pith. sign in

arxiv: 2605.29938 · v1 · pith:4BFNQGNAnew · submitted 2026-05-28 · 💻 cs.CY

When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents

Leena Mathur , Jenny T. Liang , Vasudha Varadarajan , Jimin Mun , Xuhui Zhou , Jana Schaich Borg , Yonatan Bisk , Louis-Philippe Morency
show 1 more author
Maarten Sap
This is my paper

Pith reviewed 2026-06-29 00:30 UTC · model grok-4.3

classification 💻 cs.CY
keywords social intelligenceAI agentspublic perceptionsacceptability judgmentssupport-adoption gapmixed-methods surveyUS adultsSocial-AI
0
0 comments X

The pith

US adults already report encountering AI agents they see as socially intelligent based on observable behaviors, yet support such agents more for others than for their own use.

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

The paper presents results from a mixed-methods survey of 200 US adults examining how laypeople perceive social intelligence in AI agents across chatbots and robots. Participants indicated they have already met agents they judge as socially intelligent and based those judgments primarily on visible actions rather than assumptions about the agent's internal states or intentions. The study also documents a consistent support-adoption gap, in which respondents endorse the existence of socially intelligent AI for general societal use far more readily than they accept it for their own personal interactions. These findings matter because deployment decisions for everyday AI roles will hinge on such public views. The survey additionally surfaces specific concerns that could shape appropriate contexts and safeguards for these technologies.

Core claim

Participants widely reported having already encountered AI agents they perceived as socially intelligent and grounded their judgments in observable behaviors, more than beliefs about AI agency or intent. The analysis identifies a support-adoption gap: participants supported the existence of Social-AI agents for others far more than for their own personal use. The survey further maps contextual factors that influence acceptance and documents layperson concerns about the technologies.

What carries the argument

The support-adoption gap in acceptability judgments, identified through the mixed-methods survey responses on perceived social intelligence and personal versus general use.

If this is right

  • Deployment of Social-AI should prioritize contexts where observable behaviors align with public expectations of social intelligence.
  • Governance decisions must weigh the documented difference between societal support and personal adoption preferences.
  • Risk assessments for end users should incorporate the specific concerns raised about these agents in everyday settings.
  • Agent roles in social environments need calibration to the abilities participants associate with social intelligence.

Where Pith is reading between the lines

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

  • Designers could test whether making specific observable behaviors more prominent increases personal adoption rates.
  • Similar surveys in other countries or demographic groups might reveal whether the support-adoption gap is culturally specific.
  • Policy on AI in public spaces could use the identified concerns to set boundaries on agent capabilities.

Load-bearing premise

Self-reported answers from this sample of 200 US adults accurately reflect genuine perceptions of social intelligence and can be generalized beyond the surveyed group without meaningful response bias or sampling limits.

What would settle it

A replication study using behavioral observation tasks instead of self-report, or a nationally representative sample drawn differently, that finds participants do not ground social-intelligence judgments in observable behaviors or shows no support-adoption gap.

Figures

Figures reproduced from arXiv: 2605.29938 by Jana Schaich Borg, Jenny T. Liang, Jimin Mun, Leena Mathur, Louis-Philippe Morency, Maarten Sap, Vasudha Varadarajan, Xuhui Zhou, Yonatan Bisk.

Figure 1
Figure 1. Figure 1: Overview of the survey instrument. After reading [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of participant agreement with statements about current AI agents from the Perceived AI Social Intelli [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of RQ2 codes that participants iden [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Acceptability of Social-AI across the 12 scenario conditions from Table 1. Panel A shows normalized acceptability on [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Social-AI acceptability and support-adoption gap [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Participants’ privacy preferences for Social-AI in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Definition screen shown before the survey ques [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
read the original abstract

AI researchers have been advancing socially intelligent AI agents (Social-AI) across embodiments, from chatbots to physical robots. As Social-AI is increasingly deployed in everyday settings, decisions about the roles these agents should play will depend on how laypeople perceive them. However, public perceptions of social intelligence in AI agents and the acceptability of these agents remain largely understudied. We present a mixed-methods survey of adults in the United States (N=200) that examines social intelligence as a perceived construct in AI agents. Our survey investigates the extent to which participants believe current AI agents have social intelligence, abilities of agents that participants associate with social intelligence, contextual factors influencing participant acceptance of Social-AI agents, and concerns participants hold about these technologies. Participants widely reported having already encountered AI agents they perceived as socially intelligent and grounded their judgments in observable behaviors, more than beliefs about AI agency or intent. We identified a support-adoption gap in acceptability judgments: participants supported the existence of Social-AI agents for others far more than for their own personal use. Our analysis uncovers layperson concerns about Social-AI, informing AI governance regarding appropriate deployment contexts, agent roles, and risks to end users.

