Pith. sign in

REVIEW 2 major objections 1 minor 51 references

Beliefs and Misconceptions around Integrated Conversational AI

T0 review · 2 major / 1 minor · reviewed 2026-06-30 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Citations in integrated conversational AI raise perceived trustworthiness without prompting users to verify the sources.

desk verdict Small qualitative study on Edge Copilot finds citations boost trust without much checking, but n=20 in controlled tasks limits how far the results travel. read the letter →

arxiv 2605.14849 v1 pith:35MPBQPZ submitted 2026-05-14 cs.HC

William Seymour , Adam Jenkins , Mark Cote , Jose Such This is my paper · ORCID
classification cs.HC
keywords conversationalAIusertrustcitationsfact-checkingintegratedpromptingstrategiesLLMperceptionsbrowserextension
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper investigates how users build understanding and decide on trust when conversational AI is embedded directly into a web browser. In a study of 20 participants completing information retrieval and planning tasks with Copilot in Microsoft Edge, users combined prior views of LLMs with search engine habits to shape their prompting and evaluation. A central finding is that visible citations made answers seem more reliable, yet participants rarely felt compelled to inspect those citations themselves. When they did check facts, they frequently turned to the exact sources the AI had already referenced. This pattern shows how integration can quietly align user verification behavior with the system's own outputs.

What carries the argument

The controlled user study of 20 participants performing information-retrieval and planning tasks inside a browser extension, which links beliefs about answer generation to prompting choices and citation-driven trust.

What would settle it

A follow-up observation in which users in uncontrolled settings independently verify citations or select different sources than those listed by the AI would undermine the reported trust and fact-checking pattern.

Watch

Extended reading notes

Core claim

Participants relied on a combination of existing perceptions of LLMs and internet search, tracing the effect of beliefs about how Copilot generated answers on prompting strategies. The inclusion of citations increased the trustworthiness of answers without participants feeling the need to check them, with participants often reaching for the same information sources as the CAI when fact-checking.

Load-bearing premise

Observations from 20 participants in controlled tasks inside one browser extension will generalize to everyday real-world use of integrated conversational AI.

Editorial extensions

If this is right

  • Including citations in AI responses can raise user acceptance of outputs even when verification does not occur.
  • Users may mirror the source selection of the integrated AI during their own fact-checking.
  • Pre-existing beliefs about how LLMs work directly shape how people phrase prompts to the system.
  • Trust mechanisms in integrated AI rest on surface markers such as citations rather than on active source inspection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Designers of other embedded AI tools may see similar citation effects if the integration hides the AI's origins as effectively as a browser extension does.
  • Over-reliance on AI-listed sources could narrow the range of information people encounter when double-checking answers.
  • Future interfaces might need explicit prompts or training to encourage verification outside the AI's cited set.
  • The pattern could intensify as conversational features spread across more productivity applications beyond browsers.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper reports a qualitative study in which 20 participants performed information-retrieval and planning tasks inside the Microsoft Edge browser using its integrated Copilot feature. It claims that participants drew on prior beliefs about LLMs and web search to shape their prompting, that the presence of citations increased perceived trustworthiness without prompting verification, and that participants tended to consult the same external sources that Copilot itself referenced when they did check facts.

Significance. If the reported patterns hold beyond the specific experimental context, the work would supply useful empirical grounding for how trust and verification behaviors emerge when conversational AI is embedded in everyday tools rather than used as a standalone interface. The study design itself, however, supplies no evidence that the observed effects are stable properties of integrated CAI rather than artifacts of the single-extension, short-session, controlled-task setting.

major comments (2)
  1. [Methods / Results] The central claims about citation effects on trustworthiness and source-matching in fact-checking rest on an analysis whose protocol, coding scheme, and reliability metrics are not described. The abstract states the findings but the Methods and Results sections supply no detail on interview protocol, coding scheme, inter-rater reliability, or limitations; without these the reader cannot assess whether the reported patterns are reproducible or whether they are shaped by the particular task framing.
  2. [Discussion / Limitations] The generalizability concern is load-bearing: the study uses a single browser extension, 20 participants, and controlled information-retrieval/planning tasks. The manuscript does not discuss how the novelty of the tool, the short session length, or the participant pool might have produced prompting and verification habits that would not appear in everyday, multi-tool, long-term use of integrated conversational AI.
minor comments (1)
  1. [Introduction] The abstract and introduction use the term 'integrated conversational AI' without a precise operational definition or comparison to standalone chat interfaces; a short clarifying paragraph would help readers map the claims onto existing literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's comments. We find the feedback constructive and will revise the manuscript to address the concerns raised regarding methodological transparency and generalizability.

read point-by-point responses

  1. Referee: [Methods / Results] The central claims about citation effects on trustworthiness and source-matching in fact-checking rest on an analysis whose protocol, coding scheme, and reliability metrics are not described. The abstract states the findings but the Methods and Results sections supply no detail on interview protocol, coding scheme, inter-rater reliability, or limitations; without these the reader cannot assess whether the reported patterns are reproducible or whether they are shaped by the particular task framing.

