What if AI requires so much computation that it becomes unaffordable?The fear: Training ever more capable models will become too pricey for all but the richest corporations and government agencies. Rising costs will

Synthetic datasets can inherit flaws in the real-world data they’re based on. Startups are working on solutions. Generating synthetic datasets for training machine learning systems is a booming business.

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

A neural network is helping credit card users continue to shop even when the lender’s credit-approval network goes down. Visa developed a deep learning system that analyzes individual cardholders’ behavior in real time to predict whether credit card transactions should be approved or denied.

The UK government abandoned a plan to use machine learning to assess students for higher education. The UK Department of Education discarded grades generated by an algorithm designed to predict performance on the annual Advanced Level qualifications, which had been canceled due to the pandemic.

Marketers are using computer vision to parse customers by skin color and other perceived racial characteristics. A number of companies are pitching race classification as a way for businesses to understand the buying habits of different groups.

A major retailer’s AI-powered surveillance program apparently targeted poor people and minorities. Rite-Aid, a U.S.-based pharmacy chain, installed face recognition systems in many of its New York and Los Angeles stores.

Bias arises in machine learning when we fit an overly simple function to a more complex problem. A theoretical study shows that gradient descent itself may introduce such bias and render algorithms unable to fit data properly.

MIT withdrew a popular computer vision dataset after researchers found that it was rife with social bias. Researchers found racist, misogynistic, and demeaning labels among the nearly 80 million pictures in Tiny Images, a collection of 32-by-32 pixel color photos.

A new model designed to sharpen images tends to turn some dark faces white, igniting fresh furor over bias in machine learning. Photo Upsampling via Latent Space Exploration (Pulse) generates high-resolution versions of low-resolution images.

Baidu backed out of a U.S.-led effort to promote ethics in AI, leaving the project without a Chinese presence. The Beijing-based search giant withdrew from the Partnership on AI, a consortium that promotes cooperation on issues like digital privacy and algorithmic bias.

Three of the biggest AI vendors pledged to stop providing face recognition services to police — but other companies continue to serve the law-enforcement market.

AI learns human biases: In word vector space, “man is to computer programmer as woman is to homemaker,” as one paper put it. New research helps language models unlearn such prejudices.

Machines took another step toward doing the work of journalists. Microsoft laid off dozens of human editors who select articles for the MSN news service and app. Going forward, AI will do the job.

Facebook’s leadership has thwarted changes in its algorithms aimed at making the site less polarizing, according to the Wall Street Journal. The social network’s own researchers determined that its AI software promotes divisive content.