23 Jul 2025 · 5 min
Jim Carter III walks through how bias sneaks into AI systems and what it costs when it does. He covers real examples of AI gone wrong and gives businesses concrete steps to catch bias before it becomes a liability, especially as regulations tighten.
Hey there. Jim Carter here. Welcome back to the Prompt, an AI powered podcast where we break down everything that's happening in AI and how it impacts you. Now, let me ask you this. Imagine you're coaching a youth soccer team. One player takes a shot, misses the net. But it was a bold move. Do you praise the effort or point out the miss? Another player hesitates, passes instead. And you call that smart play. Over time, your feedback shapes what those players think is the right way to play. Not because there's a rulebook for every moment, but because they're learning from you. AI works the same way. It picks up on what we reward, what we ignore, and what we call a good decision. But if our feedback is inconsistent or based on hidden preferences, we're not teaching objectivity, we're teaching bias. Whether it's soccer or software, the outcome depends on what we value. Let me share what I've learned about keeping your AI fair, your customers happy, and your business out of legal hot water. Imagine you're looking in a funhouse mirror. Everything looks weird and distorted, right? AI works the same way. When you feed it bad data, it reflects back what you give it just amplified and automated. Amazon learned this the hard way. They built an AI to screen resumes and make hiring faster. Sounds great, right? Except the system taught itself to downgrade resumes that included the word women's. Like women's chess club captain. Why? Because it learned from 10 years of hiring data, and tech had historically hired more men. The AI wasn't trying to be sexist. It was just pattern matching based on the past. Here's an even scarier one. Some hospitals started using AI to figure out which patients needed extra care. The system used cost of care as a proxy for health needs. Since our healthcare system has historically spent less money on black patients, the AI concluded they needed less help. Same illness, same severity, different recommendation based on race, all because of a badly chosen data point. The hard truth? These systems mirror our past decisions, both good and bad. When we don't actively work to fix old problems, AI makes them worse fast. You might be thinking, jim, I run a small business. This big tech stuff doesn't apply to me. Think again. If you use any automated system for decisions about people, you're in this game. That applicant tracking system that's filtering resumes, it might be screening out stay at home parents returning to work. Your Facebook ad targeting, it could be excluding entire communities. Without you knowing. That customer service chatbot. If it only understands standard American English, you're leaving behind a whole subset of customers. This isn't just about doing the right thing, though that matters. It's about trust, reputation, and avoiding expensive lawsuits. One viral TikTok about your biased AI could tank years of brand building. One more reason I don't condone the prompt copy publish mindset used by most of these ex AI gurus trying to sell you on their automate everything business nonsense. The good news is that you don't need a computer science degree to fix this stuff. You just need a plan. Start by looking at where your data is coming from, because if it's biased going in, it's going to be biased coming out. Then make sure you're testing across a diverse range of users, not just the people in your immediate circle. Fresh perspectives matter, and don't be afraid to ask questions. If your AI can't explain its decision, that's a problem. Transparency matters. So does accountability. Always know who's responsible for the outcomes, because in the end, someone has to own it. Regulation is already in motion. The EU's AI act set the tone, and now countries like the US, Canada and others across Asia are drafting their own versions. Bias audits won't just be a nice to have, they'll be the law. And the penalties? Let's just say they'll make those early fines look like pocket change. Smart businesses are getting ahead of this now. Not because they have to, but because customers are starting to ask hard questions about how decisions get made and and you should be too. The one thing you can remind yourself to do to get closer to feeling more confident is to check the citations. It's worked for Wikipedia since the beginning, and it will work for you too. Want to see how all this comes together in real time? Join me inside my Slack community. It's where I'm sharing live demos, behind the scenes workflows, and honest conversations about what's actually working with AI. Whether you're building a product, running a team, or launching something creative, this this is where we figure it out together. It's collaborative, actionable, and a whole lot more accessible than hiring me or my team directly. Thanks so much for tuning in today. This stuff really matters and I'm glad you're here for it. The more we learn, test and build together, the more we shape what AI becomes next. So let's keep pushing the limits of what's possible, one prompt at a time. I'll catch you in the next episode.
