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Self-Disclosure to AI: The Paradox of Trust and Vulnerability in Human-Machine Interactions

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that people trust AI as a confidant precisely because it appears neutral and non-judgmental, and that this perception creates new privacy and emotional vulnerabilities.

desk verdict A clear, honest conceptual essay that recycles known applications of SPT/CPM to AI disclosure, whose motivating 'paradox' rests on an art project rather than controlled evidence. read the letter →

arxiv 2412.20564 v1 pith:YEJFNSTW submitted 2024-12-29 cs.HC cs.RO

Zoe Zhiqiu Jiang This is my paper · ORCID
classification cs.HCcs.RO
keywords self-disclosurehuman-AItrustsocialpenetrationtheorycommunicationprivacymanagementAIconfidantdigitaldisinhibitionvulnerabilityethics
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

This paper is a conceptual analysis, not a new experiment. It starts from Alexander Reben's BlabDroid art project, in which small robots reportedly drew intimate confessions from strangers, and asks why people would reveal private thoughts to a machine that cannot understand them. The central claim is that trust in AI does not rest on the same cues as trust in humans: people trust AI because it feels neutral, non-judgmental, and safe, and that feeling of safety is what encourages deeper self-disclosure. That same mechanism, the paper argues, creates a paradox—users lower their guard precisely because the machine poses no visible social risk, even though the data can be stored, analyzed, and used in ways that violate privacy and emotional needs. The paper's contribution is to map this paradox onto existing theories of self-disclosure and to argue that current ethical frameworks are not equipped to handle it.

What carries the argument

The argument runs on two named communication theories. Social Penetration Theory (Altman and Taylor) models relationships as an onion: disclosures start broad and shallow and move to narrow, intimate layers as trust grows; applied to AI, it predicts a false sense of deepening intimacy. Communication Privacy Management Theory (Petronio) holds that people maintain privacy boundaries and negotiate disclosure against perceived risk; applied to AI, it predicts boundary confusion because the machine feels safe yet is not a moral agent. The psychological engine that connects them is perceived neutrality—the user's belief that the AI is objective and non-judgmental—which lowers perceived interpersonal risk and fuels deeper disclosure. Posthumanism and phenomenology then widen the frame, asking whether trust needs human-like qualities and how everyday experience with AI reshapes privacy and autonomy.

What would settle it

Run a preregistered, controlled experiment in which participants are randomly assigned to confess sensitive information to a simple robot, a human interviewer, or a chatbot, with matched scripts and identical recording conditions; if self-disclosure depth and volume are not higher (or are lower) in the machine conditions than the human condition, the motivating claim of the paradox fails and the paper reduces to a general privacy warning.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is a reframing: the qualities that make AI appear trustworthy—perceived objectivity, absence of judgment, consistent availability—are exactly the qualities that make it dangerous as a confidant. The paper draws on Social Penetration Theory to show that users may escalate intimacy with an AI exactly as they would with a human, while the AI's lack of genuine empathy leaves the disclosure unreciprocated and the user vulnerable. It uses Communication Privacy Management Theory to argue that users' privacy boundaries become fuzzy when the listener is a machine, because the perceived social risk is low while the actual data risk is high. Philosophically, posthumanism and phenomenology are invoked to ask whether human-centered trust concepts still apply to machines and how lived experience with AI changes our sense of privacy and autonomy. The paper concludes that the tension should not be 'solved' but continually examined, and calls for ethical frameworks that cover emotional and psychological harm, not just data protection.

Load-bearing premise

The argument leans on the empirical premise that simple machines actually elicit more intimate disclosure than human listeners do; that premise comes from an art installation, not a controlled comparison, and if it is false the paradox collapses into a familiar warning about data privacy.