    Authors: We agree with this assessment. The initial manuscript did not provide adequate detail on the qualitative analysis process. In the revised version, we will include a detailed description of the interview protocol (including the semi-structured questions used), the inductive thematic analysis approach, the coding scheme with examples, and any steps taken for reliability such as multiple coders reviewing transcripts. We will also explicitly discuss limitations related to task framing in a new Limitations section. This will allow readers to better evaluate the reproducibility and context of our findings. revision: yes

  2. Referee: [Discussion / Limitations] The generalizability concern is load-bearing: the study uses a single browser extension, 20 participants, and controlled information-retrieval/planning tasks. The manuscript does not discuss how the novelty of the tool, the short session length, or the participant pool might have produced prompting and verification habits that would not appear in everyday, multi-tool, long-term use of integrated conversational AI.

    Authors: We acknowledge that the manuscript's limitations section is underdeveloped on these points. While our study is positioned as an initial exploration of integrated CAI use, we will expand the Discussion to address how the specific context (single tool, short sessions, controlled tasks, participant demographics) may influence the observed behaviors. We will discuss potential novelty effects, the difference between lab-like sessions and naturalistic long-term use, and suggest directions for future research to test generalizability across tools and over time. This revision will better frame the scope of our claims without overstating them. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical qualitative study with direct observations

full rationale

The paper reports results from a controlled user study with 20 participants performing information-retrieval and planning tasks in Microsoft Edge Copilot. No equations, fitted parameters, derivations, or mathematical predictions appear in the abstract or described content. Central claims rest on direct participant observations rather than any self-referential reduction, self-citation chain, or renamed known result. The analysis is therefore self-contained against external benchmarks.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The study rests on standard HCI assumptions about the validity of think-aloud and interview data for inferring beliefs; no free parameters or invented entities are introduced.

assumptions (1)
  • domain assumption Qualitative analysis of participant behavior and statements can reliably reveal mental models of AI systems
    Invoked implicitly by the choice to conduct and interpret a user study

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beliefs and Misconceptions around Integrated Conversational AI." pith.science (2026). https://pith.science/paper/35MPBQPZ

@misc{pith2026260514849,
  author       = {Pith},
  title        = {Pith review of: Beliefs and Misconceptions around Integrated Conversational AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/35MPBQPZ}},
  note         = {Machine review of arXiv:2605.14849}
}
read the original abstract

LLM-driven conversational AI is beginning to disappear into the background, shifting from something used directly towards something increasingly integrated into existing workflows. In the process, markers of origin and training are smoothed away as LLMs become commodified in the eyes of users. We explore how people approach using a web browser with conversational AI built in, focusing on how they develop their understanding and determine whether to trust its outputs. We conducted a study where 20 participants used the Copilot AI features in Microsoft Edge to conduct information retrieval and planning tasks. Participants relied on a combination of existing perceptions of LLMs and internet search, tracing the effect of beliefs about how Copilot generated answers on prompting strategies. The inclusion of citations increased the trustworthiness of answers without participants feeling the need to be check them, with participants often reaching for the same information sources as the CAI when fact-checking.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    2024.Influencer Ad Disclosure on Social Media: Instagram and TikTok

    Advertising Standards Agency. 2024.Influencer Ad Disclosure on Social Media: Instagram and TikTok. Technical Report. https://www.asa.org.uk/ resource/influencer-ad-disclosure-on-social-media-instagram-and-tiktok-2024.html

    work page 2024

  2. [2]

    Frank Bentley, Chris Luvogt, Max Silverman, Rushani Wirasinghe, Brooke White, and Danielle Lottridge. 2018. Understanding the Long-Term Use of Smart Speaker Assistants.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.2, 3, Article 91 (Sept. 2018), 24 pages. doi:10.1145/3264901

    work page doi:10.1145/3264901 2018

  3. [3]