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 / 0 minor

Summary. The manuscript reports a mixed-methods survey of N=200 US adults examining public perceptions of social intelligence in AI agents. It claims that participants widely report prior encounters with socially intelligent AI, ground those judgments in observable behaviors more than beliefs about agency or intent, exhibit a support-adoption gap (greater support for others than personal use), and hold specific concerns that inform governance on deployment contexts and risks.

Significance. If substantiated, the work supplies empirical data on an understudied topic, identifying the support-adoption gap and the primacy of observable behaviors as potentially actionable inputs for AI governance and deployment decisions.

major comments (2)
  1. [Abstract and Methods] Abstract/Methods: The abstract states high-level findings on reported encounters and the support-adoption gap but supplies no details on the survey instrument, sampling method, statistical analysis, or exclusion criteria. This prevents verification that the data support the stated claims.
  2. [Results and Discussion] Results/Discussion: The generalizability claim to 'public perceptions' rests on an N=200 US adult sample with no reported validation against self-report bias or behavioral measures; this is load-bearing for the title and strongest claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the scope and limitations of our survey-based study. We address each major comment below, indicating where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Methods] Abstract/Methods: The abstract states high-level findings on reported encounters and the support-adoption gap but supplies no details on the survey instrument, sampling method, statistical analysis, or exclusion criteria. This prevents verification that the data support the stated claims.

    Authors: The abstract is written to be concise per standard journal guidelines, focusing on core findings. Full details on the survey instrument (including all items and scales), sampling (quota-based US adult sample via online platform), statistical analysis (descriptive statistics, thematic analysis of open responses), and exclusion criteria (e.g., attention checks and incomplete responses) are provided in the dedicated Methods section. To address the concern, we will revise the abstract to briefly note the mixed-methods design and N=200 sample, and add a cross-reference to the Methods section for verification. revision: yes

  2. Referee: [Results and Discussion] Results/Discussion: The generalizability claim to 'public perceptions' rests on an N=200 US adult sample with no reported validation against self-report bias or behavioral measures; this is load-bearing for the title and strongest claims.

    Authors: We agree that N=200 is modest and that self-report methods carry inherent bias risks; the manuscript already positions the work as an exploratory study of perceptions rather than a definitive population survey. We will revise the Results and Discussion sections to explicitly qualify generalizability claims, highlight the self-report limitation, and temper language in the title and abstract to reflect the sample scope. Behavioral validation measures would require an entirely different experimental design and are outside the current study's remit as a perception survey. revision: partial

Circularity Check

0 steps flagged

Empirical survey paper with no derivations, equations, or fitted predictions

full rationale

The paper reports findings from a mixed-methods survey (N=200 US adults) on perceptions of social intelligence in AI agents. It contains no equations, no parameter fitting, no predictive models, and no derivation chain. Claims rest directly on participant self-reports and qualitative coding rather than any reduction to prior inputs by construction. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear in the core analysis. This matches the default expectation of no significant circularity for non-theoretical empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard assumptions of survey research rather than new mathematical axioms, fitted parameters, or postulated entities.

axioms (1)
  • domain assumption Self-reported perceptions in a survey of 200 US adults reflect genuine and generalizable views on AI social intelligence.
    Core premise required for any interpretation of the reported percentages and gaps.

pith-pipeline@v0.9.1-grok · 5776 in / 1194 out tokens · 53971 ms · 2026-06-29T00:30:22.036845+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

65 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    C.; Dinkar, T.; Rieser, V.; and Talat, Z

    Abercrombie, G.; Curry, A. C.; Dinkar, T.; Rieser, V.; and Talat, Z. 2023. Mirages. on anthropomorphism in dialogue systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 4776--4790

    2023

  4. [4]

    AI, N. 2024. Artificial intelligence risk management framework: Generative artificial intelligence profile. NIST Trustworthy and Responsible AI Gaithersburg, MD, USA

    2024

  5. [5]

    Andalibi, N.; and Ingber, A. S. 2025. Public Perceptions About Emotion AI Use Across Contexts in the United States. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1--16

    2025

  6. [6]

