Seeing the Goal, Missing the Truth: Human Accountability for AI Bias

ArXi:2602.09504v2 Announce Type: replace-cross This research explores how human-defined goals influence the behavior of Large Language Models (LLMs) through purpose-conditioned cognition. Using financial prediction tasks, we show that revealing the downstream use (e.g., predicting stock returns or earnings) of LLM outputs leads the LLM to generate biased sentiment and competition measures, even though these measures are intended to be downstream task-independent.