    Johnson, Priyanshu Rai, Tathagata Chakraborti, Thomas Gschwind, Jim A Laredo, Christoph Miksovic, Paolo Scotton, Kartik Talamadupula, and Gegi Thomas

    Michelle Brachman, Qian Pan, Hyo Jin Do, Casey Dugan, Arunima Chaudhary, James M. Johnson, Priyanshu Rai, Tathagata Chakraborti, Thomas Gschwind, Jim A Laredo, Christoph Miksovic, Paolo Scotton, Kartik Talamadupula, and Gegi Thomas. 2023. Follow the Successful Herd: Towards Explanations for Improved Use and Mental Models of Natural Language Systems. InPro...

    work page doi:10.1145/3581641.3584088 2023

  4. [4]

    2012.Thematic analysis.American Psychological Association

    Virginia Braun and Victoria Clarke. 2012.Thematic analysis.American Psychological Association

    work page 2012

  5. [5]

    2021.Thematic analysis: A practical guide

    Virginia Braun and Victoria Clarke. 2021.Thematic analysis: A practical guide. SAGE publications Ltd

    work page 2021

  6. [6]

    Goran Bubaš, Snježana Babić, and Antonela Čižmešija. 2023. Usability and User Experience Related Perceptions of University Students Regarding the Use of Bing Chat Search Engine and AI Chatbot: Preliminary Evaluation of Assessment Scales. In2023 IEEE 21st Jubilee International Symposium on Intelligent Systems and Informatics (SISY). 000607–000612. doi:10.1...

    work page doi:10.1109/sisy60376.2023.10417910 2023

  7. [7]

    Sara Cannizzaro, Rob Procter, Sinong Ma, and Carsten Maple. 2020. Trust in the smart home: Findings from a nationally representative survey in the UK.Plos one15, 5 (2020), e0231615

    work page 2020

  8. [8]

    Avishek Choudhury and Hamid Shamszare. 2023. Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis.J Med Internet Res25 (14 Jun 2023), e47184. doi:10.2196/47184

    work page doi:10.2196/47184 2023

Show all 51 references
  1. [9]

    Newman, and Prabal Dutta

    Meghan Clark, Mark W. Newman, and Prabal Dutta. 2017. Devices and Data and Agents, Oh My: How Smart Home Abstractions Prime End-User Mental Models.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.1, 3, Article 44 (Sept. 2017), 26 pages. doi:10.1145/3132031

    2017 doi

  2. [10]

    What can i help you with?

    Benjamin R. Cowan, Nadia Pantidi, David Coyle, Kellie Morrissey, Peter Clarke, Sara Al-Shehri, David Earley, and Natasha Bandeira. 2017. "What can i help you with?": infrequent users’ experiences of intelligent personal assistants. InProceedings of the 19th International Confe...

    2017 doi

  3. [11]

    Peter J. Denning. 2025. In Large Language Models We Trust?Commun. ACM68, 6 (June 2025), 23–25. doi:10.1145/3726009

    2025 doi

  4. [12]

    Johnson, and Roshni Thawani

    Hyo Jin Do, Michelle Brachman, Casey Dugan, Qian Pan, Priyanshu Rai, James M. Johnson, and Roshni Thawani. 2024. Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems.Proc. ACM Hum.-Comput. Interact.8, CSCW2, Article 373 (Nov. 202...

    2024 doi

  5. [13]

    Josh Freeman. 2025. Student generative AI survey 2025.Higher Education Policy Institute: London, UK(2025)

    2025

  6. [14]

    Millen, Murray Campbell, Sadhana Kumaravel, and Wei Zhang

    Katy Ilonka Gero, Zahra Ashktorab, Casey Dugan, Qian Pan, James Johnson, Werner Geyer, Maria Ruiz, Sarah Miller, David R. Millen, Murray Campbell, Sadhana Kumaravel, and Wei Zhang. 2020. Mental Models of AI Agents in a Cooperative Game Setting. InProceedings of the 2020 CHI Co...

    2020 doi

  7. [15]

    Mark Grimes, Ryan M

    G. Mark Grimes, Ryan M. Schuetzler, and Justin Scott Giboney. 2021. Mental models and expectation violations in conversational AI interactions. Decision Support Systems144 (2021), 113515. doi:10.1016/j.dss.2021.113515

    2021 doi

  8. [16]

    Ellie Harmon and Melissa Mazmanian. 2013. Stories of the Smartphone in everyday discourse: conflict, tension & instability. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Paris, France)(CHI ’13). Association for Computing Machinery, New York, NY, ...