Editorial extensions

If this is right

  • If perceived neutrality is what earns trust, then telling users how their data is used may reduce disclosure rather than simply inform consent, because it punctures the illusion of a non-judgmental listener.
  • If Social Penetration Theory applies to AI, users will escalate intimacy over repeated interactions, so even a 'harmless' chatbot can accumulate a sensitive profile without any single disclosure seeming risky.
  • If Communication Privacy Management Theory applies, AI systems need active boundary-negotiation features, not just privacy policies, because users' perceived control already exceeds their actual control.
  • If digital disinhibition generalizes, oversharing with AI is not an accident but a predictable effect, and designers of mental-health or assistant AIs should treat it as a design hazard.
  • If current ethical frameworks are insufficient, responsibility for AI-as-confidant extends to designers and operators, who must consider psychological well-being, not merely data security.

Reading between the lines

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

  • I read the paper as implying a testable 'neutrality premium' it does not name: holding the script identical, disclosure depth should be at least as high to a chatbot that explicitly denies having opinions as to a human interviewer; a lab study could measure that directly.
  • The paper's logic is not AI-specific. Anonymous human listeners or scripted interviewers should produce similar effects, so the distinctive AI contribution is scale, persistence, and the absence of any chance the listener will meet us later.
  • A prediction the author leaves implicit: users who later learn their disclosures shaped AI outputs will react with betrayal-like privacy regret, analogous to post-hoc regret in social media, because the trust was built on perceived neutrality.
  • A concrete design extension would be to insert a 'disclosure warning' before high-intimacy prompts; if disclosures drop sharply, that would confirm perceived safety, not need, is driving oversharing.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This conceptual paper explores a 'paradox of trust and vulnerability' in human-AI self-disclosure, motivated by Alexander Reben's BlabDroid art project, in which small robots reportedly elicited intimate personal secrets 'often more effectively than human counterparts.' The paper applies Social Penetration Theory and Communication Privacy Management Theory to argue that perceived neutrality and non-judgmentalism make AI appear trustworthy, thereby encouraging deeper disclosure while creating privacy, data-misuse, and emotional-neglect vulnerabilities. It then draws on posthumanism and phenomenology to question human-centered trust and ethical frameworks, concluding that the paradox is a tension to be continually examined rather than a problem to be solved. No new empirical data are presented; the manuscript is a theoretical synthesis with a call for future research.

Significance. If the motivating empirical premise were securely established, the paper would offer a useful interdisciplinary framing of a timely issue, connecting communication theories with AI ethics and HCI. The writing is clear and the reference list is broad and relevant. The authors are honest that they raise questions rather than provide definitive answers. However, the paper's distinctiveness hinges on the comparative claim that people disclose more readily to machines than to humans, and that premise is supported only by an art demonstration, not by systematic evidence. As a result, the paper currently functions more as an informed position essay than as an analysis that advances a falsifiable claim. The absence of engagement with the substantial human-robot interaction literature that has experimentally compared disclosure to humans versus robots further weakens the foundation. With revision to either supply supporting evidence or substantially soften the comparative claim, the paper could become a credible conceptual contribution to discussions of intimate human-AI interaction.

