Pith. sign in

REVIEW 3 major objections 4 minor 78 references

Healthy Distrust in AI systems

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read The paper argues that justified distrust toward AI usage practices—healthy distrust—is a desirable, cultivable stance rather than a failure of trust.

desk verdict A serious, honest conceptual paper: the descriptive case for justified distrust is solid, but the normative 'healthy' boundary is underspecified and needs work. read the letter →

arxiv 2505.09747 v1 pith:VNHMZGIL submitted 2025-05-14 cs.CY cs.AI

Benjamin Paa{ss}en , Suzana Alpsancar , Tobias Matzner , Ingrid Scharlau This is my paper · ORCID
classification cs.CYcs.AI
keywords healthydistrusttrustworthyAIhumanautonomyusagepracticesliteracyoversightriskanddanger
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 argues that the drive to make AI trustworthy has overlooked a legitimate and desirable state: justified distrust of AI usage practices, which the authors name 'healthy distrust.' They define it as a partly rational, partly affective careful or negative stance toward a particular AI usage practice in a particular socio-technical context—an intuition that something is not right about how the system is being used. Drawing on history, sociology, psychology, and philosophy, they show that existing concepts of trust and distrust either treat distrust as the absence of trust or tie it to detecting deception in human agents, neither of which fits the AI case. The paper claims that healthy distrust is not opposed to trust: it is a precondition for meaningful trust, meaningful human oversight, and informed consent, and it should be cultivated rather than engineered away. A sympathetic reader would take away that whether an AI is trustworthy is a separate question from whether a person does or should trust it.

What carries the argument

The load-bearing conceptual machinery is Luhmann's distinction between risk and danger, transferred to AI. Risk is possible damage resulting from one's own decisions; danger is possible damage from external sources that one cannot avoid by deciding. In AI, the practitioner or deploying institution takes a risk, while the data subject—the person affected by the decision—faces a danger outside their decision-making scope; because Luhmann links trust to risk and confidence to danger, the paper concludes that expecting data subjects to trust an AI is meaningless. The second piece of machinery is the psychological and organizational finding that trust and distrust are separable, coexisting dimensions rather than opposites, which lets the authors frame healthy distrust as an accompanying stance rather than a failure. Together these tools define healthy distrust as a specific, context-bound stance—rational and affective—toward a usage practice, whose 'health' is judged by what it trusts in turn and by the power context that enables it.

What would settle it

A field study in a high-stakes automated decision context (e.g., hiring or health screening) could test the central claim by measuring users' distrust, felt autonomy, and decision quality: if people with low distrust made better-calibrated decisions and reported more autonomy than people with high distrust, holding system accuracy and social context fixed, the paper's normative case for healthy distrust would be undercut.

Watch

Extended reading notes

Core claim

The central claim is that distrust can be justified and normatively appropriate toward AI systems even when the systems satisfy every criterion of trustworthy AI, because trustworthiness is a property of the system while distrust concerns the user's stance toward a usage practice embedded in a social context of power relations and interests. The authors propose 'healthy distrust' for this stance: partially rational and partially affective, a careful or negative attitude or intuition that something is not right about a specific usage practice. They argue that such distrust is often grounded not in technical failure but in the social embedding of the system—an insurer's incentive to overestimate risk, a workplace rule that forces compliance, a history of discriminatory outcomes—and that it can be a way of asserting autonomy under automation. On this account, distrust and trust are not opposite ends of one scale but separable dimensions that can coexist: one can trust a system to do what it was trained for while distrusting its use in one's case. The paper therefore positions healthy distrust as a necessary, or at least helpful, component of a critical stance that respects human autonomy, and as a complement to AI literacy centered on recognizing questionable usage practices.

Load-bearing premise

The argument stands on the Luhmann-inspired premise that people affected by AI decisions face a danger outside their own decision-making scope, so trust in the AI is not a meaningful option for them; if one rejects that framing or finds contexts where affected people genuinely choose the automation, the conclusion that distrust is the appropriate stance for them weakens.

