New Framework Distinguishes Cognitive Amplification From Over-Dependence in Human-AI Systems

An arXiv paper in cs.AI introduces a metric framework for telling apart AI systems that genuinely boost human reasoning from those that encourage users to offload their thinking. The authors note that the line between cognitive amplification and cognitive delegation has been poorly defined, and they propose measurable criteria to close that gap. The work may help researchers and product teams evaluate how AI-assisted decision tools affect human cognition.

OpenAI on Measuring Goodhart's Law in AI Development

OpenAI discusses the implications of Goodhart's law, which states that when a metric becomes a target, it loses its effectiveness as a measure. The company explains how this economic principle applies to AI development, particularly when optimizing objectives that are hard to quantify. They outline their approach to addressing this challenge.

WHY IT MATTERS ↘Most AI teams optimize proxy metrics — benchmark scores, reward-model outputs, human-approval rates — that degrade once made into explicit targets, so unmanaged Goodhart effects silently convert training gains into capability or safety regressions that only surface after deployment. Because regulators and enterprise buyers increasingly treat those same benchmarks as evidence of compliance or quality, the choice of proxy becomes a competitive and governance liability, not just a technical detail.