn’t either. Left unchecked, AI can quietly reinforce inequities, undermine trust, and work against the very missions our organizations are trying to advance. 

But here’s the good news: many organizations are already showing what’s possible when equity in AI comes first. In this post, we highlight real-world examples of organizations leading with equity and share lessons mission-driven leaders can apply right now. 

Why Equity in AI Matters for Leaders 

AI systems that overlook diversity or amplify bias can damage reputations, erode audience trust, and reinforce systemic inequities. For mission-driven leaders, this is both a technical challenge and a strategic one. 

Equity in AI aligns with organizational purpose, ensuring that the tools we use serve all communities fairly, respect cultural contexts, and advance positive social outcomes. By prioritizing inclusive AI, leaders have an opportunity to shape technology that reflects their values and strengthens trust with the people they serve. 

Real-World Examples of Equity in AI 

Latimer.ai: Inclusive Training Data 

Latimer.ai is a standout example of integrating equity from the ground up. Their large language model (LLM) is trained using input from underrepresented communities including folk tales and oral histories from around the world, ensuring that the AI understands diverse perspectives. 

Through partnerships with universities and community organizations, Latimer.ai incorporates cultural nuance and lived experience into its datasets, showing that inclusive training data is foundational for equitable AI outputs. 

Key takeaway: AI that reflects a wide spectrum of experiences produces more balanced results that better represent a diverse audience. 

AI Now Institute: Research and Policy Advocacy 

The AI Now Institute examines the social implications of AI, with a focus on bias, fairness, and equity. Through rigorous research, policy recommendations, and frameworks, they guide organizations in adopting responsible AI practices. 

Their work underscores that responsible AI is a social challenge, and organizations need robust, research-driven insights to make ethical decisions that genuinely advance fairness. 

Key takeaway: Evidence-based research is crucial for understanding where AI falls short and shaping policies that promote equity. 

Inclusive AI Foundation: Governance & Best Practices 

The Inclusive AI Foundation is a nonprofit organization that works to embed ethical, inclusive practices across AI development. Their approach emphasizes structured governance, evaluation frameworks, and community engagement to ensure AI systems serve all populations fairly. 

They offer workshops, consulting, assessments, and road mapping for leaders looking to implement inclusive AI in their own organizations.  

Key takeaway: Governance and stakeholder engagement are essential for embedding equity into AI design and deployment. 

Five Key Lessons for Mission-Driven Leaders 

  1. Audit your AI tools and outputs: Examine datasets and model outputs for underrepresentation and bias. 
  2. Demand cultural filters or adjustable framing: Ensure AI tools allow context-aware outputs tailored to diverse audiences. 
  3. Prioritize values-aware AI: Understand the priorities, values, and constraints of the communities you serve. 
  4. Partner with underrepresented communities: Co-create datasets, evaluation metrics, or prompts to ensure authentic representation. 
  5. Measure, iterate, communicate: Track outputs for bias and inclusivity; make equity part of your organizational standard. 

These practices help leaders translate abstract principles into concrete actions that make AI more inclusive. 

Closing thought 

AI’s cultural blind spots are real, but equity is achievable. Organizations like Latimer.ai, AI Now Institute, and Inclusive AI Foundation demonstrate how inclusive AI is possible when intentionality, research, governance, and community engagement come together. 

Mission-driven leaders can learn from these examples, apply these lessons, and take proactive steps to embed equity into AI initiatives. By doing so, you not only enhance your organization’s impact but also build trust, credibility, and lasting relationships with the communities you serve. 

AI has the power to amplify good, but only if we lead with equity. Let’s make inclusive AI the standard, not the exception. 

Cultural Bias in AI: Why Leaders Need to Ask Which Humans It Reflects

Posted on by Javier Encinas

When people say AI “thinks like humans,” it sounds reassuring. If these systems are going to help us in classrooms, clinics, and community organizations, then “thinking human” feels like a good start.