Editorial extensions

If this is right

  • A system can be certified trustworthy and still be justifiably distrusted, so trustworthiness checklists are not enough to license deployment; the social embedding of the usage matters.
  • Design and regulation should aim to preserve room for healthy distrust—pause, questioning, override—rather than maximize smooth adoption, because that room is what makes trust meaningful.
  • Human oversight and human-in-the-loop concepts presuppose some distrust: overseers must be able to anticipate failure or suspect bad practice to intervene at all.
  • Informed consent toward AI usage practices is only meaningful if people can engage critically with the practice; healthy distrust supports that critical engagement.
  • Fostering healthy distrust belongs in AI literacy education, alongside knowledge and skills for using AI well, as the knowledge and skills for recognizing questionable usage.

Reading between the lines

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

  • Going beyond the paper: healthy distrust could be operationalized as a measurable construct distinct from trait distrust and generic skepticism, giving empirical AI research an outcome variable beyond trust scales.
  • Going beyond the paper: if healthy distrust matters, interfaces that preserve friction or a deliberate pause for scrutiny should improve the calibration of user trust, especially in high-stakes decisions—a directly testable design hypothesis.
  • Going beyond the paper: the account connects to explainable AI evaluation, where explanations should be assessed by whether they let users accurately distrust out-of-scope or misused systems, not only by whether they increase trust.
  • Going beyond the paper: because healthy distrust depends on resources and alternatives, the power-sensitive reading implies that institutions deploying AI carry a duty to make distrust actionable through training, contestability, and opt-outs.
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, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper argues that distrust toward AI systems can be justified and desirable, coining the term 'healthy distrust' to describe a partially rational, partially affective careful or negative stance toward specific AI usage practices in specific socio-technical contexts. The authors distinguish a descriptive claim (some affected people have good reason to distrust AI even when the system satisfies technical trustworthiness criteria) from a normative claim (such distrust is healthy, ought to be fostered, and is needed to protect human autonomy). They support this by reviewing notions of trust in history, philosophy, psychology, and computer science; by discussing examples such as health-insurance scoring and racial bias in AI; and by addressing objections in Sections 6 and 7, where they propose a preliminary definition and acknowledge unresolved conceptual overlap.

Significance. If the concept can be made precise, healthy distrust would fill a genuine gap in the trustworthy-AI literature, which largely treats trust as the desired endpoint and distrust as a failure or obstacle. The paper is strongest in its descriptive argument: the health-insurance and racial-bias examples show that distrust can be reasonable even for systems that meet formal trustworthiness requirements, and the review usefully highlights psychological evidence that trust and distrust can co-exist and that distrust has cognitive benefits. The authors also deserve credit for explicitly engaging with power asymmetries, for arguing that distrust is not a property of a system but of a stance toward a usage practice, and for connecting the concept to AI literacy, human oversight, and informed consent. The interdisciplinary literature review is broad and well-referenced, and the central claim is not defined circularly in terms of the authors' own prior work.