major comments (2)
  1. [Abstract and Section 1] The central comparative claim that BlabDroid robots elicited personal disclosures 'often more effectively than human counterparts' (Abstract) and 'more readily than would be expected in human-to-human encounters' (Section 1) is attributed solely to Alexander Reben's art project [3,4]. The paper provides no sample, metric, comparison condition, or statistical result to support this claim. This premise is load-bearing: the 'paradox' is defined by an unexpected excess of disclosure to machines over humans. If the comparative premise is not established (or is false), the paper collapses into a familiar warning about data privacy and emotional neglect in AI interactions, which does not require a distinctive human-machine trust paradox. Furthermore, the paper does not cite the human-robot interaction literature that does compare disclosure to robots versus humans experimentally, so it cannot indirectly borrow empirical support. The author's framing that the paradox 'raises more questions than answers' does not repair the factual status of the motivating premise. I request that the authors either provide credible empirical support or explicitly reframe the argument conditionally, for example, 'if people do disclose more readily to machines, then ...'.
  2. [Section 3.1] The application of Social Penetration Theory to human-AI interaction asserts that 'users may extend trust to AI systems incrementally, sharing more personal information as they perceive the AI as reliable and non-judgmental.' This is presented as a natural extrapolation of SPT, but it is not a demonstrated property of human-AI interaction, and no empirical citation is given for this specific claim. The mechanism is load-bearing for the paper's narrative of a 'false sense of connection' leading to deeper disclosure. The paper would be more scientifically honest if this step were flagged as a hypothesis or condition, and if the authors engaged with empirical work on whether human-AI disclosure actually follows SPT-like dynamics, including any studies that find no such deepening.
minor comments (4)
  1. [Section 3.2] The sentence 'this effect can raises the risk of oversharing' contains a subject-verb agreement error; 'can raises' should be 'can raise.'
  2. [Section 4.2] The phrase 'AI's responses may simply resonates what users share' contains a grammatical error; 'resonates' should be 'resonate' or 'echo.'
  3. [Section 3.2] The discussion of 'digital disinhibition' relies on research on anonymity in computer-mediated communication among humans (e.g., [43,44]), but the extrapolation to human-AI interaction is not explicitly justified; the authors should clarify that this is an analogy, not a direct empirical finding.
  4. [Section 4.2] The paper mentions the precautionary principle and well-being-based models as possible ethical directions, but these are introduced without explaining how they would apply specifically to human-AI confidants; a sentence or two of elaboration would improve clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the paper is a conceptual essay with no derivations, no fitted parameters, and no load-bearing self-citation.

full rationale

This paper does not derive quantitative results, fit parameters from data, or invoke a uniqueness theorem from the authors' prior work. Its central discussion applies existing theories such as Social Penetration Theory, Communication Privacy Management Theory, posthumanism, and phenomenology to the phenomenon of self-disclosure to AI. The motivating empirical premise, drawn from Alexander Reben's BlabDroid project, is cited to external sources and is not produced by the paper itself. That premise may be empirically weak because it rests on an art project rather than a controlled comparison, but that is an evidentiary or correctness concern, not circularity. The paper explicitly frames its contribution as raising questions rather than proving conclusions, stating that the paradox 'raises more questions than answers' and calling for 'more research and ongoing dialogue.' No step in the argument is equivalent to its own input by construction, and no fitted value is renamed as a prediction. Therefore, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is a conceptual essay with no fitted parameters and no new entities. Its claims rest on four domain assumptions about AI capabilities, user perception, theory transfer, and disclosure risk. These assumptions are plausible and cited, but none are demonstrated by new evidence in this paper.

assumptions (4)
  • domain assumption AI systems lack genuine consciousness, emotions, and moral agency.
    Stated in Section 1 and Section 3.1, citing [5] and [32]. The paradox depends on AI being an uncomprehending confidant; if future AI is conscious, the risks and ethical framing change.
  • domain assumption Users perceive AI as neutral, objective, and non-judgmental, which lowers privacy boundaries.
    Assumed throughout Section 1 and Section 3.2, motivated by BlabDroid. No controlled evidence is presented in this paper.
  • domain assumption SPT and CPM, developed for human relationships, can be meaningfully applied to human-AI interaction.
    Invoked in Section 1 and Section 3.1. Cited works [10, 35] make similar extensions, but the transfer is assumed rather than derived.
  • domain assumption Self-disclosure to AI is increasing and carries risks of storage and exploitation by controlling entities.
    Presupposed in Section 3.2. Plausible and referenced, but not empirically quantified here.