    2013 doi

  9. [17]

    Horstmann, Clara Strathmann, Lea Lambrich, and Nicole C

    Aike C. Horstmann, Clara Strathmann, Lea Lambrich, and Nicole C. Krämer. 2023. Alexa, What’s Inside of You: A Qualitative Study to Explore Users’ Mental Models of Intelligent Voice Assistants. InProceedings of the 23rd ACM International Conference on Intelligent Virtual Agents...

    2023 doi

  10. [18]

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of Hallucination in Natural Language Generation.ACM Comput. Surv.55, 12, Article 248 (March 2023), 38 pages. doi:10.1145/3571730

    2023 doi

  11. [19]

    Prerna Juneja, Wenjuan Zhang, Alison Marie Smith-Renner, Hemank Lamba, Joel Tetreault, and Alex Jaimes. 2024. Dissecting users’ needs for search result explanations. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Ass...

    2024 doi

  12. [20]

    Shyam Sundar

    Yongnam Jung, Cheng Chen, Eunchae Jang, and S. Shyam Sundar. 2024. Do We Trust ChatGPT as much as Google Search and Wikipedia?. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA ’24). Association for Computing Machinery, New York, N...

    2024 doi

  13. [21]

    You Always Get an Answer

    Ilkka Kaate, Joni Salminen, Soon-Gyo Jung, Trang Thi Thu Xuan, Essi Häyhänen, Jinan Y. Azem, and Bernard J. Jansen. 2025. “You Always Get an Answer”: Analyzing Users’ Interaction with AI-Generated Personas Given Unanswerable Questions and Risk of Hallucination. InProceedings o...

    2025 doi

  14. [22]

    Markelle Kelly, Aakriti Kumar, Padhraic Smyth, and Mark Steyvers. 2023. Capturing Humans’ Mental Models of AI: An Item Response Theory Approach. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency(Chicago, IL, USA)(FAccT ’23). Association for...

    2023 doi

  15. [23]

    Changhyun Lee and Kyungjin Cha and. 2024. Toward the Dynamic Relationship Between AI Transparency and Trust in AI: A Case Study on ChatGPT.International Journal of Human–Computer Interaction0, 0 (2024), 1–18. arXiv:https://doi.org/10.1080/10447318.2024.2405266 doi:10.1080/ 104...

    2024 doi

  16. [24]

    Sunok Lee, Minji Cho, and Sangsu Lee. 2020. What If Conversational Agents Became Invisible? Comparing Users’ Mental Models According to Physical Entity of AI Speaker.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.4, 3, Article 88 (Sept. 2020), 24 pages. doi:10.1145/3411840

    2020 doi

  17. [25]

    Houjiang Liu, Anubrata Das, Alexander Boltz, Didi Zhou, Daisy Pinaroc, Matthew Lease, and Min Kyung Lee. 2024. Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI.Proc. ACM Hum.-Comput. Interact.8, CSCW2, Article 423 (Nov. 2024), 44 pages...

    2024 doi

  18. [26]

    Like Having a Really Bad PA

    Ewa Luger and Abigail Sellen. 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems(San Jose, California, USA)(CHI ’16). Association for Com...

    2016 doi

  19. [27]

    David Lyell and Enrico Coiera. 2017. Automation bias and verification complexity: a systematic review.Journal of the American Medical Informatics Association24, 2 (2017), 423–431

    2017

  20. [28]

    Maria Madsen and Shirley Gregor. 2000. Measuring human-computer trust. In11th australasian conference on information systems, Vol. 53. Citeseer, 6–8

    2000

  21. [29]

    Kirsti Malterud, Volkert Dirk Siersma, and Ann Dorrit Guassora. 2016. Sample size in qualitative interview studies: guided by information power. Qualitative health research26, 13 (2016), 1753–1760

    2016

  22. [30]

    Artificial Intelligence (AI)

    Dogan Gursoy Mesut Cicek and Lu Lu. 2024. Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk.Journal ...

    2024 doi

  23. [31]

    Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. 2016. The ethics of algorithms: Mapping the debate.Big Data & Society3, 2 (2016), 2053951716679679

    2016

  24. [32]

    Vikram Mohanty, Jude Lim, and Kurt Luther. 2025. What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New Yor...

