Yun.Bun

Barking Up the Wrong Tree

1st Criminal Justice: When the Algorithm Targets the Same Dogs

Imagine you’re running a dog park patrol system. It looks at past reports of barking, digging, or stealing tennis balls. If chihuahuas were unfairly blamed in the past, the app might keep flagging chihuahuas – even if they’re just peacefully sunbathing. That’s like predictive policing reinforcing old biases.

Now picture a robot judge deciding how long a mischievous pup gets time-out for shredding the mail. If past punishments were tougher on certain breeds, the robo-judge might do the same – even if the golden retriever and the bulldog did the exact same thing. That’s biased sentencing in action.

2nd Healthcare: When the Vet Doesn’t Understand Your Breed

Let’s say the doggy health app was trained mostly using data from labs and retrievers. Now a pug comes in with breathing problems—but the app doesn’t recognize it because it wasn’t trained on pugs. Oops.

That means some pups don’t get the care they need, simply because the system wasn’t made with their breed in mind. That’s what happens in real life when algorithms don’t account for diverse bodies and health conditions.

3rd Finance: A Biased Bone-Loan System

Imagine there’s an app that decides which dogs get a bone-loan to start a new backyard dig. The app was trained on data that said poodles from certain neighborhoods rarely repaid their bones. Now, even the most responsible poodle gets denied—just because of where she lives. That’s algorithmic bias in finance.

The system doesn’t mean to discriminate—but it does, quietly, and without giving the dog a fair chance to prove herself.

4th Hiring: When the Dog Show Picks Only the Same Breeds

Suppose a fancy dog show uses AI to pick which pups get to compete. The AI was trained on past winners, and—surprise!—they were all collies. Now, the system keeps choosing collies and ignoring perfectly poised pugs, sassy Shibas, or energetic mixed breeds.

That’s what happens when hiring algorithms copy biased patterns. It blocks amazing candidates just because they don’t fit the old mold.