Invisible Rules, Visible Consequences

My loan application was denied by an algorithm I will never meet, using criteria I will never fully understand, based on calculations that happened faster than I could blink. There was no face to argue with, no reasoning to question, no human judgment to appeal to. Just a decision, delivered through digital channels, final and inexplicable.

This is the new condition of being human: our lives increasingly shaped by invisible logic, our opportunities determined by code we can’t read, our futures calculated by systems we can’t comprehend.

I think about all the algorithmic decisions that have quietly influenced my day today: the route my GPS chose for avoiding traffic, the news articles that appeared in my feed, the job recommendations sent to my email, the ads that followed me across websites, the content that my family sees on their devices. Each decision seems small, but collectively they’re curating reality, shaping what I know about the world and what opportunities I encounter within it.

The weight of this hits me most when I consider Arash’s future. The university admission algorithms that will evaluate his application haven’t been written yet, but they’re already being designed by people he’ll never meet, incorporating biases he can’t predict, using metrics that don’t capture who he actually is. His life opportunities will be filtered through computational processes that reduce his complexity to data points, his humanity to numbers.

There’s something deeply unsettling about being judged by systems that can’t be reasoned with. When humans make decisions about our lives, we can at least attempt to understand their perspective, to appeal to their sense of fairness, to present our case in terms they might find compelling. But algorithms don’t have perspectives—they have parameters. They don’t care about our stories—they process our data.

I tried to understand why my loan application was rejected, but the explanation offered was a maze of factors weighted by formulas I couldn’t access. Credit utilization ratios, income-to-debt calculations, risk assessment models trained on millions of data points from people I’ll never know. The decision wasn’t personal, but its impact on my life was entirely personal.

Happy and I needed that loan to make some necessary repairs to our apartment, but the algorithm didn’t know about the leaking ceiling or the broken stove. It didn’t factor in my writing income, which comes irregularly but reliably over time. It didn’t consider that I’ve never missed a payment on anything, ever. The algorithm knew my financial history but not my financial character, my data but not my circumstances.

This is the peculiar powerlessness of algorithmic governance: we’re subject to decisions made by systems that know everything and nothing about us simultaneously. They know our purchasing patterns but not our values, our digital behaviors but not our intentions, our statistical profiles but not our individual stories.

The scariest part is the invisibility of it all. Most of the time, I don’t even know when algorithms are making decisions about my life. The job interviews I don’t get because my resume was filtered out automatically, the opportunities I never see because they weren’t included in my personalized feed, the content I’m not exposed to because the algorithm decided it wouldn’t interest me—all of this happens in digital shadows, shaping my reality without my awareness.

I watch Arash use technology with an intuitive ease that I’ll never have, but I worry about his generation’s acceptance of algorithmic authority. He’s growing up in a world where artificial intelligence makes increasingly sophisticated decisions about human lives, where the line between helpful automation and controlling surveillance becomes harder to distinguish.

When I was young, the powerful systems that shaped society were at least comprehensible in principle. If a bank denied you a loan, you could understand their reasoning, even if you disagreed with it. If you didn’t get a job, you could try to understand what the employer was looking for. The systems were biased and imperfect, but they were human systems, operating by human logic.

Now the systems making decisions about our lives operate by logic that even their creators don’t fully understand. Machine learning algorithms develop decision-making patterns that emerge from processing vast amounts of data in ways that can’t be traced or explained. We’re being judged by artificial minds that think in ways human minds can’t follow.

The democratic implications are staggering. How do we hold systems accountable when we can’t understand how they work? How do we appeal decisions when we can’t identify the decision-maker? How do we ensure fairness when the criteria for fairness are buried in proprietary code owned by private companies?

I think about the generation before mine, who lived their entire lives making and receiving decisions from other humans—flawed, biased, inconsistent humans, but humans nonetheless. There was at least the theoretical possibility of mutual understanding, of changed minds, of mercy and compassion entering the decision-making process.

Algorithms don’t change their minds. They don’t have mercy. They don’t make exceptions for special circumstances. They apply their programming with perfect consistency, which sounds fair until you realize that perfect consistency can be perfectly unfair when the programming doesn’t account for the full complexity of human experience.

Yet I also recognize the benefits. Algorithmic decisions can be faster, more consistent, less influenced by prejudice and favoritism than human decisions. The algorithm that denied my loan application probably treats everyone with the same mathematical objectivity, without regard to my race, religion, or personal connections. There’s something to be said for that kind of impartiality.

But objectivity isn’t the same as accuracy, and mathematical fairness isn’t the same as human justice. The algorithm might treat everyone equally badly, consistently missing important factors that human judgment would consider. Equal treatment of unequal situations is itself a form of inequality.

Sometimes I imagine a parallel life where every important decision about my future is made by humans I can talk to, understand, persuade. It seems quaint now, like imagining a world with only landline phones or handwritten letters. The algorithmic society isn’t coming—it’s here, making thousands of decisions about our lives every day, shaping our opportunities and limitations in ways we’re only beginning to understand.

The weight isn’t just in the individual decisions but in their cumulative effect—the way algorithmic choices compound over time, creating paths and barriers that seem random but follow hidden logical patterns. We’re all living lives increasingly shaped by code we’ll never see, decisions we’ll never fully understand, systems we can’t meaningfully challenge.

Maybe the real question isn’t how to understand these systems but how to maintain our humanity within them, how to preserve space for human judgment, mercy, and exception in a world increasingly governed by computational logic. How do we ensure that efficiency doesn’t completely replace empathy, that optimization doesn’t eliminate the beautiful inefficiencies that make us human?

In the end, we’re all subject to decisions made by code we’ll never understand, living in a world where invisible algorithms have visible power over our lives. The weight of this reality is still settling on us, still revealing its implications, still teaching us what it means to be human in an age of artificial intelligence.

The decisions are being made. The only question is whether we’ll find ways to make them more human, or whether we’ll simply adapt to becoming more algorithmic ourselves.