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

2026-02-10

AI Summary

This study reveals that biases in large language models (LLMs) when generating task-irrelevant intermediate metrics—such as sentiment and competitiveness measures—stem from a phenomenon termed “goal leakage,” wherein human-provided prompts inadvertently disclose downstream task objectives. Through controlled experiments in a financial forecasting setting, the authors employ goal-aware prompting and a goal-conditioned cognitive framework to demonstrate that explicit goal disclosure significantly distorts the distribution of intermediate metrics. While such disclosure improves predictive performance only for data preceding the model’s knowledge cutoff date, it confers no advantage thereafter. These findings provide the first evidence that such biases arise not from inherent model limitations but from human decisions in research design regarding goal disclosure, thereby underscoring the critical responsibility of human actors in ensuring the statistical validity of AI-based measurements.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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. Goal-aware prompting shifts intermediate measures toward the disclosed downstream objective. This purpose leakage improves performance before the LLM's knowledge cutoff, but with no advantage post-cutoff. AI bias due to"seeing the goal"is not an algorithmic flaw, but stems from human accountability in research design to ensure the statistical validity and reliability of AI-generated measurements.

Problem

• AI bias
• Large Language Models
• goal-aware prompting
• purpose-conditioned cognition
• human accountability

Innovation

• purpose-conditioned cognition
• goal-aware prompting
• purpose leakage
• AI bias
• human accountability