    C.; Gabriel, I.; and Mohamed, S

    Birhane, A.; Isaac, W.; Prabhakaran, V.; Diaz, M.; Elish, M. C.; Gabriel, I.; and Mohamed, S. 2022. Power to the people? Opportunities and challenges for participatory AI. In Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1--8

    2022

  7. [7]

    Bondi, E.; Xu, L.; Acosta-Navas, D.; and Killian, J. A. 2021. Envisioning communities: a participatory approach towards AI for social good. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 425--436

    2021

  8. [8]

    L.; Vervier, L.; and Ziefle, M

    Brauner, P.; Glawe, F.; Liehner, G. L.; Vervier, L.; and Ziefle, M. 2026. Charting the AI perception gap: divergent views on risk, benefit, and value between experts and the public challenge the societal acceptance of AI. AI & society, 1--29

    2026

  9. [9]

    Brown, B.; Bu, F.; Mandel, I.; and Ju, W. 2024. Trash in motion: Emergent interactions with a robotic trashcan. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1--17

    2024

  10. [10]

    S.; Franyutti-Cintron, A

    Chen, A.; Kim, S. S.; Franyutti-Cintron, A. N.; Dharmasiri, A.; Mukherjee, K.; Russakovsky, O.; and Fan, J. E. 2026. Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1--30

    2026

  11. [11]

    Conzelmann, K.; Weis, S.; and S \"u , H.-M. 2013. New findings about social intelligence. Journal of Individual Differences

    2013

  12. [12]

    M.; and Strauss, A

    Corbin, J. M.; and Strauss, A. 1990. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative sociology, 13(1): 3--21

    1990

  13. [13]

    Cronbach, L. J. 1951. Coefficient alpha and the internal structure of tests. psychometrika, 16(3): 297--334

    1951

  14. [14]

    Delgado, F.; Yang, S.; Madaio, M.; and Yang, Q. 2023. The participatory turn in ai design: Theoretical foundations and the current state of practice. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1--23

    2023

  15. [15]

    Deng, E.; Mutlu, B.; and Matari \'c , M. J. 2019. Embodiment in socially interactive robots. Foundations and Trends in Robotics , 7(4): 251--356

    2019

  16. [16]

    Dennler, N.; Ruan, C.; Hadiwijoyo, J.; Chen, B.; Nikolaidis, S.; and Matari \'c , M. 2023. Design metaphors for understanding user expectations of socially interactive robot embodiments. ACM Transactions on Human-Robot Interaction, 12(2): 1--41

    2023

  17. [17]

    J.; Baguley, T.; and Brunsden, V

    Dunn, T. J.; Baguley, T.; and Brunsden, V. 2014. From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British journal of psychology, 105(3): 399--412

    2014

  18. [18]

    Fei-Fei, L.; and Krishna, R. 2022. Searching for computer vision north stars. Daedalus, 151(2): 85--99

    2022

  19. [19]

    Gambino, A.; Fox, J.; and Ratan, R. A. 2020. Building a stronger CASA: Extending the computers are social actors paradigm. Human-Machine Communication, 1: 71--85

    2020

  20. [20]

    K.; Lee, J

    Gordon, G.; Spaulding, S.; Westlund, J. K.; Lee, J. J.; Plummer, L.; Martinez, M.; Das, M.; and Breazeal, C. 2016. Affective personalization of a social robot tutor for children’s second language skills. In Proceedings of the AAAI conference on artificial intelligence, volume 30

    2016

  21. [21]

    Gweon, H.; Fan, J.; and Kim, B. 2023. Socially intelligent machines that learn from humans and help humans learn. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2251)

    2023

  22. [22]

    Heerink, M.; Kr \"o se, B.; Evers, V.; and Wielinga, B. 2010. Assessing acceptance of assistive social agent technology by older adults: the almere model

    2010

  23. [23]

    Henschel, A.; Laban, G.; and Cross, E. S. 2021. What makes a robot social? A review of social robots from science fiction to a home or hospital near you. Current Robotics Reports, 2(1): 9--19

    2021

  24. [24]

    Hurst, N.; Clabaugh, C.; Baynes, R.; Cohn, J.; Mitroff, D.; and Scherer, S. 2020. Social and emotional skills training with embodied moxie. arXiv preprint arXiv:2004.12962

    work page arXiv 2020

  25. [25]

    S.; Haimson, O

    Ingber, A. S.; Haimson, O. L.; and Andalibi, N. 2025. Distinguishing Emotion AI: Factors Shaping Perceptions Including Input Data, Emotion Data Recipients, and Identity. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 498--510