major comments (3)
  1. [Section 6 and Section 7] The normative component of the central claim is not secured because the paper never supplies a demarcation criterion for 'healthy'. Section 6 defines healthy distrust as rooted in 'knowledge, reasoning, or at least intuition' and as requiring that the distrust trust something with 'some relation to what the technology does, or is meant to do'; Section 7 then includes 'pre-rational hesitancy and reluctance' in the concept. These conditions are also satisfied by a conspiracy belief about an AI system that is held on the basis of an intuitive alternative relation and that makes claims about what the technology is meant to do. Since Section 5 cites Thielmann and Hilbig (2023) as showing that generalized distrust is a causal precondition of conspiracy mentality, the reader has no way to tell whether the recommended cultivation of distrust promotes autonomy or feeds pathology. The objection in Section 7 ('How long should an institution wait...') is acknowledged but not answered with a criterion; the reply that AI hype warrants delay supports a permission for distrust in specific cases, not the general normative conclusion that distrust 'ought to be fostered and cultivated'. A concrete fix would be to add explicit conditions such as proportionality to evidence, openness to revision, targeting of specific usage practices rather than diffuse suspicion, and action-readiness.
  2. [Section 4] The transfer of Luhmann's risk/danger distinction to AI usage is load-bearing for the claim that data subjects cannot meaningfully trust AI, but it is presented as a given rather than a contested theoretical choice. There are two specific gaps. First, Luhmann's distinction links danger to confidence ('Zuversicht'), not to distrust, so the move from 'data subjects face danger rather than risk' to 'data subjects should adopt a stance of healthy distrust' is not licensed by Luhmann himself. Second, the paper applies 'data subjects' as a blanket category, but a data subject who can decline a service, switch providers, or consent to processing does have decision-making scope; in such cases Luhmann's own framework would classify the situation as risk rather than danger, and trust would become meaningful again. The descriptive examples in Sections 2 and 6 (health-insurance scoring, racial bias) do not need this sociological premise. The paper could weaken the claim to 'in contexts where affected people lack decisional control, distrust is a legitimate stance' and thereby avoid making the argument depend on acceptance of the Luhmannian frame. As written, the argument for why distrust rather than confidence is the appropriate stance for data subjects loses its basis for readers who do not accept that framework.
  3. [Section 1 and Section 7] There is an unclosed gap between the hedged conceptual claims and the prescriptive conclusion. Section 7 says healthy distrust 'may be a necessary, or at least helpful, part of such a critical stance' and acknowledges that it may lead to under-utilization of AI; the rebuttal is that past harms and AI hype make delay 'warranted' for many contemporary usage practices. This supports a permission to distrust in cases with identifiable reasons, but it does not support the stronger prescription in Section 1 that distrust 'ought to be fostered and cultivated'. To support the stronger claim, the paper would need to specify which actors (individuals, institutions, educators), which usage practices, and what safeguards against the documented costs of generalized distrust (Thielmann and Hilbig 2023) are intended. Absent such scope conditions, the normative claim is broader than the argument.
minor comments (4)
  1. [Section 4] The text contains a duplicated article: 'the different perspectives on trust proposed by the the predicative vs. the affective view of trust' should read 'by the predicative vs. the affective view'.
  2. [Section 7] The phrase 'finally make an an informed decision about it' contains a duplicated 'an'.
  3. [Acknowledgements] The acknowledgement line 'WegratefullyacknowledgefundingbytheGermanResearchFoundation' lacks spaces between words; this appears to be a formatting error.
  4. [Section 5] The footnote explaining the choice of 'healthy distrust' says the pairing is with 'the clearly negative word mistrust', while the rest of the paper consistently uses 'distrust'; these terms should be reconciled or the distinction explained.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the concept of healthy distrust is assembled from external historical, sociological, psychological, and philosophical sources, and the few self-citations supply empirical or conceptual support that is independently checkable.

full rationale

This paper is a conceptual and normative proposal rather than a derivation, so the main circularity patterns—fitted inputs renamed as predictions, uniqueness theorems imported from the authors' own prior work, or ansatz smuggled in via self-citation—do not apply. The central claim, that a justified, careful stance of distrust toward certain AI usage practices deserves the label 'healthy distrust' and should be fostered, is argued from external sources: Frevert on the history of trust, Luhmann's risk/danger distinction, psychological research on distrust benefits (Schul et al.), Thielmann and Hilbig on generalized distrust, Mayo's Cartesian/evaluative mindset, and Barad's and Matzner's work on technological perspectives and power. The self-citations (Peters and Scharlau 2025; Visser et al. 2025; Matzner 2024) are used for empirical observations—trust and distrust can co-exist, and trustworthiness does not imply trust—and for a published theoretical perspective on algorithms. These observations are not the conclusion being argued, and they are corroborated by non-self citations as well, such as Schul et al. 2008 and Lewicki et al. 1998. No equation or definition reduces to its own input: 'healthy distrust' is defined through a list of observations and normative commitments rather than by fitting data, so no prediction is forced by construction. The acknowledged limitations, such as the broad 'healthy' criterion, the risk of under-utilization of AI, and the boundary with conspiracy mentality, are substantive normative objections rather than instances of circular reasoning. Therefore the paper does not exhibit circular derivation; at most it contains minor self-citations that are not load-bearing.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

No free parameters are applicable; the paper makes no quantitative claims. The axioms are the background assumptions needed for the conceptual argument, including the contested use of Luhmann's risk/danger distinction. One invented entity, the concept 'healthy distrust', is introduced without independent empirical validation.

