#ai-bias

#ai-bias

Its imperfections are human, visible, and correctable. You can see who edited what, when, and why. Grokipedia is its antithesis. It replaces deliberation with automation, transparency with opacity, and pluralism with personality. Its "editors" are algorithms trained under Musk's direction, generating rewritten entries that emphasize his favorite narratives and downplay those he disputes. It is a masterclass in how not to make an encyclopedia, a warning against confusing speed with wisdom.

"We're like, 'Show us a picture of a senior leadership team from Chanel visiting Microsoft'-it is all men in suits," she said.

Researchers have analysed hundreds of thousands of images from places like IMDb and Google Image Search as well as the kinds of online texts used to train large language models. They found a persistent age gap between men and women when it comes to how they are shown and talked about online.

AI is able to build things in minutes or even seconds that would take humans hours to do. But its biggest strength is also its biggest flaw. AI is, at its core, a tool used to imitate human knowledge in an advanced capacity. So while it's able to produce work faster, it also inherits all our biases. It's important to address these biases when building products to ensure that what we're developing can be used by everyone.

This kind of age-related gender bias has been seen in other studies of specific industries, and anecdotally . . . but no one has previously been able to examine this at such scale,

Generative AI is moving from drafting emails to shaping labor markets. On platforms like Fiverr, Freelancer.com, and Upwork, millions of workers rely on hourly rates to compete for jobs. As AI increasingly influences pricing recommendations, business leaders face a critical question: Do large language models (LLMs) make these pricing decisions fairly? Or do they perpetuate the same biases and inequities that have long plagued human labor markets?

"I scraped millions of Google Maps restaurant reviews, and gave each reviewer's profile picture to an AI model that rates how hot they are out of 10," says San Francisco-based website creater Riley Walz. "This map shows how attractive each restaurant's clientele is. Red means hot, blue means not."

"The findings, detailed in a preprint paper titled "AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights," further amplify persistent concerns about AI bias. Authors Jiannan Xu, a PhD candidate at the University of Maryland, Gujie Li, assistant professor at the National University of Singapore, and Jane Yi Jiang, assistant professor at Ohio State University, found that "LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled."

The goal of the Ethical AI Guidebook is to spark a mindset shift-encouraging content creators, agencies, and AI developers to make inclusivity a core part of how they generate and use imagery.

As opposed to investigating deepfake detection models, the study looks more broadly at AI models used for "fake news detection." This highlights systemic biases in technology.