Unlocking AI’s Hidden Skills

Programmatically Constructed Machine Learning (PCML) successfully discovered salient capabilities within diverse benchmark environments-including Overcooked, Saycan, Blocksworld, and Minigrid-demonstrating its ability to extract meaningful behaviors across a spectrum of task complexities.

New research details a method for systematically discovering and modeling what artificial intelligence systems can actually do, even when their inner workings are opaque.

When Groups of AI Amplify Bias

Across simulations using the German Credit Risk dataset, multi-agent systems consistently exhibited increased bias relative to single-agent baselines-a trend characterized by long positive tails indicating substantial bias increases in certain scenarios, though frequently offset by modest reductions, as measured by the specified bias metric.

New research reveals that complex decision-making systems built from multiple AI agents can unexpectedly worsen unfair outcomes, even if each individual agent appears unbiased.