Confirmation Bias

A good analyst should occasionally try to break their own idea. Not performatively. Not with a decorative “on the other hand” paragraph. Actually break it. Ask the kind of question that could make the current hypothesis collapse, then watch whether the evidence forces a better one. That simple discipline is the center of FalsifyBench: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games, a new paper by Leonardo Bertolazzi, Katya Tentori, and Raffaella Bernardi.1 The paper is framed around scientific reasoning, but its practical message travels well beyond science. If an AI agent cannot test outside its own current belief, it may look careful while doing something much less impressive: confirming the first plausible story it invented. ...

TL;DR for operators An AI investment assistant does not enter every portfolio discussion as a blank analyst. The paper behind this article shows that large language models can carry latent investment preferences: for certain sectors, for larger companies, and for contrarian rather than momentum arguments.1 The important mechanism is simple and uncomfortable. When buy and sell evidence are balanced, the model’s internal prior can break the tie. When counter-evidence later becomes stronger, that prior does not necessarily disappear. In mixed-evidence settings, the model may latch onto the fragment of evidence that supports its original inclination and discount the stronger opposing side. Splendid. Your “neutral” analyst has discovered confirmation bias and brought it to the investment committee. ...