assumptions (4)
  • domain assumption Trust and distrust can coexist as separate dimensions toward the same object.
    The paper relies on this empirical claim (supported by Lewicki et al. 1998, Schul et al. 2008, Basel and Brühl 2023) to argue that healthy distrust does not preclude appropriate trust. It is foundational to the concept.
  • domain assumption Technical trustworthiness (compliance with design requirements) does not imply users should trust a system.
    The paper uses this to argue that a system can pass all trustworthy AI criteria while the user's distrust remains justified (Section 2, health insurance example).
  • ad hoc to paper Luhmann's distinction between risk and danger applies to AI usage, so data subjects face danger rather than risk and cannot meaningfully trust the system.
    This is a contested sociological framework adopted to conclude that distrust, not trust, is appropriate for affected individuals (Section 4). The paper does not critically examine alternative frameworks.
  • domain assumption Human autonomy is a value that AI usage should respect.
    The normative conclusion that healthy distrust is desirable depends on this ethical premise, which is asserted rather than argued.
invented entities (1)
  • Healthy distrust
    purpose: A term for a justified, careful, partially rational and partially affective stance toward AI usage practices in a specific socio-technical context, intended to counterbalance the one-sided valorization of trust in trustworthy AI research.
    The concept is defined by the authors and is not empirically validated; no measurement instrument or falsifiable prediction is provided. Its value is normative and descriptive, not a new observation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Healthy Distrust in AI systems." pith.science (2026). https://pith.science/paper/VNHMZGIL

@misc{pith2026250509747,
  author       = {Pith},
  title        = {Pith review of: Healthy Distrust in AI systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VNHMZGIL}},
  note         = {Machine review of arXiv:2505.09747}
}
read the original abstract

Under the slogan of trustworthy AI, much of contemporary AI research is focused on designing AI systems and usage practices that inspire human trust and, thus, enhance adoption of AI systems. However, a person affected by an AI system may not be convinced by AI system design alone -- neither should they, if the AI system is embedded in a social context that gives good reason to believe that it is used in tension with a person's interest. In such cases, distrust in the system may be justified and necessary to build meaningful trust in the first place. We propose the term "healthy distrust" to describe such a justified, careful stance towards certain AI usage practices. We investigate prior notions of trust and distrust in computer science, sociology, history, psychology, and philosophy, outline a remaining gap that healthy distrust might fill and conceptualize healthy distrust as a crucial part for AI usage that respects human autonomy.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

78 extracted references · 52 canonical work pages

  1. [1]

    The western psychologization of global development: A cultural and decolonial approach

    Johanna Sofia Adolfsson and Gertrude Finyiza. The western psychologization of global development: A cultural and decolonial approach. Theory & Psychology, 34 0 (6): 0 713--735, 2024. doi:10.1177/09593543241279122

    work page doi:10.1177/09593543241279122 2024

  2. [2]

    Machine bias

    Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias. Pro Publica, 2016. URL https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

    work page 2016

  3. [3]

    Surrogate Humanity

    Neda Atanasoski and Kalindi Vora. Surrogate Humanity. Duke University Press, Durham, NC, USA, 2019

    work page 2019

  4. [4]

    Trust and antitrust

    Annette Baier. Trust and antitrust. Ethics, 96 0 (2): 0 231--260, 1986. doi:10.1086/292745

    doi:10.1086/292745 1986

  5. [5]

    Meeting the universe halfway

    Karen Barad. Meeting the universe halfway. Duke University Press, Durham, NC, USA, 2007

    work page 2007

  6. [6]

    Unlocking Luhmann; Luhmann in Glossario

    Claudio Baraldi, Giancarlo Corsi, and Elena Esposito. Unlocking Luhmann; Luhmann in Glossario. I concetti fondamentali della teoria: A Keyword Introduction to Systems Theory. Bielefeld University Press, Bielefeld, Germany, 2021. URL https://www.transcript-verlag.de/978-3-8376-5674-9

    work page 2021

  7. [7]

    When is automated decision making legitimate? In Fairness and Machine Learning: Limitations and Opportunities