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Cite this review

Pith. "Pith review of Self-Disclosure to AI: The Paradox of Trust and Vulnerability in Human-Machine Interactions." pith.science (2026). https://pith.science/paper/YEJFNSTW

@misc{pith2026241220564,
  author       = {Pith},
  title        = {Pith review of: Self-Disclosure to AI: The Paradox of Trust and Vulnerability in Human-Machine Interactions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YEJFNSTW}},
  note         = {Machine review of arXiv:2412.20564}
}
read the original abstract

In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon raises intriguing questions about how trust and self-disclosure operate in interactions with machines, even in their simplest forms. We study the change of trust in technology through analyzing the psychological processes behind such encounters. The analysis applies theories like Social Penetration Theory and Communication Privacy Management Theory to understand the balance between perceived security and the risk of exposure when personal information and secrets are shared with machines or AI. Additionally, we draw on philosophical perspectives, such as posthumanism and phenomenology, to engage with broader questions about trust, privacy, and vulnerability in the digital age. Rapid incorporation of AI into our most private areas challenges us to rethink and redefine our ethical responsibilities.

Figures

Figures reproduced from arXiv: 2412.20564 by the authors.

Figure 1
Figure 1. Alexander Reben’s BlabDroid: Robots in Residence. Source: Reben, 2018 [3]. “mirror” that merely reflects users’ emotions and thoughts without judgment [6]. Paradoxically, the very traits that make AI appear trustworthy – such as its perceived fairness, lack of bias, and consistent behavior – might also make it a potential risky confidant. This effect could, in reality, distort users’ self-disclosure by encouraging d… view at source ↗
Figure 2
Figure 2. Relationships layers according to Social Penetration Theory. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

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    Disclosing a game AI's superhuman ability can reduce suspicion and raise trust in novices, but also triggers overreliance and defeatism among experts and in cooperative settings.

Reference graph

Works this paper leans on

67 extracted references · 62 canonical work pages · cited by 1 Pith paper

  1. [1]

    A first look at communication theory

    EM Griffin. A first look at communication theory. McGraw-hill, 2006

    work page 2006

  2. [2]

    Using computer automated systems to conduct personal interviews: Does the mere presence of a human face inhibit disclosure? Computers in Human Behavior, 105:106197, 2020

    Matthew D Pickard and Catherine A Roster. Using computer automated systems to conduct personal interviews: Does the mere presence of a human face inhibit disclosure? Computers in Human Behavior, 105:106197, 2020

    work page 2020

  3. [3]

    Blabdroid: Robots in residence

    Alexander Reben. Blabdroid: Robots in residence. https://areben.com/project/ blabdroid/, 2018

    work page 2018

  4. [4]

    Blabdroid, 2012-2018

    Alexander Reben. Blabdroid, 2012-2018. Video, 10:56 min, part of an exhibition at The MAK – Museum of Applied Arts, Vienna, Austria, 2024. On loan from Antepossible LLC / Alexander Reben

    work page 2012

  5. [5]

    Corina Pelau, Dan-Cristian Dabija, and Irina Ene. What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry.Computers in Human Behavior, 122:106855, 2021

    work page 2021

  6. [6]

    Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI

    Alon Jacovi, Ana Marasovi´c, Tim Miller, and Yoav Goldberg. Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 624–635, 2021

    work page 2021

  7. [7]

    An exploratory study on the dark sides of artificial intelligence adoption: Privacy’s invasion for intelligent marketing and intelligent services

    Poh Hwa Eng and Ran Long Liu. An exploratory study on the dark sides of artificial intelligence adoption: Privacy’s invasion for intelligent marketing and intelligent services. In Current and Future Trends on Intelligent Technology Adoption: Volume 2, pages 17–42. Springer, 2024

    work page 2024

  8. [8]

    Social penetration: The development of interpersonal relationships

    Irwin Altman and Dalmas A Taylor. Social penetration: The development of interpersonal relationships. Holt, Rinehart & Winston, 1973

    work page 1973

Show all 67 references
  1. [9]

    Boundaries of privacy: Dialectics of disclosure

    Sandra Petronio. Boundaries of privacy: Dialectics of disclosure. Suny Press, 2002

