Machines Make Decisions Now
We used to think of algorithms as tools. They sorted lists, predicted weather, helped with spelling. That changed fast. Now they make decisions—real ones.
Who gets a loan. Who gets a job interview. What news people see. Which criminal defendant gets labeled a risk. These aren’t just calculations. They shape lives.
So when algorithms go wrong, the damage can be deep.
One Click, Real Consequences
In 2020, the UK used an algorithm to assign A-level results when exams were canceled due to the pandemic. It downgraded students from poorer schools and upgraded those from wealthier ones. The result? Outrage. Protests. Careers potentially derailed. It was reversed, but the impact lingered.
This wasn’t some obscure backend error. This was a choice made at scale. And it wasn’t the only one.
Bias Doesn’t Come from Nowhere
Algorithms learn from data. But data reflects society. And society isn’t neutral. If historical hiring data shows men getting most tech jobs, the algorithm might learn that pattern and continue it. That’s not learning. That’s repeating bias.
Amazon tried to build an AI tool to review resumes. It penalized candidates with the word “women’s” in their applications, like “women’s chess club.” Why? Because its training data came from ten years of male-dominated hiring.
They scrapped it.
But this pattern isn’t rare. Predictive policing tools often point to neighborhoods already over-policed. Facial recognition systems misidentify people of color more often than white faces. These aren’t quirks. They’re warnings.
What Happens When AI Makes the Call?
AI in healthcare looks promising. Faster diagnoses. Smarter drug discovery. But risk follows close behind.
In one case, a system used to allocate extra healthcare to high-risk patients ended up favoring white patients. Why? It used past healthcare spending as a proxy for need. But historically, Black patients often received less care. So they appeared “lower risk.” The algorithm wasn’t racist. But its inputs were soaked in inequality.
Now imagine AI systems used in war zones. Or in autonomous vehicles. Or in deciding who gets parole. Mistakes here can’t be undone with a refund or an apology.
AI Ethics Isn’t Optional
Some developers treat ethics as something to address later. But the longer you delay, the harder it is to fix. By the time a product scales, values are already baked in.
Ethics has to be part of the process. Not a patch.
- Testing datasets for bias early
- Auditing outputs with real human review
- Hiring ethicists alongside engineers
- Giving users transparency and control
Tools like Google’s PAIR and IBM’s AI Fairness 360 try to make fairness visible. They help identify where models behave differently for different groups.
But awareness isn’t enough. Companies need pressure. That means regulation, watchdogs, and clear liability.
Personal Accountability Still Matters
Engineers often say, “The model did it.”
But models don’t code themselves. They don’t choose their training sets. They don’t decide which features to prioritize. Humans do.
So when a recruitment model weeds out good candidates unfairly, the blame isn’t abstract. It’s not “the algorithm.” It’s the people and systems behind it.
We don’t accept “it was the spreadsheet” as an excuse when money is mismanaged. We shouldn’t accept “it was the algorithm” either.
The Black Box Problem
One major issue is transparency. Many modern algorithms—especially deep learning models—are black boxes. They produce answers, but we can’t always say how they got there.
That’s a problem.
If a judge uses a tool to determine sentencing risk, the defendant should have the right to question it. But if no one can explain the model’s reasoning, how can anyone challenge it?
Efforts like Explainable AI (XAI) aim to fix this. But it’s a slow road. And until explainability improves, we shouldn’t let AI make unreviewable decisions.
