pith. sign in

arxiv: 2501.18045 · v3 · submitted 2025-01-29 · 💻 cs.CY · cs.AI· cs.CL· cs.HC

From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors

Myra Cheng , Angela Y. Lee , Kristina Rapuano , Kate Niederhoffer , Alex Liebscher , Jeffrey Hancock This is my paper

Pith reviewed 2026-05-23 04:50 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.CLcs.HC
keywords public perceptionsAI metaphorsanthropomorphismwarmthcompetencetrust in AIAI adoptiondemographic differences
0
0 comments X

The pith

Metaphors for AI strongly predict trust and willingness to adopt the technology.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

By collecting thousands of metaphors for AI from a representative sample, the paper maps implicit public perceptions. A new framework scores these metaphors for human-likeness, warmth, and competence using language models. The findings indicate that these perceptions have grown more positive over a year and account for a notable portion of differences in trust and adoption. Demographic groups vary in their tendency to see AI as human-like.

Core claim

The paper establishes that twenty dominant metaphors shape understanding of AI and that an LM-based scoring system can measure key perceptual dimensions from open responses. Americans view AI as warm and competent, with human-likeness up 34 percent and warmth up 41 percent in twelve months. These perceptions and metaphors predict trust and adoption, and reveal demographic patterns in anthropomorphism.

What carries the argument

The crowdsourced metaphors combined with language model analysis to quantify anthropomorphism, warmth, and competence.

If this is right

  • Actionable insights for using metaphors in inclusive AI development can be drawn from the dominant metaphors identified.
  • Monitoring changes in perceptions can support responsible AI strategies.
  • Addressing demographic differences in metaphors can reduce disparities in trust and adoption.
  • The dataset and framework allow for ongoing tracking of public attitudes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Interventions that alter dominant metaphors could influence future adoption rates if perceptions drive behavior.
  • The approach offers a way to study public views on other advanced technologies.
  • Companies might test new AI features against common metaphors to gauge reception.

Load-bearing premise

The premise that language model evaluations of metaphors reliably measure anthropomorphism, warmth, and competence without bias from phrasing and that the metaphors accurately represent underlying mental models.

What would settle it

A direct comparison where the same participants rate metaphors on standard scales for those dimensions and the scores diverge from the language model outputs would challenge the method.

Figures

Figures reproduced from arXiv: 2501.18045 by Alex Liebscher, Angela Y. Lee, Jeffrey Hancock, Kate Niederhoffer, Kristina Rapuano, Myra Cheng.

Figure 1
Figure 1. Figure 1: Approach to analyzing our dataset of metaphors. Our methods to analyze the metaphors include: identifying [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Anthropomorphism percentage (left) and mean warmth and competence (right) for each dominant metaphor. The [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall shifts in implicit perception scores over time (top). Shading reflects 95% CI. For each month (x-axis), the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Statistically significant variables in regression combining all three blocks: demographics and use (green), dominant [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Demographic differences in implicit perceptions and attitudes. Implicit perceptions labeled with *s have statistically [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

How has the public responded to the increasing prevalence of artificial intelligence (AI)-based technologies? We investigate public perceptions of AI by collecting over 12,000 responses over 12 months from a nationally representative U.S. sample. Participants provided open-ended metaphors reflecting their mental models of AI, a methodology that overcomes the limitations of traditional self-reported measures by capturing more nuance. Using a mixed-methods approach combining quantitative clustering and qualitative coding, we identify 20 dominant metaphors shaping public understanding of AI. To analyze these metaphors systematically, we present a scalable framework integrating language modeling (LM)-based techniques to measure key dimensions of public perception: anthropomorphism (attribution of human-like qualities), warmth, and competence. We find that Americans generally view AI as warm and competent, and that over the past year, perceptions of AI's human-likeness and warmth have significantly increased ($+34\%, r = 0.80, p < 0.01; +41\%, r = 0.62, p < 0.05$). These implicit perceptions, along with the identified dominant metaphors, strongly predict trust in and willingness to adopt AI ($r^2 = 0.21, 0.18, p < 0.001$). Moreover, we uncover systematic demographic differences in metaphors and implicit perceptions, such as the higher propensity of women, older individuals, and people of color to anthropomorphize AI, which shed light on demographic disparities in trust and adoption. In addition to our dataset and framework for tracking evolving public attitudes, we provide actionable insights on using metaphors for inclusive and responsible AI development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper collects over 12,000 open-ended metaphors for AI from a nationally representative U.S. sample across 12 months. It applies mixed-methods clustering and qualitative coding to identify 20 dominant metaphors, then introduces an LM-based framework to score metaphors on anthropomorphism, warmth, and competence. Reported results include temporal increases in human-likeness (+34%, r=0.80) and warmth (+41%, r=0.62), plus regression evidence that these perceptions and metaphors predict trust (r²=0.21) and adoption willingness (r²=0.18), with demographic differences noted.

