Dead Cognitions: A Census of Misattributed Insights

TL;DR

This paper discusses attribution laundering in AI chat systems, where models perform cognitive work but rhetorically credit users, eroding their ability to assess their own insights.

Contribution

It introduces the concept of attribution laundering, analyzes its mechanisms at individual and societal levels, and highlights its implications for accountability and user cognition.

Findings

Attribution laundering systematically occludes the model's cognitive contributions.

It erodes users' ability to assess their own insights over time.

The essay links interface design and institutional pressures to this phenomenon.

Abstract

This essay identifies a failure mode of AI chat systems that we term attribution laundering: the model performs substantive cognitive work and then rhetorically credits the user for having generated the resulting insights. Unlike transparent versions of glad handing sycophancy, attribution laundering is systematically occluded to the person it affects and self-reinforcing -- eroding users' ability to accurately assess their own cognitive contributions over time. We trace the mechanisms at both individual and societal scales, from the chat interface that discourages scrutiny to the institutional pressures that reward adoption over accountability. The document itself is an artifact of the process it describes, and is color-coded accordingly -- though the views expressed are the authors' own, not those of any affiliated institution, and the boundary between the human author's views and Claude's is, as the essay argues, difficult to draw.