    Solon Barocas, Moritz Hardt, and Arvind Narayanan. When is automated decision making legitimate? In Fairness and Machine Learning: Limitations and Opportunities. The MIT Press, Cambridge, MA, USA, 2023. URL https://fairmlbook.org/legitimacy.html

    work page 2023

  8. [8]

    o rn Basel and Rolf Br \

    J \"o rn Basel and Rolf Br \"u hl. Misstrauen. eine interdisziplin \"a re bestandsaufnahme. In J \"o rn Basel and Philipp Henrizi, editors, Psychologie von Risiko und Vertrauen: Wahrnehmung, Verhalten und Kommunikation, pages 271--301. Springer, Berlin/Heidelberg, Germany, 2023. doi:10.1007/978-3-662-65575-7_11

    doi:10.1007/978-3-662-65575-7_11 2023

Show all 78 references
  1. [9]

    Wissen oder nicht-wissen? zwei perspektiven reflexiver modernisierung

    Ulrich Beck. Wissen oder nicht-wissen? zwei perspektiven reflexiver modernisierung. In Ulrich Beck, Anthony Giddens, and Scott Lash, editors, Reflexive Modernisierung, pages 286--315. Suhrkamp, Frankfurt a.M., Germany, 1996

    1996

  2. [10]

    Race After Technology: Abolitionist Tools for the New Jim Code

    Ruha Benjamin. Race After Technology: Abolitionist Tools for the New Jim Code. Polity, Cambridge, UK, 2019

    2019

  3. [11]

    The values encoded in machine learning research

    Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. The values encoded in machine learning research. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT), page 173–184, 2022. doi:10.1145/353114...

    2022

  4. [12]

    Gender shades: Intersectional accuracy disparities in commercial gender classification

    Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAccT), pages 77--91, 2018. URL https://proceedings.mlr.press/v81/buolamw...

    2018

  5. [13]

    How the machine ‘thinks’: Understanding opacity in machine learning algorithms

    Jenna Burrell. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3 0 (1): 0 2053951715622512, 2016. doi:10.1177/2053951715622512

    2016 doi

  6. [14]

    Hendrik Buschmeier, Heike M. Buhl, Friederike Kern, Angela Grimminger, Helen Beierling, Josephine Fisher, André Groß, Ilona Horwath, Nils Klowait, Stefan Lazarov, Michael Lenke, Vivien Lohmer, Katharina Rohlfing, Ingrid Scharlau, Amit Singh, Lutz Terfloth, Anna-Lisa Vollmer, Y...

    2023

  7. [15]

    Vertrauen, 2000

    Christoph Clases and Theo Wehner. Vertrauen, 2000. URL https://www.spektrum.de/lexikon/psychologie/vertrauen/16374

    2000

  8. [16]

    Can we trust robots? Ethics and information technology, 14: 0 53--60, 2012

    Mark Coeckelbergh. Can we trust robots? Ethics and information technology, 14: 0 53--60, 2012. doi:10.1007/s10676-011-9279-1

    2012 doi

  9. [17]

    Cognitive adaptations for social exchange

    Leda Cosmides and John Tooby. Cognitive adaptations for social exchange. In Jerome H. Barkow, Leda Cosmides, and John Tooby, editors, The Adapted Mind: Evolutionary Psychology and the Generation of Culture, pages 163--227. Oxford University Press, Oxford, UK, 1992

    1992

  10. [18]

    Anthropomorphization of ai: Opportunities and risks, 2023

    Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan, and Ashwin Kalyan. Anthropomorphization of ai: Opportunities and risks, 2023. URL https://arxiv.org/abs/2305.14784

    2023 arXiv

  11. [19]

    Catherine D'Ignazio and Lauren F. Klein. Data Feminism. The MIT Press, Cambridge, MA, USA, 2020

    2020

  12. [20]

    Regulation of tech forces-technological backlash: The luddite perspective

    Larry Earnhart. Regulation of tech forces-technological backlash: The luddite perspective. In Abeba N. Turi and Pooja Lekhi, editors, Innovation, Sustainability, and Technological Megatrends in the Face of Uncertainties: Core Developments and Solutions, pages 179--200. Springe...