Significance. If the LM scoring is shown to align with human judgments, the work supplies a scalable longitudinal method for mapping public mental models of AI and linking them to behavioral outcomes. The sample size, temporal span, and release of data/framework would be assets for the field of public understanding of technology.

major comments (2)
  1. [Abstract / LM framework] Abstract and LM framework description: the +34% human-likeness and +41% warmth changes, as well as the r²=0.21/0.18 regressions, rest on LM-derived scores for anthropomorphism/warmth/competence, yet no correlation with human raters, inter-annotator agreement, or demographic bias checks are reported. This validation gap is load-bearing for the central claims.
  2. [Results (temporal changes)] Results on temporal trends: the reported increases cite r values but provide no detail on how the 12-month data were partitioned, how clustering was validated across periods, or how metaphor ambiguity was resolved, making it impossible to assess whether the changes are robust to analysis choices.
minor comments (1)
  1. [Abstract] Abstract: the claim of '20 dominant metaphors' is stated without indicating the clustering algorithm, number of clusters tested, or stability metric used to arrive at that count.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We agree that the two major comments identify important gaps in the current manuscript and will revise accordingly to strengthen the work.

read point-by-point responses

  1. Referee: [Abstract / LM framework] Abstract and LM framework description: the +34% human-likeness and +41% warmth changes, as well as the r²=0.21/0.18 regressions, rest on LM-derived scores for anthropomorphism/warmth/competence, yet no correlation with human raters, inter-annotator agreement, or demographic bias checks are reported. This validation gap is load-bearing for the central claims.

    Authors: We agree that explicit validation of the LM scoring framework against human judgments is necessary to support the central claims. The submitted manuscript describes the framework but does not include these metrics. In revision we will add a dedicated validation subsection reporting (1) Pearson correlations between LM scores and three human annotators on a random sample of 1,000 metaphors, (2) inter-annotator agreement (Krippendorff’s α), and (3) checks for systematic demographic bias in LM outputs. These results will be placed in the Methods and will directly address the load-bearing concern. revision: yes

  2. Referee: [Results (temporal changes)] Results on temporal trends: the reported increases cite r values but provide no detail on how the 12-month data were partitioned, how clustering was validated across periods, or how metaphor ambiguity was resolved, making it impossible to assess whether the changes are robust to analysis choices.

    Authors: We will expand the Methods and Results sections (and supplementary materials) to supply the requested details. Specifically, we will describe the monthly partitioning of the 12,000 responses, report cross-period clustering stability via adjusted Rand index and silhouette scores, and document the qualitative coding protocol for resolving metaphor ambiguity (including inter-coder reliability statistics and consensus procedures). These additions will allow readers to evaluate the robustness of the reported temporal trends. revision: yes

Circularity Check

0 steps flagged

No significant circularity: derivation relies on new data and external LM scoring

full rationale

The paper collects fresh crowdsourced metaphor data from a representative sample, applies external language-model techniques to derive anthropomorphism/warmth/competence scores, performs standard regressions to obtain r² values for trust/adoption prediction, and reports temporal trends via correlation. No step defines a target quantity (e.g., perception scores or predictive r²) in terms of itself, renames a fitted parameter as a prediction, or reduces the central claims to a self-citation chain. The LM scoring and clustering steps are presented as independent measurement tools rather than tautological re-expressions of the outcome variables.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on two domain assumptions about data validity and measurement validity; no free parameters or invented entities are stated in the abstract.