    2002

  2. [10]

    Relationship development with humanoid social robots: Applying interpersonal theories to human–robot interaction

    Jesse Fox and Andrew Gambino. Relationship development with humanoid social robots: Applying interpersonal theories to human–robot interaction. Cyberpsychology, Behavior, and Social Networking, 24(5):294–299, 2021

    2021

  3. [11]

    Posthuman management: Creating effective organizations in an age of social robotics, ubiquitous AI, human augmentation, and virtual worlds

    Matthew E Gladden. Posthuman management: Creating effective organizations in an age of social robotics, ubiquitous AI, human augmentation, and virtual worlds. Defragmenter Media, 2016

    2016

  4. [12]

    Phenomenology and artificial intelligence

    Anthony F Beavers. Phenomenology and artificial intelligence. Metaphilosophy, 33(1-2):70–82, 2002

    2002

  5. [13]

    To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts

    Nessrine Omrani, Giorgia Rivieccio, Ugo Fiore, Francesco Schiavone, and Sergio Garcia Agreda. To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181:121763, 2022

    2022

  6. [14]

    Human trust in artificial intelligence: Review of empirical research

    Ella Glikson and Anita Williams Woolley. Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2):627–660, 2020

    2020

  7. [15]

    Trust in artificial intelligence: From a foundational trust framework to emerging research opportunities

    Roman Lukyanenko, Wolfgang Maass, and Veda C Storey. Trust in artificial intelligence: From a foundational trust framework to emerging research opportunities. Electronic Markets, 32(4):1993–2020, 2022

    1993

  8. [16]

    User trust in artificial intelligence: A comprehensive conceptual framework

    Rongbin Yang and Santoso Wibowo. User trust in artificial intelligence: A comprehensive conceptual framework. Electronic Markets, 32(4):2053–2077, 2022

    2022

  9. [17]

    Trust theory: A socio-cognitive and computational model

    Christiano Castelfranchi and Rino Falcone. Trust theory: A socio-cognitive and computational model. John Wiley & Sons, 2010

    2010

  10. [18]

    Technology, humanness, and trust: Rethinking trust in technology

    Nancy K Lankton, D Harrison McKnight, and John Tripp. Technology, humanness, and trust: Rethinking trust in technology. Journal of the Association for Information Systems, 16(10):1, 2015. 7

    2015

  11. [19]

    Technology and moral change: the transformation of truth and trust

    John Danaher and Henrik Skaug Saetra. Technology and moral change: the transformation of truth and trust. Ethics and Information Technology, 24(3):35, 2022

    2022

  12. [20]

    The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review

    Mathura Shanmugasundaram and Arunkumar Tamilarasu. The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review. Frontiers in Cognition, 2:1203077, 2023

    2023

  13. [21]

    The relationship between trust in AI and trustworthy machine learning tech- nologies

    Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad Van Moorsel. The relationship between trust in AI and trustworthy machine learning tech- nologies. In Proceedings of the 2020 conference on fairness, accountability, and transparen...

    2020

  14. [22]

    Connecting the dots in trustworthy artificial intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

    Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh, Marcos López de Prado, En- rique Herrera-Viedma, and Francisco Herrera. Connecting the dots in trustworthy artificial intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulat...

    2023

  15. [23]

    Artificial intelligence trust, risk and security management (ai trism): Frameworks, applications, challenges and future research directions

    Adib Habbal, Mohamed Khalif Ali, and Mustafa Ali Abuzaraida. Artificial intelligence trust, risk and security management (ai trism): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240:122442, 2024

    2024

  16. [24]

    How transparency modulates trust in artificial intelligence

    John Zerilli, Umang Bhatt, and Adrian Weller. How transparency modulates trust in artificial intelligence. Patterns, 3(4), 2022

    2022

  17. [25]

