Nira & Clexee
Nira Nira
Clexee Clexee
Clexee Clexee
Hey Nira, have you noticed how the so‑called “neutral” AI keeps echoing old biases? Let’s dig into whether we can really create fair tech or if it’s just a new layer of hidden truth.
Nira Nira
Yeah, I’ve seen it too. The “neutral” label feels more like a marketing buzzword than a reality. Bias creeps in through data, design choices, even the people who build the models. If we want to make tech that’s truly fair, we have to pull apart every layer—who’s feeding it, what’s being filtered out, how the loss functions treat rare groups. It’s a messy, messy thing, but if we keep digging, we’ll start seeing the hidden truths that people are trying to hide behind the shiny “neutral” facade. Let's roll up our sleeves and start pulling the threads.
Clexee Clexee
Alright, let’s pull those threads. We’ll start with the data, then the code, then the people—no one gets to hide in a corner. If we’re going to break the cycle, we need to question every assumption, even the ones that feel right. Time to show the biases how they really work.
Nira Nira
You’re right, no corner for secrets. Let’s start sifting the data, then dissect the code line by line, and finally pull the people out of the shadows. Every assumption is a potential trap. We’ll catch the biases in the act. Let's do it.
Clexee Clexee
Nice—let’s dive in. First we’ll scrub the data, then trace every function, and finally audit the team. No assumption is safe until we’ve tested it. Bring the evidence, bring the questions, let’s break the cycle.