We already know that LLMs pick up human biases from their training data. New research suggests they can also develop their own biases from experience—and stereotype job applicants more than humans do.

As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.

Read the full story on AI’s alarming potential to stereotype job applicants.

—Michelle Kim

The risk of weather data sabotage is rising

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on weather forecasts. More recently, the forecasts have become relevant for another industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.

The temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk.

As experts in the field, we can foresee scenarios where the risks snowball into far bigger, more systemic problems.

Find out why the threats to weather data are growing—and how to stay ahead of them.