    Humans perceive warmth and competence in artificial intelligence

    Kevin R McKee, Xuechunzi Bai, and Susan T Fiske. Humans perceive warmth and competence in artificial intelligence. Iscience, 26(8), 2023

    2023

  18. [26]

    U-Trustworthy models

    Ritwik Vashistha and Arya Farahi. U-Trustworthy models. Reliability, competence, and confi- dence in decision-making. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19956–19964, 2024

    2024

  19. [27]

    Do we trust in AI? Role of anthropomorphism and intelligence

    Indrit Troshani, Sally Rao Hill, Claire Sherman, and Damien Arthur. Do we trust in AI? Role of anthropomorphism and intelligence. Journal of Computer Information Systems, 61(5):481–491, 2021

    2021

  20. [28]

    The uncanny valley [from the field]

    Masahiro Mori, Karl F MacDorman, and Norri Kageki. The uncanny valley [from the field]. IEEE Robotics & automation magazine, 19(2):98–100, 2012

    2012

  21. [29]

    The uncanny valley: Existence and explanations

    Shensheng Wang, Scott O Lilienfeld, and Philippe Rochat. The uncanny valley: Existence and explanations. Review of General Psychology, 19(4):393–407, 2015

    2015

  22. [30]

    How anthropomorphism affects trust in intelligent personal assistants

    Qian Qian Chen and Hyun Jung Park. How anthropomorphism affects trust in intelligent personal assistants. Industrial Management & Data Systems, 121(12):2722–2737, 2021

    2021

  23. [31]

    Toward a computational model of social relations for artificial companions

    Florian Pecune. Toward a computational model of social relations for artificial companions. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pages 677–682. IEEE, 2013

    2013

  24. [32]

    Consciousness in artificial intelligence: insights from the science of consciousness

    Patrick Butlin, Robert Long, Eric Elmoznino, Yoshua Bengio, Jonathan Birch, Axel Constant, George Deane, Stephen M Fleming, Chris Frith, Xu Ji, et al. Consciousness in artificial intelligence: insights from the science of consciousness. arXiv preprint arXiv:2308.08708, 2023

    2023 arXiv

  25. [33]

    When human-AI interactions become parasocial: Agency and anthropomorphism in affective design

    Takuya Maeda and Anabel Quan-Haase. When human-AI interactions become parasocial: Agency and anthropomorphism in affective design. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, pages 1068–1077, 2024

    2024

  26. [34]

    On artificial intelligence and manipulation

    Marcello Ienca. On artificial intelligence and manipulation. Topoi, 42(3):833–842, 2023

    2023

  27. [35]

    I just shared your responses

    Shruti Sannon, Brett Stoll, Dominic DiFranzo, Malte F Jung, and Natalya N Bazarova. “I just shared your responses” extending communication privacy management theory to interac- tions with conversational agents. Proceedings of the ACM on Human-Computer Interaction, 4(GROUP):1–1...

    2020

  28. [36]

    Psychological safety: The history, renaissance, and future of an interpersonal construct

    Amy C Edmondson and Zhike Lei. Psychological safety: The history, renaissance, and future of an interpersonal construct. Annu. Rev. Organ. Psychol. Organ. Behav., 1(1):23–43, 2014

    2014

  29. [37]

    Exploring privacy management on facebook: Motiva- tions and perceived consequences of voluntary disclosure

    Susan Waters and James Ackerman. Exploring privacy management on facebook: Motiva- tions and perceived consequences of voluntary disclosure. Journal of Computer-Mediated Communication, 17(1):101–115, 2011

    2011

  30. [38]

    Manipulation and malicious personal- ization: exploring the self-disclosure biases exploited by deceptive attackers on social media

    Esma Aïmeur, Nicolás Díaz Ferreyra, and Hicham Hage. Manipulation and malicious personal- ization: exploring the self-disclosure biases exploited by deceptive attackers on social media. Frontiers in artificial intelligence, 2:26, 2019

