Tech solutionism, as identified by Moss and Metcalf,7 is the notion that all problems have tractable technical fixes. We see variants in the naming and definition of this phenomenon: the technology imperative,8 or "the underlying technocratic philosophy of inevitability",4 or even old-fashioned technocracy itself. All versions designate a confident deployment of technology to solve a non-technical problem, with costs and other drawbacks reduced to secondary consideration. A certain Tech Leader promotes a new startup, Sunshine, thus: "… by applying AI … you can both solve valuable problems and you can give people back time. You can also build their confidence in AI."6

Parents are starting to fret about lunch packs, school uniforms and schoolbooks. School leavers who have university places are wondering what freshers' week will be like. And some university professors, especially in the humanities, will be apprehensively pondering how to deal with students who are already more adept users of large language models (LLMs) than they are. They're right to be concerned. As Ian Bogost, a professor of film and media and computer science at Washington University in St Louis, puts it: "If the first year of AI college ended in a feeling of dismay, the situation has now devolved into absurdism. Teachers struggle to continue teaching even as they wonder whether they are grading students or computers; in the meantime, an endless AI cheating and detection arms race plays out in the background."

It's a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man. Get the same algorithm to generate high-resolution images of actress Lucy Liu or congresswoman Alexandria Ocasio-Cortez from low-resolution inputs, and the resulting faces look distinctly white. As one popular tweet quoting the Obama example put it: "This image speaks volumes about the dangers of bias in AI." But what's causing these outputs and what do they really tell us about AI bias?

A new report from Data and Society raises doubts about automated solutions to deceptively altered videos, including machine learning-altered videos called deepfakes. Authors Britt Paris and Joan Donovan argue that deepfakes, while new, are part of a long history of media manipulation -- one that requires both a social and a technical fix. Relying on AI could actually make things worse by concentrating more data and power in the hands of private corporations. "The panic around deepfakes justifies quick technical solutions that don't address structural inequality," says Paris. "It's a massive project, but we need to find solutions that are social as well as political so people without power aren't left out of the equation."

A new report from Data and Society raises doubts about automated solutions to deceptively altered videos, including machine learning-altered videos called deepfakes. Authors Britt Paris and Joan Donovan argue that deepfakes, while new, are part of a long history of media manipulation -- one that requires both a social and a technical fix. Relying on AI could actually make things worse by concentrating more data and power in the hands of private corporations. The Verge reports: As Paris and Donovan see it, deepfakes are unlikely to be fixed by technology alone. "The relationship between media and truth has never been stable," the report reads.