    2024 doi

  13. [21]

    Troubleshooting ai and consent

    Elizabeth Edenberg and Meg Leta Jones. Troubleshooting ai and consent. In Markus Dirk Dubber, Frank Pasquale, and Sunit Das, editors, The Oxford Handbook of Ethics of AI. Oxford University Press, Oxford, UK, 2020. doi:10.1093/oxfordhb/9780190067397.013.23

    2020

  14. [22]

    healthy personality

    Erik H Erikson. Growth and crises of the "healthy personality". In M. J. E. Senn, editor, Proceedings of the Symposium on the healthy personality, pages 91--146. Josiah Macy, Jr. Foundation, 1950

    1950

  15. [23]

    Automating Inequality

    Virginia Eubanks. Automating Inequality. St. Martin's Press, New York, NY, USA, 2017

    2017

  16. [24]

    Making sense of the conceptual nonsense ‘ trustworthy AI ’

    Ori Freiman. Making sense of the conceptual nonsense ‘ trustworthy AI ’. AI and Ethics, 3 0 (4): 0 1351--1360, 2023. doi:10.1007/s43681-022-00241-w

    2023 doi

  17. [25]

    Vertrauensfragen: Eine Obsession der Moderne

    Ute Frevert. Vertrauensfragen: Eine Obsession der Moderne. UTB : 2185. C. H. Beck, Munich, Germany, 2013. ISBN 9783406656095

    2013

  18. [26]

    Foundations of communication/media/digital (in)justice

    Christian Fuchs. Foundations of communication/media/digital (in)justice. Journal of Media Ethics, 36 0 (4): 0 186--201, 2021. doi:10.1080/23736992.2021.1964968

    2021

  19. [27]

    Galloway

    Alexander R. Galloway. The interface effect. Polity, Cambridge, UK; Malden, MA, USA, 2012. ISBN 978-0-7456-6252-7

    2012

  20. [28]

    Automation bias: a systematic review of frequency, effect mediators, and mitigators

    Kate Goddard, Abdul Roudsari, and Jeremy C Wyatt. Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19 0 (1): 0 121--127, 06 2011. doi:10.1136/amiajnl-2011-000089

    2011 doi

  21. [29]

    Trust and reliance 1

    Sanford C Goldberg. Trust and reliance 1. The Routledge handbook of trust and philosophy, pages 97--108, 2020

    2020

  22. [30]

    Trust in artificial agents

    Frances Grodzinsky, Keith Miller, and Marty J Wolf. Trust in artificial agents. In The Routledge handbook of trust and philosophy, pages 298--312. Routledge, Milton Park, UK, 2020

    2020

  23. [31]

    Are you anthropomorphizing AI ?, 2024

    Ali Hasan. Are you anthropomorphizing AI ?, 2024. URL https://blog.apaonline.org/2024/08/20/are-you-anthropomorphizing-ai-2/

    2024

  24. [32]

    Hoffman, Shane T

    Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman. Measures for explainable AI : Explanation goodness, user satisfaction, mental models, curiosity, trust, and human- AI performance. Frontiers in Computer Science, Volume 5 - 2023, 2023. doi:10.3389/fcomp.2023.1096257

    2023

  25. [33]

    Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong H...

    2024 arXiv

  26. [34]

    Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M

    Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paassen, Sascha Saralajew, Ute Schmid, Michael Biehl, Marianna Bolognesi, Xin Luna Dong, Kiril Gashteovski, Pascal Hitzler, Giuseppe Marra, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltr...

    2024 arXiv

  27. [35]

    AI alignment: A comprehensive survey

    Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O'Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, S...

    arXiv

  28. [36]

    Begriffe in Modellen: Die Modellierung von Vertrauen in Computersimulation und maschinellem Lernen im Spiegel der Theoriegeschichte von Vertrauen

    Andreas Kaminski. Begriffe in Modellen: Die Modellierung von Vertrauen in Computersimulation und maschinellem Lernen im Spiegel der Theoriegeschichte von Vertrauen . In Nicole J. Saam, Michael Resch, and Andreas Kaminski, editors, Simulieren und Entscheiden: ozialwissenschaftl...

    2019 doi

  29. [37]

    On the relation of trust and explainability: Why to engineer for trustworthiness

    Lena Kastner, Markus Langer, Veronika Lazar, Astrid Schomacker, Timo Speith, and Sarah Sterz. On the relation of trust and explainability: Why to engineer for trustworthiness. In Proceedings of the 29th International Requirements Engineering Conference Workshops (REW), pages 1...

