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The Urgent Need to Address Racial Bias in AI
Co-Founder Taliferro
Co-Founder Taliferro
What is racial bias in AI?
Racial bias in AI is when an automated system produces worse results for certain racial groups because of the data it learned from, the way the model was evaluated, or how the system is used after launch.
Quick playbook: reduce bias before it ships
Example: A model can look “accurate” overall and still be harmful. If it misidentifies Black and Brown faces at higher rates, the system is unsafe—even if the overall score looks good.
Artificial Intelligence (AI) is showing up everywhere—it's in the tools we use, the platforms we interact with, and the systems businesses and governments rely on to make decisions. But as AI expands, there's a big problem that needs attention now: racial bias.
This isn't just a technical issue—it's a societal one. And it impacts non-white communities the most. When AI is biased, it doesn't just create small errors. It leads to real-world harm, from biased hiring practices to wrongful arrests. Communities of color are paying the price.
The time to fix this is now. Governments need to step in with policies that ensure AI is built and used in ways that minimize racial bias and promote fairness. Without action, the very technology that's supposed to help us could end up deepening the inequalities we've fought to eliminate.
When people talk about AI bias, it might sound like a technical glitch. But it's personal. AI systems run on data, and if that data is flawed, the outcomes will be too. Take facial recognition systems—they misidentify Black and Brown faces at much higher rates than white faces. That's not just an error—it's a fundamental flaw that can have serious consequences.
Imagine being misidentified in a police database or denied a loan because an AI system didn't interpret your information correctly. This isn't some futuristic scenario—it's happening right now.
And the problem runs deeper. AI is being used in decisions about who gets job interviews or which neighborhoods get more police surveillance. If we don't fix the bias in these systems, we'll see the same old patterns of inequality continue in new ways.
Racial bias in AI hits non-white communities the hardest. These groups have always faced systemic bias and inequality, and AI—if not built carefully—can reinforce these issues. AI might be seen as neutral, but without fairness built in, it can become another tool of oppression.
For Black and Brown communities, this means the same problems they've dealt with for years—racial profiling, discrimination in hiring, lack of access to resources—are now driven by machines. Machines that don't understand the historical context or complexities of race.
We can't wait to address this. AI isn't just about the future; it's shaping the present for millions of people. Governments need to act now, or we'll end up building systems that are just as biased as the ones we've been trying to change.
Policy is the lever that forces consistency. Without rules, bias checks turn into “nice-to-have” work. That means they get skipped when teams are under pressure.
Good policy should require three things:
If you buy AI from vendors, make it a contract requirement. Ask for testing results by group, documentation of training data sources, and a commitment to re-test after updates.
At Taliferro Group, we understand the importance of tackling racial bias in AI. We're working with the state of Washington, through the Office of Equity and Inclusion, to help shape policies that make sure AI is used fairly.
Our approach is simple: make sure AI systems are built with the communities they impact in mind. This means looking at the data being used and asking the tough questions about how decisions are made. By getting involved early in the process, we aim to reduce bias before it becomes a problem.
But we know this is bigger than any one organization. The challenge of AI bias is too large for one group or government to handle alone. That's why we're calling on other businesses, governments, and organizations to join us in this fight.
Reducing racial bias in AI won't be easy. It will take a coordinated effort from policymakers, tech companies, and the public. But it's a challenge we need to face. The alternative—letting bias creep into the systems that run our lives—is unacceptable.
Governments need to lead the way. They must set clear guidelines that prioritize fairness and equality, especially for non-white communities that have faced systemic bias for generations. This includes testing AI systems for bias before they are widely used, and holding companies accountable when they fail to meet these standards.
The need to address racial bias in AI is urgent. This isn't just about fixing a technical issue—it's about ensuring AI is fair for everyone, especially for non-white groups that have long been affected by bias.
At Taliferro Group, we're doing our part. But it's going to take more than just us. Governments must take the lead in creating policies that address this head-on. The time to act is now.
Need an AI policy that accounts for equity?
We help teams set rules, review processes, and measurement so bias doesn’t quietly ship into production.
It shows up as higher error rates for certain groups, fewer opportunities triggered by automated decisions, or higher friction in identity and access flows.
No. Bias can come from how labels were created, how the model was evaluated, which thresholds were chosen, and how the system is used after launch.
Measure outcomes by group, fix the highest-impact error cases first, and keep monitoring after deployment so drift doesn’t reintroduce the same problem.
Bias drift is when a model’s fairness changes over time because the data, users, or environment changed, even if the code didn’t.
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