CHAPTER II
Trust the Data

Two weeks passed, and Mike heard nothing about his report. He tried to focus on his regular work—optimizing ad placement algorithms, fine-tuning recommendation engines—but his mind kept drifting back to those loan applications.

Lisa found him in the break room, staring at the coffee machine like it held the secrets of the universe.

"Still thinking about the loan algorithm?" she asked.

"I can't help it. I keep seeing those rejection letters. Real people with real dreams being told no by a machine that doesn't even know them."

Lisa poured herself a cup of coffee. "Have you considered that maybe the algorithm is right? Maybe those neighborhoods do have higher default rates, and the algorithm is just protecting the bank's interests."

"That's exactly the problem. The algorithm is protecting the bank's interests, not the people's. And the reason those neighborhoods have higher default rates is because banks have been denying them loans for decades. It's a self-fulfilling prophecy."

Lisa considered this. "So what are you going to do?"

"I'm going to run my own analysis. Compare the algorithm's decisions with actual outcomes, not just predictions. See if there's a gap between what the algorithm thinks will happen and what actually happens."

"That sounds like a lot of work."

"It is. But if I'm right, it could change everything."

Mike spent the next month working late into the night, gathering data from public records, cross-referencing loan applications with actual repayment rates. The results were worse than he had imagined.

The algorithm wasn't just reflecting historical bias—it was amplifying it. Qualified applicants from certain neighborhoods were being rejected at rates 40% higher than equally qualified applicants from other areas. And when those rejected applicants managed to get loans elsewhere, they repaid them at the same rate as everyone else.

The algorithm wasn't predicting risk. It was creating it.

Mike compiled his findings into a report and submitted it through the official channels. He expected pushback, but he wasn't prepared for what came next.

A meeting invitation appeared in his calendar:
"Urgent: Algorithm Review with Dr. Chen and Legal Team."

The subject line made his stomach sink. This wasn't a discussion. It was a defense.

— To Be Continued —

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