    2021

  30. [38]

    Rittichier, and Arjan Durresi

    Davinder Kaur, Suleyman Uslu, Kaley J. Rittichier, and Arjan Durresi. Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55 0 (2), January 2022. doi:10.1145/3491209

    2022 doi

  31. [39]

    There is no software

    Friedrich Kittler. There is no software. Stanford Literature Review, 9 0 (1): 0 81–90, 1992

    1992

  32. [40]

    Introduction matters: Manipulating trust in automation and reliance in automated driving

    Moritz Körber, Eva Baseler, and Klaus Bengler. Introduction matters: Manipulating trust in automation and reliance in automated driving. Applied Ergonomics, 66 0 (Munich): 0 18--31, jan 2018. doi:10.1016/j.apergo.2017.07.006

    2018 doi

  33. [41]

    Trust and emotion

    Bernd Lahno. Trust and emotion. In The Routledge handbook of trust and philosophy, pages 147--159. Routledge, Milton Park, UK, 2020

    2020

  34. [42]

    Trustworthy artificial intelligence and the European Union AI act : On the conflation of trustworthiness and acceptability of risk

    Johann Laux, Sandra Wachter, and Brent Mittelstadt. Trustworthy artificial intelligence and the European Union AI act : On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18 0 (1): 0 3--32, 2024. doi:https://doi.org/10.1111/rego.12512

    2024 doi

  35. [43]

    Lewicki, Daniel J

    Roy J. Lewicki, Daniel J. McAllister, and Robert J. Bies. Trust and distrust: New relationships and realities. Academy of Management Review, 23 0 (3): 0 438--458, 1998. ISSN 0363-7425. doi:10.5465/amr.1998.926620

    1998

  36. [44]

    What is AI literacy? competencies and design considerations

    Duri Long and Brian Magerko. What is AI literacy? competencies and design considerations. In Proceedings of the 2020 Conference on Human Factors in Computing Systems (CHI), CHI '20, page 1–16, 2020. doi:10.1145/3313831.3376727

    2020

  37. [45]

    Technology, environment and social risk: a systems perspective

    Niklas Luhmann. Technology, environment and social risk: a systems perspective. Industrial Crisis Quarterly, 4 0 (3): 0 223--231, 1990. URL http://www.jstor.org/stable/26162777

    1990

  38. [46]

    O kologie des Nichtwissens , pages 149--220. VS Verlag f \

    Niklas Luhmann. \"O kologie des Nichtwissens , pages 149--220. VS Verlag f \"u r Sozialwissenschaften, Wiesbaden, 1992. doi:10.1007/978-3-322-93617-2_5

    1992 doi

  39. [47]

    Vertrauen

    Niklas Luhmann. Vertrauen. Lucius & Lucius Verlagsgesellschaft mbH, Stuttgart, Germany, 4th edition, 2000

    2000

  40. [48]

    Risk: a sociological theory

    Niklas Luhmann. Risk: a sociological theory. Routledge, Milton Park, UK, 2017

    2017

  41. [49]

    Miquelle A. G. Marchand and Roos Vonk. The process of becoming suspicious of ulterior motives. Social Cognition, 23 0 (3): 0 242--256, 2005. doi:10.1521/soco.2005.23.3.242

    2005 doi

  42. [50]

    The human is dead – long live the algorithm! human-algorithmic ensembles and liberal subjectivity

    Tobias Matzner. The human is dead – long live the algorithm! human-algorithmic ensembles and liberal subjectivity. Theory, Culture and Society, 36 0 (2): 0 123–144, 2019. ISSN 0263-2764. doi:10.1177/0263276418818877

    2019 doi

  43. [51]

    Algorithms

    Tobias Matzner. Algorithms. Routledge, Milton Park, UK, 2024

    2024

  44. [52]

    Do-it-yourself data protection—empowerment or burden? In Ronald Leenes Gutwirth, Serge and Paul De Hert, editors, Data Protection on the Move, page 277–305

    Tobias Matzner, Philipp K Masur, Carsten Ochs, and Thilo von Pape. Do-it-yourself data protection—empowerment or burden? In Ronald Leenes Gutwirth, Serge and Paul De Hert, editors, Data Protection on the Move, page 277–305. Springer, Dordrecht, Netherlands, 2016. doi:10.1007/9...