But here is the real question: which humans?

A Harvard study (Henrich et al., 2023) revealed cultural bias in AI, showing that large language models (LLMs) mostly mirror the mindset of people from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. That is the shorthand researchers use for the populations most often studied in psychology and social science. In practice, it means AI often sounds like it grew up in Boston or Berlin, not Bogotá or Bamako.

And sometimes, the models lean even further into this worldview than the people themselves. They can be more WEIRD than WEIRD.

For mission-driven leaders, this blind spot matters. If your work depends on AI for insights, outreach, or strategy, the technology you’re using may be leaving out entire communities.

Cultural bias in AI at home and abroad

Globally, the mismatch is obvious. Populations in Africa, South Asia, and Indigenous communities align very little with how AI “thinks.”

But this is not only a global issue. In the United States, AI’s blind spots show up in familiar ways:

  • A curriculum tool built from suburban school data might not resonate in rural Oklahoma or majority-minority districts in Houston.
  • A healthcare assistant trained on urban hospital systems may be out of touch with the realities of rural clinics or community health workers.
  • A workforce app that assumes everyone has credit cards, stable internet, and four-year degrees will miss low-income families who live in a different reality.

AI reflects the voices that dominate online. That means it tilts toward urban, affluent, English-speaking communities and misses those less represented in digital spaces. 

And that’s not just hearsay; multiple studies have proven these gaps. 

For example, Stanford researchers document how major LLMs are trained predominantly on English language data, leaving many languages and cultural contexts under-represented (Stanford HAI, 2025). 

Another analysis found disparities in the accuracy of image geolocation estimation across different regions, with a tendency for AI tools to predict higher-income locations more often (Salgado Uribe, Bosch, & Chenal, 2024).

With mounting evidence of these biases, it’s important to assess the impact on our own AI-powered initiatives.  

Why inclusive AI matters for leaders

Mission-driven work depends on connecting with people where they are. And if your audience doesn’t align with the demographics that LLMs are trained on, you  run the risk of undermining your company’s impact, reputation, and funding.

Here’s how that might look:

  • Excluding key voices: Campaigns unintentionally overlook rural, multilingual, or underrepresented communities.
  • Missing the mark: Messaging comes across as out of touch, weakening trust with your target audience.
  • Missed opportunities: Important insights get lost, leading to lower fundraising, adoption, and customer loyalty.

This is not just a technology problem; it’s a leadership challenge. But one that can improve with the right changes. 

What more inclusive AI could look like

Right now, AI is a sponge. It soaks up what is most available online, which skews the results. A more inclusive approach would look different:

  • Diverse data: Training should include stories, conversations, and materials from underrepresented communities, not just Silicon Valley blogs and English-language media, so the model reflects a wider range of lived experiences.
  • Cultural filters: Imagine an “equity mode” setting, where leaders can shift how a model frames ideas depending on the audience. While some tools offer surface-level tone adjustments, they are not yet sophisticated enough to capture cultural norms, values, and context-specific subtleties.
  • Values awareness: AI needs to understand not just what people say, but why they say it. That could be loyalty to family, faith traditions, or the need to stretch every dollar. This understanding enables more authentic, relevant, and responsible engagement.

The opportunity for leaders

Cultural bias is a risk of using AI tools, but it also offers a chance to lead.

  • Spotting biases early helps leaders avoid costly missteps and apply thoughtful scrutiny when using LLMs.
  • Audiences notice when companies go beyond AI defaults. Tailored messaging makes communities feel understood and sets your brand apart.
  • Leading with inclusivity in AI as part of equity work raises the standard for trust across industries and communities.

Closing thought

Cultural bias in AI is real, but it is not unavoidable. The leaders who see it and demand more will be the ones shaping technology that bridges communities instead of excluding them.

Spotting these blind spots is the first step. In the next post, we’ll share six practical questions you can ask your tech teams and partners to hold them accountable, ensuring AI truly reflects the communities you serve.