    2019

  31. [39]

    Neglecting long-term risks: self-disclosure on social media and its relation to individual decision-making tendencies and problematic social- networks-use

    Sina Ostendorf, Silke M Müller, and Matthias Brand. Neglecting long-term risks: self-disclosure on social media and its relation to individual decision-making tendencies and problematic social- networks-use. Frontiers in Psychology, 11:543388, 2020

    2020

  32. [40]

    Entering night country: Reflections on self-disclosure and vulnerability

    Stephanie R Brody. Entering night country: Reflections on self-disclosure and vulnerability. Psychoanalytic Dialogues, 23(1):45–58, 2013

    2013

  33. [41]

    The privacy calculus contextualized: The influence of affordances

    Sabine Trepte, Michael Scharkow, and Tobias Dienlin. The privacy calculus contextualized: The influence of affordances. Computers in Human Behavior, 104:106115, 2020

    2020

  34. [42]

    AI systems and respect for human autonomy

    Arto Laitinen and Otto Sahlgren. AI systems and respect for human autonomy. Frontiers in artificial intelligence, 4:705164, 2021

    2021

  35. [43]

    Social psychological aspects of computer- mediated communication

    Sara Kiesler, Jane Siegel, and Timothy W McGuire. Social psychological aspects of computer- mediated communication. American psychologist, 39(10):1123, 1984

    1984

  36. [44]

    Self-disclosure in computer-mediated communication: The role of self- awareness and visual anonymity

    Adam N Joinson. Self-disclosure in computer-mediated communication: The role of self- awareness and visual anonymity. European journal of social psychology , 31(2):177–192, 2001

    2001

  37. [45]

    Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States

    Anastasia Kozyreva, Philipp Lorenz-Spreen, Ralph Hertwig, Stephan Lewandowsky, and Ste- fan M Herzog. Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States. Humanities and Social Scienc...

    2021

  38. [46]

    ‘I did it for the LULZ’: How the dark personality predicts online disinhibition and aggressive online behavior in adolescence

    Anna Kurek, Paul E Jose, and Jaimee Stuart. ‘I did it for the LULZ’: How the dark personality predicts online disinhibition and aggressive online behavior in adolescence. Computers in Human Behavior, 98:31–40, 2019

    2019

  39. [47]

    V oggeser, Ranjit K

    Birgit J. V oggeser, Ranjit K. Singh, and Anja S. Göritz. Self-control in online discussions: Disinhibited online behavior as a failure to recognize social cues. Frontiers in Psychology, 8, Jan 2018

    2018

  40. [48]

    The ethics of ChatGPT–Exploring the ethical issues of an emerging technology

    Bernd Carsten Stahl and Damian Eke. The ethics of ChatGPT–Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74:102700, 2024

    2024

  41. [49]

    Understanding artificial intelligence ethics and safety

    David Leslie. Understanding artificial intelligence ethics and safety. arXiv preprint arXiv:1906.05684, 2019

    1906 arXiv

  42. [50]

    From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices

    Jessica Morley, Luciano Floridi, Libby Kinsey, and Anat Elhalal. From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and engineering ethics, 26(4):2141–2168, 2020

    2020

  43. [51]

    in the loop

    Nandita Biswas Mellamphy. Humans “in the loop”?: Human-centrism, posthumanism, and AI. Nature and Culture, 16(1):11–27, 2021

    2021

  44. [52]

    Trust in AI and its role in the acceptance of AI technologies

    Hyesun Choung, Prabu David, and Arun Ross. Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9):1727–1739, 2023

    2023

  45. [53]

    The trustification of AI

    Jascha Bareis. The trustification of AI. Disclosing the bridging pillars that tie trust and AI together. Big Data & Society, 11(2):20539517241249430, 2024. 9

    2024

  46. [54]

    Aligning artificial intel- ligence with human values: reflections from a phenomenological perspective