    2025

  26. [26]

    E.; Rueben, M.; Smart, W

    Kaminski, M. E.; Rueben, M.; Smart, W. D.; and Grimm, C. M. 2016. Averting robot eyes. Md. L. Rev., 76: 983

    2016

  27. [27]

    A.; et al

    Kian, M.; Zong, M.; Fischer, K.; Velentza, A.-M.; Singh, A.; Shrestha, K.; Sang, P.; Upadhyay, S.; Browning, W.; Faruki, M. A.; et al. 2025. Engagement and Disclosures in LLM-Powered Cognitive Behavioral Therapy Exercises: A Factorial Design Comparing the Influence of a Robot vs. Chatbot Over Time. In 2025 34th IEEE International Conference on Robot and H...

    2025

  28. [28]

    F.; and Cantor, N

    Kihlstrom, J. F.; and Cantor, N. 2000. Social intelligence

    2000

  29. [29]

    Krosnick, J. A. 2017. Questionnaire design. In The Palgrave handbook of survey research, 439--455. Springer

    2017

  30. [30]

    Lee, S.; Li, M.; Lai, B.; Jia, W.; Ryan, F.; Cao, X.; Kara, O.; Boote, B.; Shi, W.; Yang, D.; et al. 2024. Towards social ai: A survey on understanding social interactions. arXiv preprint arXiv:2409.15316

    work page arXiv 2024

  31. [31]

    Li, M.; Shi, W.; Ziems, C.; and Yang, D. 2024. Social intelligence data infrastructure: Structuring the present and navigating the future. In Findings of the Association for Computational Linguistics: ACL 2024, 2789--2805

    2024

  32. [32]

    V.; and Wright, J

    Marsden, P. V.; and Wright, J. D. 2010. Handbook of survey research. Emerald Group Publishing

    2010

  33. [33]

    P.; and Morency, L.-P

    Mathur, L.; Liang, P. P.; and Morency, L.-P. 2024. Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions. In Al-Onaizan, Y.; Bansal, M.; and Chen, Y.-N., eds., Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 20541--20560. Miami, Florida, USA: Association for Computational Linguistics

    2024

  34. [34]

    P.; and Morency, L.-P

    Mathur, L.; Qian, M.; Liang, P. P.; and Morency, L.-P. 2025. Social Genome: Grounded Social Reasoning Abilities of Multimodal Models. In Christodoulopoulos, C.; Chakraborty, T.; Rose, C.; and Peng, V., eds., Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 24868--24891. Suzhou, China: Association for Computational Li...

    2025

  35. [35]

    McDonald, R. P. 2013. Test theory: A unified treatment. psychology press

    2013

  36. [36]

    McNemar, Q. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2): 153--157

    1947

  37. [37]

    Mun, J.; Jiang, L.; Liang, J.; Cheong, I.; DeCario, N.; Choi, Y.; Kohno, T.; and Sap, M. 2024. Particip-ai: A democratic surveying framework for anticipating future ai use cases, harms and benefits. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 7, 997--1010

    2024

  38. [38]

    Mun, J.; Yeong, W. B. A.; Deng, W. H.; Borg, J. S.; and Sap, M. 2025. Why (Not) Use AI? Analyzing People’s Reasoning and Conditions for AI Acceptability. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 1771--1784

    2025

  39. [39]

    Mushkani, R.; Berard, H.; Ammar, T.; Chatonnier, C.; and Koseki, S. 2025. Co-Producing AI: Toward an Augmented, Participatory Lifecycle. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 1785--1799

    2025

  40. [40]

    Nakanishi, J.; Kuramoto, I.; Baba, J.; Ogawa, K.; Yoshikawa, Y.; and Ishiguro, H. 2020. Continuous hospitality with social robots at a hotel. SN Applied Sciences, 2(3): 452

    2020

  41. [41]

    Nass, C.; and Moon, Y. 2000. Machines and mindlessness: Social responses to computers. Journal of social issues, 56(1): 81--103

    2000

  42. [42]

    Nass, C.; Steuer, J.; and Tauber, E. R. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems, 72--78

    1994

  43. [43]

    Natale, S. 2019. If software is narrative: Joseph Weizenbaum, artificial intelligence and the biographies of ELIZA. new media & society, 21(3): 712--728

    2019

  44. [44]

    Paepcke, S.; and Takayama, L. 2010. Judging a bot by its cover: An experiment on expectation setting for personal robots. In 2010 5th ACM/IEEE international Conference on human-robot interaction (HRI), 45--52. IEEE

