cs.CYAug 25, 2026

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

Authors: Jacy Reese Anthis, Erik Brynjolfsson, James Evans

Organizations: University of Chicago, Chicago, IL, USA · Stanford University, Stanford, CA, USA · Santa Fe Institute, Santa Fe, NM, USA

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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 method\textit{method} of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the mind\textit{mind} of an AI system, ranging from a passive tool to a humanlike "digital mind," and (iii) the morality\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.

Figures & tables

Figure 1. Summary of our conceptual framework. We identify four cognitive challenges faced by humans ( Section 5.2 ) as they wrestle with three primary dimensions of what AI means for society ( Section 5.3 ). Four boxes are shown at the top in shades of red and yellow, for each cognitive challenge. Arrows extend down to three rows for the three primary dimensions. Each row has the dimension label and a bidirectional arrow. Each end of each arrow has the label of the dimension, a small visualization (e.g., a bridge for ``top-down''), and a related participant quote.
Figure 2. The project developed data-driven theory by iterating between computational modeling and semi-structured interviews with professionals in the field of AI. Events between the first and second set of interviews that informed theory development include the releases of ChatGPT, Gemini, and Claude. The timeline is made of colored boxes and arrows, representing three sets of topic models (2021, 2023, and after the 2023 interviews). The topic models for 2021 and 2023 are shown as providing the interview questions for that year.
  Country or Region% Current Location% Original LocationOccupation% Occupation
Male (%)77%US50%30%Manager20%
Age (mean)41India14%23%Data scientist18%
Years in AI (mean)9UK9%9%Researcher16%
Years in AI (median)5South America5%5%Software engineer14%
  Eastern Europe2%5%Entrepreneur9%
  Middle East2%5%Consultant7%
Table 1. Summary of participants. Most respondents who identified as a manager or entrepreneur had prior experience in another listed occupation (e.g., data scientist).
Components of sociotechnical AI systems
Topic LabelCorpusNearest Words
ChipsNewschip chips processors implanted neuromorphic
CompaniesTwittercompanies organizations businesses leaders orgs
DevicesTwitterphones devices ipads smart_speakers appliances
GPT-4Newsgpt4 gpt3 generative chatgpt dalle
GPUsTwittergpus inferencing nvidia_gpus inference workloads
Table 2. A selection of the frames identified through k-SVD dictionary learning. Rather than raw vector form, each topic is presented with a researcher-generated label as a shorthand for its semantic content, the corpus it is drawn from, and its five nearest words from the semantic space (with a count of at least 100 in the corpus). Frames are loosely grouped into four broad categories: components, contexts, dynamics, and issues.
Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Search Terms
artificial agen*artificial assistant*artificial consc*
artificial emotion*artificial feel*artificial general intelligence*
artificial intelligence*artificial mind*artificial sentien*
autonomous carautonomous carsautonomous vehicle*
big databotbots
chatbotchatbotscomputer brain*
Appendix
Table A1. Asterisks indicate wildcard characters. Terms were also included if they began with a hashtag or if they contained no spaces between words (e.g., “#artificialintelligence”).
Name20212023GenderAgeOccupationCurrent LocationOriginal LocationYears in AI
Andrew✓✓Male60–69AdvisorUSUS20–29
Cameron✓ Male50–59ConsultantAustraliaAustralia20–29
Charles✓✓Male50–59ConsultantSingaporeSingapore30–39
Christopher ✓Male40–49ConsultantUSUK10–19
Darius✓ Male30–39Data scientistMiddle EastMiddle East10–19
Daryna✓ Female20–29Data scientistEastern EuropeEastern Europe0
Appendix
Table A2. Characteristics of participants. Names are pseudonyms. For readability, we use gendered pseudonyms that begin with the first letter of the occupation. No participants reported non-binary genders. “2021” and “2023” indicate the year(s) in which the participant was interviewed. Age, country of residence, country of birth, and years of experience in AI are sometimes de-specified to decades or regions to ensure anonymity. Most respondents who identified as a manager or entrepreneur had professional experience as another listed occupation (e.g., data scientist).
Figure A1. A selection of 36 frames drawn from the Twitter corpus measured over time by the relative frequency of up to 50 associated words: the 25 closest words with word count over 100 and the 25 closest words with word count over 1000. The frames shown were selected by the authors on the basis of general interest. Nine rows of four columns show the 36 topics and their changes over time as a line graph.
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