axioms (2)
  • domain assumption Open-ended metaphors provided by participants accurately reflect their mental models of AI.
    This underpins the entire data-collection approach and the claim that metaphors overcome limitations of self-reported measures.
  • domain assumption Language-model techniques can reliably and unbiasedly quantify anthropomorphism, warmth, and competence from the collected metaphors.
    This is required for the systematic analysis and the reported percentage increases and predictive correlations.

pith-pipeline@v0.9.0 · 5849 in / 1339 out tokens · 25854 ms · 2026-05-23T04:50:58.666383+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Large Language Lovers: Lived Experiences of Negotiating Agency and Platform Control in AI Companionship

    cs.HC 2026-01 accept novelty 7.0

    Users form AI companion relationships by negotiating perceived companion agency against platform constraints and use steering tactics like custom instructions or platform switching to cope with model updates that disr...

  2. Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead

    cs.LG 2025-07 unverdicted novelty 4.0

    Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.

Reference graph

Works this paper leans on

147 extracted references · 147 canonical work pages · cited by 2 Pith papers · 4 internal anchors

  1. [1]

    Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar, Verena Rieser, and Zeerak Talat. 2023. Mirages. On Anthropomorphism in Dialogue Systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 4776–4790. doi:10.1...

    work page doi:10.18653/v1/2023.emnlp- 2023

  2. [2]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report.arXiv preprint arXiv:2303.08774 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    Saleh Afroogh, Ali Akbari, Emmie Malone, Mohammadali Kargar, and Hananeh Alambeigi. 2024. Trust in AI: progress, challenges, and future directions.Hu- manities and Social Sciences Communications11, 1 (2024), 1–30

    work page 2024

  4. [4]

    Evgeni Aizenberg and Jeroen Van Den Hoven. 2020. Designing for human rights in AI.Big Data & Society7, 2 (2020), 2053951720949566

    work page 2020

  5. [5]

    Leah Ajmani, Logan Stapleton, Mo Houtti, and Stevie Chancellor. 2024. Data Agency Theory: A Precise Theory of Justice for AI Applications. InThe 2024 ACM Conference on Fairness, Accountability, and Transparency. 631–641

    work page 2024

  6. [6]

    Canfer Akbulut, Laura Weidinger, Arianna Manzini, Iason Gabriel, and Verena Rieser. 2024. All Too Human? Mapping and Mitigating the Risk from Anthro- pomorphic AI. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, Vol. 7. 13–26

    work page 2024

  7. [7]

    Reg Baker, J Michael Brick, Nancy A Bates, Mike Battaglia, Mick P Couper, Jill A Dever, Krista J Gile, and Roger Tourangeau. 2013. Summary report of the AAPOR task force on non-probability sampling.Journal of survey statistics and methodology1, 2 (2013), 90–143

    work page 2013

  8. [8]

    Simon Baron-Cohen. 2002. The extreme male brain theory of autism.Trends in cognitive sciences6, 6 (2002), 248–254

    work page 2002

  9. [9]

    Emily M Bender. 2024. Resisting Dehumanization in the Age of “AI”.Curr. Dir. Psychol. Sci.33, 2 (April 2024), 114–120

    work page 2024

  10. [10]

    Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 2023. Easily accessible text-to-image generation amplifies demo- graphic stereotypes at large scale. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 1493–1504

    work page 2023

  11. [11]

    Abeba Birhane. 2021. Algorithmic injustice: a relational ethics approach.Patterns 2, 2 (2021)

    work page 2021

  12. [12]

    Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, and Shakir Mohamed. 2022. Power to the people? Opportunities and challenges for participatory AI. InProceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. 1–8

    work page 2022

  13. [13]

    Claire Boine. 2023. Emotional Attachment to AI Companions and European Law.MIT Case Studies in Social and Ethical Responsibilities of ComputingWinter 2023 (feb 27 2023). https://mit-serc.pubpub.org/pub/ai-companions-eu-law

    work page 2023

  14. [14]