    2010

  45. [45]

    W.; Grover, I.; Spaulding, S.; Gomez, L.; and Breazeal, C

    Park, H. W.; Grover, I.; Spaulding, S.; Gomez, L.; and Breazeal, C. 2019. A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. In Proceedings of the AAAI conference on artificial intelligence, volume 33, 687--694

    2019

  46. [46]

    Parts, J.; Leoste, J.; Tammem \"a e, K.; and Rakic, S. 2025. A Systematic Scoping Review of Privacy Challenges and Privacy Enhancing Technologies in Teleoperated Robotics. IEEE Access, 13: 216724--216747

    2025

  47. [47]

    Sap, M.; Rashkin, H.; Chen, D.; Le Bras, R.; and Choi, Y. 2019. Social IQa: Commonsense reasoning about social interactions. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), 4463--4473

    2019

  48. [48]

    Sartori, L.; and Bocca, G. 2023. Minding the gap (s): public perceptions of AI and socio-technical imaginaries. AI & society, 38(2): 443--458

    2023

  49. [49]

    D.; Boyd, D.; Friedler, S

    Selbst, A. D.; Boyd, D.; Friedler, S. A.; Venkatasubramanian, S.; and Vertesi, J. 2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency, 59--68

    2019

  50. [50]

    W.; Miner, A

    Sharma, A.; Lin, I. W.; Miner, A. S.; Atkins, D. C.; and Althoff, T. 2023. Human--AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1): 46--57

    2023

  51. [51]

    The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

    Slattery, P.; Saeri, A. K.; Grundy, E. A.; Graham, J.; Noetel, M.; Uuk, R.; Dao, J.; Pour, S.; Casper, S.; and Thompson, N. 2024. The ai risk repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv preprint arXiv:2408.12622

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  52. [52]

    Spearman, C. 1961. The proof and measurement of association between two things

    1961

  53. [53]

    A.; Laban, G.; Lim, A.; and Gunes, H

    Spitale, M.; Axelsson, M.; Jeong, S.; Tutt \"o s \' , P.; Stamatis, C. A.; Laban, G.; Lim, A.; and Gunes, H. 2025. Past, present, and future: A survey of the evolution of affective robotics for well-being. IEEE Transactions on Affective Computing

    2025

  54. [54]

    Takayama, L.; Ju, W.; and Nass, C. 2008. Beyond dirty, dangerous and dull: what everyday people think robots should do. In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, 25--32

    2008

  55. [55]

    Taylor, S.; Jaques, N.; Nosakhare, E.; Sano, A.; and Picard, R. 2017. Personalized multitask learning for predicting tomorrow's mood, stress, and health. IEEE Transactions on Affective Computing, 11(2): 200--213

    2017

  56. [56]

    Thomaz, A. 2023. Robots in real life: Putting hri to work. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 3--3

    2023

  57. [57]

    L.; and Stein, S

    Thorndike, R. L.; and Stein, S. 1937. An evaluation of the attempts to measure social intelligence. Psychological bulletin, 34(5): 275

    1937

  58. [58]

    Turner, J. H. 1988. A theory of social interaction. Stanford University Press

    1988

  59. [59]

    Ullstein, C.; Jarvers, S.; Hohendanner, M.; Papakyriakopoulos, O.; and Grossklags, J. 2025. Participatory AI and the EU AI Act. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 2550--2562

    2025

  60. [60]

    Bureau of Labor Statistics

    U.S. Bureau of Labor Statistics . 2018. The 2018 Standard Occupational Classification System

    2018

  61. [61]

    P.; and Yuan, T

    Wang, B.; Rau, P.-L. P.; and Yuan, T. 2023. Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 42(9): 1324--1337

    2023

  62. [62]

    Weis, S.; and S \"u , H.-M. 2005. Social intelligence--A review and critical discussion of measurement concepts. Emotional intelligence: An international handbook, 203--230

    2005

  63. [63]

    Weizenbaum, J. 1983. ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 26(1): 23--28

    1983

  64. [64]

    Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics bulletin, 1(6): 80--83

    1945

  65. [65]

    Zhou, X.; Zhu, H.; Mathur, L.; Zhang, R.; Yu, H.; Qi, Z.; Morency, L.-P.; Bisk, Y.; Fried, D.; Neubig, G.; et al. 2024. Sotopia: Interactive evaluation for social intelligence in language agents. In International Conference on Learning Representations, volume 2024, 40975--41019

    2024