Crowdsourced System Tracks AI Harms and Flaws

Writing AI Lab each week means I occasionally encounter AI models that behave badly and bizarrely. Usually, there’s nothing to be done about it, save for sharing those tales with you. But that could soon change.

A group of AI researchers has set up a crowdsourced website, Flaw Reporting for AI (FLARE-AI), for reporting and tracking AI harms. If, for example, a chatbot generates malware or a bomb-making recipe, leaks personal information, or triggers delusional thinking in users, FLARE-AI could be used to sound the alarm. The open source code behind the system allows others to verify an issue and route reports to model makers, as well as organizations like MITRE, a nonprofit that tracks problems with technical systems.

It’s a bit like Downdetector, which compiles real-time user reports for global service outages affecting things like apps and websites. The website is another step in the group’s ongoing work with AI reporting, which I first wrote about last year. Members of the group also consulted on a congressional bill announced in June, which would see the US government take a central role in tracking this kind of AI misbehavior.

“Right now, there is no centralized, accountable way to report flaws in AI systems,” says Avijit Ghosh, an artificial intelligence policy researcher at HuggingFace who co-led development of FLARE-AI with computer scientists Elaine Zhu and Shayne Longpre. The alarm system was developed in collaboration with 49 AI experts from 32 different organizations. In a paper outlining the work, the researchers argue that their initiative could prove crucial as AI is adopted more widely and as agentic systems gain greater power.

The lack of a consistent way to report AI flaws is a significant problem, they believe. “I think it’s a really good initiative,” says Jessica Ji, a researcher at the think tank Center for Security and Emerging Technology. Ji says the researchers are right to note that existing reporting mechanisms are fragmented and that AI models are black boxes.

“I’m in support of anything that makes AI more transparent,” she says. Though bugs and cybersecurity problems get a lot of attention—especially of late—Ghosh tells me that problems with AI systems span topics like psychological harm, discrimination or bias, and misinformation. He adds that different companies have different standards around such issues, which means some problems go unrecognized.

“In the absence of a coordinated disclosure system, there are no external mechanisms to enforce transparency,” Ghosh says. A spate of recent incidents involving popular AI tools shows how easily the technology can go bad. This week, a company called LayerX disclosed a way to dupe AI-infused web browsers, including OpenAI’s Atlas and Perplexity’s Comet, into vaulting their guardrails.