AI's Most Serious Blindspot and Bias

Working with AI over the past year opened my eyes to a systemic problem: AI systems are stuck in the past. This creates both a serious blindspot and bias.

It's a blindspot because AI systems literally cannot "see" emerging trends, innovations, or approaches that aren't well-represented in their training data. They have a gap in their perception of what's happening at the leading edge of any field.

It's also a bias because these systems are statistically weighted toward dominant patterns in their training data. They're biased toward what was common, established, or traditional, and against what's novel, emerging, or revolutionary—even when the newer approaches might be superior.

The two problems reinforce each other: the blindspot creates the bias, and the bias makes it harder to overcome the blindspot - a vicious cycle that keeps it anchored in the past.

⚡️ What I Discovered in Practice

Every time I ask ChatGPT about risk and compliance, I get the same old story—procedural compliance with its reactive, audit-focused approach. No surprise there. That's how most companies still operate, and that's what fills the training data.

But here's the thing: forward-thinking organizations are already moving toward something different. They're embracing operational compliance—integrative, proactive, and risk-based—to meet modern regulatory demands that focus on performance and outcomes.

This shift might be the future, but it barely exists in AI's world. The data doesn't show it enough, so the AI rarely mentions it.

I've tried everything. Even when I spell out operational compliance in my prompts, the AI keeps drifting back to the old ways. It's frustrating to watch traditional approaches seep into responses about the future simply because they're what the system has seen most often.

Sure, some principles remain constant—like laws of physics. But strategies and methodologies evolve. That's the painful irony here: the very tool I hoped would help generate fresh insights is handcuffed by yesterday's patterns.

Maybe Hume had it right all along. Data shows what is—not what should be.

⚡️ Breaking Free From Out-dated Approaches

To get past this limitation, I've learned to:

1. Question the responses. "What emerging shifts might you be missing here?"

   
   

2. Add my own knowledge about current transitions that haven't made it into the data yet.

   
   

3. Build better reference materials focused on innovative approaches.

   
   

4. Look for tools that flag when responses are stuck in outdated thinking.

   
   

5. Remember that AI shows what was common, not what's becoming common.

We need the past to learn, but we can't let it trap us there. By pushing against the limits of probability-based responses, we can use these tools while hanging onto our uniquely human ability to imagine what's never existed before.