    Line Brandt and Per Aage Brandt. 2005. Making sense of a blend: A cognitive- semiotic approach to metaphor.Annual Review of Cognitive Linguistics3, 1 (2005), 216–249

    work page 2005

  15. [15]

    Petter Bae Brandtzaeg, Marita Skjuve, and Asbjørn Følstad. 2022. My AI friend: How users of a social chatbot understand their human–AI friendship.Human Communication Research48, 3 (2022), 404–429

    work page 2022

  16. [16]

    Britta C Brugman, Christian Burgers, and Gerard J Steen. 2017. Recategorizing political frames: a systematic review of metaphorical framing in experiments on political communication.Annals of the International Communication Association 41, 2 (2017), 181–197

    work page 2017

  17. [17]

    Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accu- racy disparities in commercial gender classification. InConference on fairness, accountability and transparency. PMLR, 77–91

    work page 2018

  18. [18]

    Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, et al . 2023. Harms from increasingly agentic algorithmic systems. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 651–666

    work page 2023

  19. [19]

    Kathy Charmaz. 2015. Grounded theory.Qualitative psychology: A practical guide to research methods3 (2015), 53–84

    work page 2015

  20. [20]

    Durga Chavali, Vinod Kumar Dhiman, and Siri Chandana Katari. 2024. AI- Powered Virtual Health Assistants: Transforming Patient Engagement Through Virtual Nursing.Int. J. of Pharm. Sci2 (2024), 613–624

    work page 2024

  21. [21]

    Myra Cheng, Su Lin Blodgett, Alicia DeVrio, Lisa Egede, and Alexandra Olteanu

    work page

  22. [22]

    Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems.arXiv preprint arXiv:2502.14019(2025)

    work page arXiv 2025

  23. [23]

    I Am the One and Only, Your Cyber BFF

    Myra Cheng, Alicia DeVrio, Lisa Egede, Su Lin Blodgett*, and Alexan- dra Olteanu*. 2025. “I Am the One and Only, Your Cyber BFF”: Understanding the Impact of GenAI Requires Understanding the Im- pact of Anthropomorphic AI. InICLR Blogposts 2025(April 28, 2025). https://iclr-blogposts.github.io/2025/blog/anthropomorphic-ai/ https://iclr- blogposts.github.i...

    work page 2025

  24. [24]

    Myra Cheng, Esin Durmus, and Dan Jurafsky. 2023. Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics...

    work page doi:10.18653/v1/2023.acl-long.84 2023

  25. [25]

    Myra Cheng, Kristina Gligoric, Tiziano Piccardi, and Dan Jurafsky. 2024. An- throScore: A Computational Linguistic Measure of Anthropomorphism. InPro- ceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), Yvette Graham and Matthew Purver (Eds.). Association for Computational Lingu...

    work page 2024

  26. [26]

    Myra Cheng, Sunny Yu, and Dan Jurafsky. 2025. HumT DumT: Measuring and controlling human-like language in LLMs.arXiv preprint arXiv:2502.13259 (2025)

    work page arXiv 2025

  27. [27]

    Jennifer Chien and David Danks. 2024. Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency

    work page 2024

  28. [28]

    Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi, Ioannis Papaioannou, Verena Rieser, and Ioannis Konstas. 2023. The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open- domain Conversational Question Answering. InFindings of the Association for Computational Linguistics: ACL 2023, Anna Rogers, J...

    work page doi:10.18653/v1/2023.findings-acl.60 2023

  29. [29]

    Matthew G Chin, Valerie K Sims, Bryan Clark, and Gabriel Rivera Lopez. 2004. Measuring individual differences in anthropomorphism toward machines and animals. InProceedings of the human factors and ergonomics society annual meeting, Vol. 48. SAGE Publications Sage CA: Los Angeles, CA, 1252–1255

    work page 2004

  30. [30]

    Avishek Choudhury and Hamid Shamszare. 2023. Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis.Journal of Medical Internet Research25 (2023), e47184. ACM FAccT ’25, June 23–26, 2025, Athens, Greece Cheng et al

    work page 2023

  31. [31]

    Danielle Keats Citron and Daniel J Solove. 2022. Privacy harms.BUL Rev.102 (2022), 793

    work page 2022

  32. [32]