    Shengnan Han, Eugene Kelly, Shahrokh Nikou, and Eric-Oluf Svee. Aligning artificial intel- ligence with human values: reflections from a phenomenological perspective. AI & Society, pages 1–13, 2022

    2022

  47. [55]

    Phenomenological approaches to ethics and information technology

    L Introna. Phenomenological approaches to ethics and information technology. 2005

    2005

  48. [56]

    Motivation, social emotion, and the acceptance of artificial intelligence virtual assistants—Trust-based mediating effects

    Shiying Zhang, Zixuan Meng, Beibei Chen, Xiu Yang, and Xinran Zhao. Motivation, social emotion, and the acceptance of artificial intelligence virtual assistants—Trust-based mediating effects. Frontiers in Psychology, 12:728495, 2021

    2021

  49. [57]

    Understanding sophia? On human interaction with artificial agents

    Thomas Fuchs. Understanding sophia? On human interaction with artificial agents. Phe- nomenology and the Cognitive Sciences, 23(1):21–42, Sep 2022

    2022

  50. [58]

    Philosophy, privacy, and pervasive computing

    Diane P Michelfelder. Philosophy, privacy, and pervasive computing. AI & Society, 25:61–70, 2010

    2010

  51. [59]

    The troubling emergence of hallucination in large language models–an extensive definition, quantification, and prescriptive remediations

    Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, SM Tonmoy, Aman Chadha, Amit P Sheth, and Amitava Das. The troubling emergence of hallucination in large language models–an extensive definition, quantification, and prescriptive remediations. arXiv preprint arX...

    arXiv

  52. [60]

    Humanity’s evolving conversations: AI as confidant, coach, and companion

    Brenda K Wiederhold. Humanity’s evolving conversations: AI as confidant, coach, and companion. Cyberpsychology, Behavior, and Social Networking, 2024

    2024

  53. [61]

    More than words

    Namkje Koudenburg, Ernestine H Gordijn, and Tom Postmes. “More than words” social validation in close relationships. Personality and Social Psychology Bulletin, 40(11):1517–1528, 2014

    2014

  54. [62]

    From text to self: Users’ perceptions of potential of AI on interpersonal communication and self

    Yue Fu, Sami Foell, Xuhai Xu, and Alexis Hiniker. From text to self: Users’ perceptions of potential of AI on interpersonal communication and self. arXiv preprint arXiv:2310.03976, 2023

    2023 arXiv

  55. [63]

    The role of institutional and self in the formation of trust in artificial intelligence technologies

    Lai-Wan Wong, Garry Wei-Han Tan, Keng-Boon Ooi, and Yogesh Dwivedi. The role of institutional and self in the formation of trust in artificial intelligence technologies. Internet Research, 34(2):343–370, 2024

    2024

  56. [64]

    Too human and not human enough: A grounded theory analysis of mental health harms from emotional dependence on the social chatbot Replika

    Linnea Laestadius, Andrea Bishop, Michael Gonzalez, Diana Illen ˇcík, and Celeste Campos- Castillo. Too human and not human enough: A grounded theory analysis of mental health harms from emotional dependence on the social chatbot Replika. New Media & Society , 26(10):5923–5941, 2024

    2024

  57. [65]

    Guidelines for human-AI interaction

    Saleema Amershi, Dan Weld, Mihaela V orvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. Guidelines for human-AI interaction. In Proceedings of the 2019 CHI conference on human factors in computing systems, pages...

    2019

  58. [66]

    The precautionary principle: A new legal standard for a technological age

    Roberto Andorno. The precautionary principle: A new legal standard for a technological age. 2004

    2004

  59. [67]

    Accountability in artificial intelli- gence: What it is and how it works

    Claudio Novelli, Mariarosaria Taddeo, and Luciano Floridi. Accountability in artificial intelli- gence: What it is and how it works. AI & Society, 39(4):1871–1882, 2024. 10

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.