AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.
Novel technique overcomes spurious correlations problem in AI
Reader’s Picks
-
When Grandma and Grandpa are in charge, the children are likely staring at a screen—a long-standing parental complaint now supported [...]
-
Researchers at the University of Tsukuba have demonstrated that intensified environmental variability (EV) can promote the evolution of cooperation through [...]
-
Using Major League Baseball as a case study, Cornell research highlights potential shortcomings in diversity metrics that could obscure inequities [...]