Bias Remorse

We’ve all been there. It's 11:48 p.m. You've had some wine and you’re six YouTube videos deep into “life-changing gadgets you never knew you needed.” Suddenly, you spot a foldable treadmill, a tactical flashlight, and a jumper that looks like a crisp packet. One click later, the order is placed. It ships. It arrives. Only then do you realise this was a terrible, terrible decision.

Buyer’s remorse. That creeping, soul-wilting sensation that you’ve made a mistake you now have to live with. Like adopting a parrot that only knows how to swear, or buying furniture that requires a degree in Scandinavian engineering to assemble.

Now take that feeling and scale it up. You didn’t just order something daft from the internet. You trained an AI model. It looked good in testing. It did what you asked (It even had a dashboard). But then, with horrifying confidence, it starts doing things like rejecting all applicants from streets with the word “Hill” in the name or recommending only people named Colin for promotion.

Welcome to bias remorse. Like buyer’s remorse, but instead of feeling sheepish for buying glowing shoelaces, you're facing awkward meetings, reputational risk, and possibly a call from someone with "regulator" in their job title.

Bias doesn’t come clanging through the front door in chainmail. It’s quieter than that. It slinks in through overlooked spreadsheets, rushed labelling, and the fact that Steve from accounts insisted, “We’ll clean the data later.”

Bias doesn’t come clanging through the front door in chainmail. It’s quieter than that. It slinks in through overlooked spreadsheets, rushed labelling,

Here’s the rogues’ gallery of the most common offenders, although there are many others:

• Selection Bias: Like entering a pub quiz with five retired history teachers and being surprised when no one knows a single song lyric from the last 30 years. If your training data only reflects a narrow group, your model won't know what to do when someone unexpected turns up.

• Confirmation Bias: This happens when the training data tells your model exactly what it already wanted to hear. Imagine judging the success of a holiday based solely on selfies, while ignoring the four-hour airport queue and the incident with the sea urchin.

• Measurement Bias: This one’s about using the wrong stand-in to measure something important. Imagine trying to assess someone’s fitness based on how many Mr Motivator VHS tapes they own. Nostalgic? Sure. Useful? Not so much.

• Labelling Bias: This comes from human subjectivity during data labelling. One reviewer sees “assertive,” another sees “aggressive.” Your model doesn’t know the difference. It just starts thinking that anyone who uses bullet points in an email is power-hungry.

• Survivorship Bias: This shows up when you only study the stuff that worked. It’s like trying to understand how to win at poker by interviewing only the winners at the table, who conveniently forget to mention the 47 hands they lost first.

Let’s now take a scenic detour through some real-world examples of bias in AI. The names have been withheld to protect the guilty (or at least the well-lawyered).

• The CV-Sorting System That Didn't Like Women’s Colleges: A major tech retailer developed a model to rank job applicants. Unfortunately, the model was trained on ten years of successful applicants. These were, funnily enough, mostly men. It quickly learned to downgrade CVs that included the phrase “women’s chess club” or anything remotely related. The project was quietly shelved after someone probably asked why all the top candidates were called Greg.

• The Risk Algorithm with Strong Opinions on Bail: A tool used in the US legal system was designed to estimate how likely someone was to reoffend. It didn’t ask about background directly. It just relied on historical data that was riddled with past decisions. The result? People with similar records received wildly different scores. When questioned, the system shrugged (in machine terms) and insisted, “That’s what the data said.”

• The Credit Limit Mystery: A high-profile credit card partnership launched a system to determine customer credit limits. A number of people noticed that their spouses, who shared finances, accounts, assets, and occasionally toothbrushes, were being offered dramatically lower limits for no clear reason. One prominent tech figure’s wife received one-tenth the credit he did, despite identical financial profiles. When pressed, the issuing bank declared, “The algorithm made the decision,” as if that was helpful.

• The Healthcare Shortlist That Missed the Point: A widely used health risk tool tried to identify patients in need of extra support by looking at healthcare costs. The assumption was that those with high costs must have more complex needs. But the logic broke down fast. Costs don’t always reflect need. The result? People with serious health concerns were deprioritised because they hadn’t previously racked up expensive bills. The developers eventually admitted they had probably measured the wrong thing.

You see, bias isn’t some spooky AI ghost. It’s usually just regular human messiness that’s been vacuum-sealed into code. And once it’s running a live system, the damage can spread fast.

bias isn’t some spooky AI ghost. It’s usually just regular human messiness that’s been vacuum-sealed into code. And once it’s running a live system, the damage can spread fast.

If you're building AI or using it to make decisions, don’t wait for a scandal, complaint, or unexpected chart that makes no sense. Get ahead of it.

• • • Dig into your data. Ask annoying questions. Is this representative? Who labelled this? Did they all agree on what “assertive” meant?

    • Break your models before they break you. Run them through weird cases. Feed them nonsense. Make them uncomfortable. See what comes out.

    • Let other voices in. Not just the usual ones. Different people spot different weirdness.

    • Document what you’re doing. Make notes like a detective with a corkboard. When something goes sideways, it helps to know what decisions got you there.

Because if you don’t, and the AI starts making ridiculous decisions in your name, it won’t be long before you get that familiar, stomach-dropping sensation.

That feeling when the delivery van pulls away, the box is in your hands, and you realise you’ve made a terrible mistake.

Only this time, it’s not a neon raincoat or a novelty mug. It’s an expensive system that’s confidently wrong, and worse, someone’s asking who approved it.

So, check your data. Ask awkward questions. And maybe sleep on it before pressing “deploy.”

Only then can you successfully avoid bias remorse