Are AI Models Leaving Underserved Communities Behind?

Table of Contents

🚨 Did you know? A recent meta-analysis in JAMA Network Open examined 517 studies encompassing 555 neuroimaging-based AI models aimed at detecting psychiatric disorders. The analysis revealed that 83.1 % of these models (461 out of 555) were found to have a high risk of bias (ROB). (Source: Science.)

This isn’t just a tech hiccup; it’s a systemic issue. AI models often leave out underserved and disenfranchised communities—not because anyone intends harm, but because bias loves to hide in plain sight.

AI Exclusion: Here’s What’s Going On

AI doesn’t wake up one day and decide to solve problems for everyone. It learns from the data we feed it, and unfortunately, that data is often a reflection of our world: messy, unequal, and full of blind spots. When the training data is incomplete or biased, AI fails to serve—or even actively harms—those it leaves out.

Examples of exclusion:

Healthcare AI: A 2019 study found that mortality prediction algorithms used in U.S. hospitals underestimated the health needs of Black patients by 46 % compared to white patients. (Source: Science.)

Hiring algorithms: AI hiring tools reject women 25 % more often than men for technical roles, even with equivalent qualifications. (Source: Reuters.)

Language models: Generative AI is trained on just a few of the world’s 7,000 languages. Here’s why that’s a problem. Over 2,500 languages are at risk of digital extinction because most AI systems prioritize dominant global languages.

Why Being Left Out by AI Models Matters

AI’s potential to transform industries and improve lives is undeniable, but it also has the power to automate and scale inequality faster than ever before. If AI isn’t inclusive, it becomes a tool that perpetuates existing disparities instead of solving them. And as we enter an era where vector databases like pgai enable AI applications to scale rapidly, we must ensure these technologies serve everyone—not just a privileged few.

What Can We Do About It?

Here’s the good news: Building ethical, inclusive AI is possible—but it requires intentional action. Whether you’re a developer, a business leader, or a curious observer, here are three key steps we can take:

Invest in data diversity: Developers must ensure their datasets reflect the full spectrum of the human experience. This means sourcing data from underrepresented groups and actively addressing gaps in existing datasets. For example, at Timescale, our pgai technology supports large-scale AI applications by enabling more comprehensive and inclusive vector search capabilities.

Test and audit for bias: Use tools like fairness audits and transparency frameworks to evaluate your models before deployment. Consider implementing open-source bias-checking tools to ensure your AI applications meet ethical standards.

Engage with communities: Ethical AI development starts with the people it’s meant to serve. By co-creating solutions with underserved communities, businesses can build trust and ensure their technology is both accessible and impactful.

A Call to Action: Building AI Together

At Timescale, we believe that education and community-building are critical to responsible AI adoption. That’s why we’re committed to fostering awareness and supporting developers through resources, discussions, and innovative AI technologies like pgai. By empowering the next generation of developers with tools to build responsibly, we can help create AI systems that work for everyone.

So, here’s my challenge to you:

Developers: How are you ensuring your datasets and models are inclusive? What tools have helped you identify and address bias?

Leaders: What steps is your organization taking to make AI adoption equitable and transparent?

Community members: How can we better engage and educate those outside the tech industry about AI’s impact and opportunities?

Check out our Discord and let’s have these conversations—and act on them. Together, we can ensure AI lives up to its promise of solving humanity’s biggest challenges without leaving anyone behind.

timescale / pgai

A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL

Power your RAG and Agentic applications with PostgreSQL

Docs · Join the pgai Discord! · Try timescale for free! · Changelog

A Python library that transforms PostgreSQL into a robust, production-ready retrieval engine for RAG and Agentic applications.

🔄 Automatically create and synchronize vector embeddings from PostgreSQL data and S3 documents. Embeddings update automatically as data changes.
🤖 Semantic Catalog: Enable natural language to SQL with AI. Automatically generate database descriptions and power text-to-SQL for agentic applications.
🔍 Powerful vector and semantic search with pgvector and pgvectorscale.
🛡️ Production-ready out-of-the-box: Supports batch processing for efficient embedding generation, with built-in handling for model failures, rate limits, and latency spikes.
🐘 Works with any PostgreSQL database, including Timescale Cloud, Amazon RDS, Supabase and more.

Basic Architecture The system consists of an application you write, a PostgreSQL database, and stateless vectorizer workers. The application defines a vectorizer configuration to embed…

View on GitHub