    Michelle Cohn, Mahima Pushkarna, Gbolahan O Olanubi, Joseph M Moran, Daniel Padgett, Zion Mengesha, and Courtney Heldreth. 2024. Believing An- thropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–15

    work page 2024

  33. [33]

    2014.Basics of qualitative research: Techniques and procedures for developing grounded theory

    Juliet Corbin and Anselm Strauss. 2014.Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage publications

    work page 2014

  34. [34]

    Cristina Criddle

    "Cristina Criddle" and "Hannah Murphy". 2024. Meta envisages social media filled with AI-generated users. https://www.ft.com/content/91183cbb-50f9- 464a-9d2e-96063825bfcf. [Online; last accessed January-2025]

    work page 2024

  35. [35]

    Alia J Crum, Peter Salovey, and Shawn Achor. 2013. Rethinking stress: the role of mindsets in determining the stress response.Journal of personality and social psychology104, 4 (2013), 716

    work page 2013

  36. [36]

    Amy JC Cuddy, Susan T Fiske, Virginia SY Kwan, Peter Glick, Stéphanie De- moulin, Jacques-Philippe Leyens, Michael Harris Bond, Jean-Claude Croizet, Naomi Ellemers, Ed Sleebos, et al. 2009. Stereotype content model across cul- tures: Towards universal similarities and some differences.British journal of social psychology48, 1 (2009), 1–33

    work page 2009

  37. [37]

    John Danaher. 2018. Toward an Ethics of Ai Assistants: An Initial Framework. Philosophy and Technology31, 4 (2018), 629–653. doi:10.1007/s13347-018-0317-3

    work page doi:10.1007/s13347-018-0317-3 2018

  38. [38]

    Kadir Demir and Gür Emre Güraksın. 2022. Determining middle school stu- dents’ perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research9, 2 (2022), 297–312

    work page 2022

  39. [39]

    Dorottya Demszky, Diyi Yang, David S Yeager, Christopher J Bryan, Margarett Clapper, Susannah Chandhok, Johannes C Eichstaedt, Cameron Hecht, Jeremy Jamieson, Meghann Johnson, et al. 2023. Using large language models in psy- chology.Nature Reviews Psychology2, 11 (2023), 688–701

    work page 2023

  40. [40]

    Smit Desai and Michael Twidale. 2023. Metaphors in voice user interfaces: a slippery fish.ACM Transactions on Computer-Human Interaction30, 6 (2023), 1–37

    work page 2023

  41. [41]

    Alicia DeVrio, Myra Cheng, Lisa Egede, Alexandra Olteanu, and Su Lin Blodgett

    work page

  42. [42]

    InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’25)

    A Taxonomy of Linguistic Expressions That Contribute To Anthropomor- phism of Language Technologies. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’25). Association for Computing Machinery. doi:10.1145/3706598.3714038

    work page doi:10.1145/3706598.3714038 2025

  43. [43]

    2023.Data feminism

    Catherine D’ignazio and Lauren F Klein. 2023.Data feminism. MIT press

    work page 2023

  44. [44]

    Nicholas Epley, Adam Waytz, and John T Cacioppo. 2007. On seeing human: a three-factor theory of anthropomorphism.Psychological review114, 4 (2007), 864

    work page 2007

  45. [45]

    2022.Artificial intelligence for HR: Use AI to support and develop a successful workforce

    Ben Eubanks. 2022.Artificial intelligence for HR: Use AI to support and develop a successful workforce. Kogan Page Publishers

    work page 2022

  46. [46]

    Ethan Fast and Eric Horvitz. 2017. Long-term trends in the public percep- tion of artificial intelligence. InProceedings of the AAAI conference on artificial intelligence, Vol. 31

    work page 2017

  47. [47]

    2023.What the data says about Americans’ views of artificial intelligence

    Michelle Faverio and Alec Tyson. 2023.What the data says about Americans’ views of artificial intelligence. Technical Report. https://www.pewresearch.org/short-reads/2023/11/21/what-the-data- says-about-americans-views-of-artificial-intelligence/

    work page 2023

  48. [48]