    2025 doi

  25. [33]

    Kathleen L Mosier and Linda J Skitka. 2018. Human decision makers and automated decision aids: Made for each other? InAutomation and human performance. CRC Press, 201–220

    2018

  26. [34]

    Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate. 2021. Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. InProceedings of the 26th International Conference on Intelligent ...

    2021 doi

  27. [35]

    Saumya Pareek, Niels van Berkel, Eduardo Velloso, and Jorge Goncalves. 2024. Effect of Explanation Conceptualisations on Trust in AI-assisted Credibility Assessment.Proc. ACM Hum.-Comput. Interact.8, CSCW2, Article 383 (Nov. 2024), 31 pages. doi:10.1145/3686922

    2024 doi

  28. [36]

    Sohyun Park, Someen Park, Jaehoon Kim, and Kyungsik Han. 2024. Exploring the Impact of AI-Generated Images on Political News Perception and Understanding. InCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing(San Jose, Costa...

    2024 doi

  29. [37]

    2026.Accenture ’links staff promotions to use of AI tools’

    Joanna Partridge. 2026.Accenture ’links staff promotions to use of AI tools’. https://www.theguardian.com/accenture/2026/feb/19/accenture-links- staff-promotions-to-use-of-ai-tools

    2026

  30. [38]

    It’s Weird That it Knows What I Want

    James Prather, Brent N. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023. “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers.ACM Tra...

    2023 doi

  31. [39]

    William Seymour and Jose Such. 2023. Ignorance is Bliss? The Effect of Explanations on Perceptions of Voice Assistants.Proc. ACM Hum.-Comput. Interact.7, CSCW1, Article 64 (April 2023), 24 pages. doi:10.1145/3579497

    2023 doi

  32. [40]

    William Seymour and Max Van Kleek. 2021. Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants.Proc. ACM Hum.-Comput. Interact.5, CSCW2, Article 371 (Oct. 2021), 16 pages. doi:10.1145/3479515

    2021 doi

  33. [41]

    Vera Liao, and Ziang Xiao

    Nikhil Sharma, Q. Vera Liao, and Ziang Xiao. 2024. Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Association for Computin...

    2024 doi

  34. [42]

    Stephen P Stich and Shaun Nichols. 2003. Folk psychology.The blackwell guide to philosophy of mind(2003), 235–255

    2003

  35. [43]

    Ana Stojanov, Qian Liu, and Joyce Hwee Ling Koh. 2024. University students’ self-reported reliance on ChatGPT for learning: A latent profile analysis.Computers and Education: Artificial Intelligence6, 4 (2024), 100243

    2024

  36. [44]

    Haoheng Tang and Mrinalini Singha. 2024. A Mystery for You: A fact-checking game enhanced by large language models (LLMs) and a tangible interface. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI EA ’24). Association for ...

    2024 doi

  37. [45]

    Paul Thomas, Bodo Billerbeck, Nick Craswell, and Ryen W. White. 2019. Investigating Searchers’ Mental Models to Inform Search Explanations. ACM Trans. Inf. Syst.38, 1, Article 10 (dec 2019), 25 pages. doi:10.1145/3371390 Manuscript submitted to ACM 16 Seymour and Jenkins, et al

    2019 doi

  38. [46]

    Irene Weber. 2024. Large language models as software components: A taxonomy for llm-integrated applications.arXiv preprint arXiv:2406.10300 (2024)

    2024

  39. [47]

    Zamfirescu-Pereira, Richmond Y

    J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Associa...

    2023 doi

  40. [48]

    Xia Zeng, David La Barbera, Kevin Roitero, Arkaitz Zubiaga, and Stefano Mizzaro. 2024. Combining Large Language Models and Crowdsourcing for Hybrid Human-AI Misinformation Detection. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in In...

    2024 doi

  41. [49]

    Xiao Zhan, Juan-Carlos Carrillo, William Seymour, and Jose Such. 2025. Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information. In34th USENIX Security Symposium. USENIX Association

    2025

  42. [50]

    Zhang, Aditya Ranganathan, Sarah Emlen Metz, Scott Appling, Connie Moon Sehat, Norman Gilmore, Nick B

    Amy X. Zhang, Aditya Ranganathan, Sarah Emlen Metz, Scott Appling, Connie Moon Sehat, Norman Gilmore, Nick B. Adams, Emmanuel Vincent, Jennifer Lee, Martin Robbins, Ed Bice, Sandro Hawke, David Karger, and An Xiao Mina. 2018. A Structured Response to Misinformation: Defining a...

    2018 doi

  43. [51]

    Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamb...

    2023 doi

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.