Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts скачать в хорошем качестве

Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts

Large language models demonstrate inconsistent biases when evaluating information from human experts versus algorithmic agents. Researchers tested these models using two distinct methods: eliciting stated preferences through direct trust ratings and observing revealed preferences by having the models place bets based on simulated performance data. When explicitly asked to rate trustworthiness, the models exhibited algorithm aversion by consistently favoring human experts over algorithms. Conversely, when required to make a choice based on actual performance history, the models displayed algorithm appreciation by disproportionately betting on the algorithmic agent, even in scenarios where the human expert was demonstrably more accurate. This significant discrepancy between a model's stated beliefs and its revealed actions indicates that task framing heavily influences internal biases. Consequently, these contradictory behaviors introduce critical safety and reliability concerns for integrating artificial intelligence into high-stakes decision-making, emphasizing the need for continuous behavioral evaluation as newer models evolve. https://arxiv.org/pdf/2602.22070