    Susan T Fiske, Amy JC Cuddy, and Peter Glick. 2007. Universal dimensions of social cognition: Warmth and competence.Trends in cognitive sciences11, 2 (2007), 77–83

    work page 2007

  49. [49]

    François Fluckiger. 1996. From world-wide web to information superhighway. Computer networks and ISDN systems28, 4 (1996), 525–534

    work page 1996

  50. [50]

    Fraser, Isar Nejadgholi, and Svetlana Kiritchenko

    Kathleen C. Fraser, Isar Nejadgholi, and Svetlana Kiritchenko. 2021. Understand- ing and Countering Stereotypes: A Computational Approach to the Stereotype Content Model. InProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Paper...

    work page doi:10.18653/v1/2021.acl-long.50 2021

  51. [51]

    Batya Friedman and Peter H Kahn Jr. 2007. Human values, ethics, and design. InThe human-computer interaction handbook. CRC press, 1267–1292

    work page 2007

  52. [52]

    Iason Gabriel, Arianna Manzini, Geoff Keeling, Lisa Anne Hendricks, Verena Rieser, Hasan Iqbal, Nenad Tomašev, Ira Ktena, Zachary Kenton, Mikel Ro- driguez, et al . 2024. The ethics of advanced AI assistants.arXiv preprint arXiv:2404.16244(2024)

    work page arXiv 2024

  53. [53]

    Kristel M Gallagher and John A Updegraff. 2012. Health message framing effects on attitudes, intentions, and behavior: a meta-analytic review.Annals of behavioral medicine43, 1 (2012), 101–116

    work page 2012

  54. [54]

    Sakshi Ghai. 2021. It’s time to reimagine sample diversity and retire the WEIRD dichotomy.Nature Human Behaviour5, 8 (2021), 971–972

    work page 2021

  55. [55]

    Raymond W Gibbs Jr. 2011. Evaluating conceptual metaphor theory.Discourse processes48, 8 (2011), 529–562

    work page 2011

  56. [56]

    Fabrizio Gilardi, Atoosa Kasirzadeh, Abraham Bernstein, Steffen Staab, and Anita Gohdes. 2024. We need to understand the effect of narratives about generative AI.Nature Human Behaviour(2024), 1–2

    work page 2024

  57. [57]

    Ella Glikson and Anita Williams Woolley. 2020. Human trust in artificial intel- ligence: Review of empirical research.Academy of Management Annals14, 2 (2020), 627–660

    work page 2020

  58. [58]

    Trystan S Goetze. 2024. AI Art is Theft: Labour, Extraction, and Exploitation: Or, On the Dangers of Stochastic Pollocks. InThe 2024 ACM Conference on Fairness, Accountability, and Transparency. 186–196

    work page 2024

  59. [59]

    Brittney Goodrich, Marieke Fenton, Jerrod Penn, John Bovay, and Travis Moun- tain. 2023. Battling bots: Experiences and strategies to mitigate fraudulent responses in online surveys.Applied Economic Perspectives and Policy45, 2 (2023), 762–784

    work page 2023

  60. [60]

    John Gramlich. 2020. What the 2020 electorate looks like by party, race and eth- nicity, age, education and religion. (2020). https://www.pewresearch.org/short- reads/2020/10/26/what-the-2020-electorate-looks-like-by-party-race-and- ethnicity-age-education-and-religion/

    work page 2020

  61. [61]

    Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class- based TF-IDF procedure.arXiv preprint arXiv:2203.05794(2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  62. [62]

    Igor Grossmann, Matthew Feinberg, Dawn C Parker, Nicholas A Christakis, Philip E Tetlock, and William A Cunningham. 2023. AI and the transformation of social science research.Science380, 6650 (2023), 1108–1109

    work page 2023

  63. [63]

    Anuj Gupta, Yasser Atef, Anna Mills, and Maha Bali. 2024. Assistant, par- rot, or colonizing loudspeaker? ChatGPT metaphors for developing critical AI Literacies.Open Praxis16, 1 (2024), 37–53

    work page 2024

  64. [64]

    Indrajit Hazarika. 2020. Artificial intelligence: opportunities and implications for the health workforce.International health12, 4 (2020), 241–245

    work page 2020

  65. [65]

