Method, Mind, and Morality: How People Make Sense of Artificial Intelligence

Jacy Reese Anthis, Erik Brynjolfsson, James Evans
8/25/2026
cs.CYcs.AIcs.CL

No confident code implementation found for this paper.

Abstract

How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts grounded in 57 semi-structured interviews with AI professionals in 2021 and 2023--before and after the recent surge of public interest. We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts. We develop a framework of three primary debates across which frames are adopted and contested: (i) the $\textit{method}$ of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the $\textit{mind}$ of an AI system, ranging from a passive tool to a humanlike "digital mind," and (iii) the $\textit{morality}$ of how AI is used, particularly the decision of whether to slow down or speed up AI development. As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{anthis2026method,
  title  = {Method, Mind, and Morality: How People Make Sense of Artificial Intelligence},
  author = {Jacy Reese Anthis and Erik Brynjolfsson and James Evans},
  year   = {2026},
  eprint = {2608.24748},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2608.24748v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2608.24748.svg)](https://codeofpaper.com/paper/2608.24748)

Discussion