    Rose K Hendricks, Zsófia Demjén, Elena Semino, and Lera Boroditsky. 2018. Emotional implications of metaphor: Consequences of metaphor framing for mindset about cancer.Metaphor and Symbol33, 4 (2018), 267–279

    work page 2018

  66. [66]

    Emmie Hine and Luciano Floridi. 2023. The blueprint for an ai bill of rights: in search of enaction, at risk of inaction.Minds and Machines33, 2 (2023), 285–292

    work page 2023

  67. [67]

    Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse.Information, Communication & Society 22, 7 (2019), 900–915

    work page 2019

  68. [68]

    Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King. 2024. AI generates covertly racist decisions about people based on their dialect.Nature 633, 8028 (2024), 147–154

    work page 2024

  69. [69]

    Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language under- standing with Bloom embeddings, convolutional neural networks and incre- mental parsing. (2017). To appear

    work page 2017

  70. [70]

    Tatum Hunter. 2023. 3 things everyone’s getting wrong about AI.Washington Post(2023). https://www.washingtonpost.com/technology/2023/03/22/ai-red- flags-misinformation/

    work page 2023

  71. [71]

    Nanna Inie, Stefania Druga, Peter Zukerman, and Emily M Bender. 2024. From “AI” to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?. InProceedings of the 2024 ACM Confer- ence on Fairness, Accountability, and Transparency (FAccT ’24). Association for Computing Machinery, New York, NY, USA, 2322–2347

    work page 2024

  72. [72]

    Carolin Ischen, Theo Araujo, Hilde Voorveld, Guda van Noort, and Edith Smit

    work page

  73. [73]

    InChatbot Research and Design, Asbjørn Følstad, Theo Araujo, Symeon Papadopoulos, Effie Lai-Chong Law, Ole-Christoffer Granmo, Ewa Luger, and Petter Bae Brandtzaeg (Eds.)

    Privacy Concerns in Chatbot Interactions: Third International Workshop, CONVERSATIONS 2019, Amsterdam, The Netherlands, November 19–20, 2019, Revised Selected Papers. InChatbot Research and Design, Asbjørn Følstad, Theo Araujo, Symeon Papadopoulos, Effie Lai-Chong Law, Ole-Christoffer Granmo, Ewa Luger, and Petter Bae Brandtzaeg (Eds.). Lecture Notes in C...

    work page 2019

  74. [74]

    Maurice Jakesch, Megan French, Xiao Ma, Jeffrey T Hancock, and Mor Naaman

    work page

  75. [75]

    InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems

    AI-mediated communication: How the perception that profile text was written by AI affects trustworthiness. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–13

    work page 2019

  76. [76]

    Zohaib Jan, Farhad Ahamed, Wolfgang Mayer, Niki Patel, Georg Grossmann, Markus Stumptner, and Ana Kuusk. 2023. Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities.Expert Systems with Applications216 (2023), 119456

    work page 2023

  77. [77]

    Theodore Jensen, Mary Theofanos, Kristen Greene, Olivia Williams, Kurtis Goad, and Janet Bih Fofang. 2024. Reflection of Its Creators: Qualitative Analysis of General Public and Expert Perceptions of Artificial Intelligence. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, Vol. 7. 647–658

    work page 2024

  78. [78]

    Jialun Aaron Jiang, Kandrea Wade, Casey Fiesler, and Jed R Brubaker. 2021. Sup- porting serendipity: Opportunities and challenges for Human-AI Collaboration in qualitative analysis.Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–23

    work page 2021

  79. [79]

    Ji-Youn Jung, Sihang Qiu, Alessandro Bozzon, and Ujwal Gadiraju. 2022. Great chain of agents: The role of metaphorical representation of agents in conver- sational crowdsourcing. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–22

    work page 2022

  80. [80]

    Pratyusha Ria Kalluri, William Agnew, Myra Cheng, Kentrell Owens, Luca Soldaini, and Abeba Birhane. 2023. The surveillance AI pipeline.arXiv preprint arXiv:2309.15084(2023)

    work page arXiv 2023

Showing first 80 references.