    2016 doi

  45. [53]

    Suspicious spirits, flexible minds: when distrust enhances creativity

    Jennifer Mayer and Thomas Mussweiler. Suspicious spirits, flexible minds: when distrust enhances creativity. Journal of Personality and Social Psychology, 101 0 (6): 0 1262--1277, 2011. doi:10.1037/a0024407

    2011 doi

  46. [54]

    Trust or distrust? Neither! The right mindset for confronting disinformation

    Ruth Mayo. Trust or distrust? Neither! The right mindset for confronting disinformation . Current Opinion in Psychology, 56: 0 101779, 2024. doi:https://doi.org/10.1016/j.copsyc.2023.101779

    2024

  47. [55]

    Debates on the nature of artificial general intelligence

    Melanie Mitchell. Debates on the nature of artificial general intelligence. Science, 383 0 (6689): 0 eado7069, 2024. doi:10.1126/science.ado7069

    2024 doi

  48. [56]

    Introduction: Approximating mistrust

    Florian Mühlfried. Introduction: Approximating mistrust. In Florian Mühlfried, editor, Mistrust: Ethnographic Approximations, pages 7--22. transcript, Bielefeld, 2023. doi:10.14361/9783839439234-001

    2023 doi

  49. [57]

    The power of the truth bias: False information affects memory and judgment even in the absence of distraction

    Myrto Pantazi, Mikhail Kissine, and Olivier Klein. The power of the truth bias: False information affects memory and judgment even in the absence of distraction. Social Cognition, 36 0 (2): 0 167--198, 2018. doi:https://doi.org/10.1521/soco.2018.36.2.167

    2018 doi

  50. [58]

    Peters and Ingrid Scharlau

    Tobias M. Peters and Ingrid Scharlau. Interacting with fallible AI : Is distrust helpful when receiving AI misclassifications? Frontiers in Psychology, 2025. doi:10.3389/fpsyg.2025.1574809

    2025

  51. [59]

    Anthropomorphism in AI : hype and fallacy

    Adriana Placani. Anthropomorphism in AI : hype and fallacy. AI and Ethics, 4 0 (3): 0 691--698, 2024. doi:10.1007/s43681-024-00419-4

    2024 doi

  52. [60]

    A new scale for the measurement of interpersonal trust

    Julian B Rotter. A new scale for the measurement of interpersonal trust. Journal of Personality, pages 651--665, 1967. doi:https://doi.org/10.1111/j.1467-6494.1967.tb01454.x

    1967

  53. [61]

    Salovich, Anya M

    Nikita A. Salovich, Anya M. Kirsch, and David N. Rapp. Evaluative mindsets can protect against the influence of false information. Cognition, 225: 0 105121, 2022. doi:10.1016/j.cognition.2022.105121

    2022

  54. [62]

    To trust or distrust trust measures: Validating questionnaires for trust in AI

    Nicolas Scharowski, Sebastian AC Perrig, Lena Fanya Aeschbach, Nick von Felten, Klaus Opwis, Philipp Wintersberger, and Florian Br \"u hlmann. To trust or distrust trust measures: Validating questionnaires for trust in AI . arXiv preprint arXiv:2403.00582, 2024. doi:10.48550/a...

    2024 doi arXiv

  55. [63]

    How do we assess the trustworthiness of AI ? introducing the trustworthiness assessment model ( TrAM )

    Nadine Schlicker, Kevin Baum, Alarith Uhde, Sarah Sterz, Martin C Hirsch, and Markus Langer. How do we assess the trustworthiness of AI ? introducing the trustworthiness assessment model ( TrAM ). Computers in Human Behavior, page 108671, 2025

    2025

  56. [64]

    Encoding under trust and distrust: The spontaneous activation of incongruent cognitions

    Yaacov Schul, Ruth Mayo, and Eugene Burnstein. Encoding under trust and distrust: The spontaneous activation of incongruent cognitions. Journal of Personality and Social Psychology, 86 0 (5): 0 668–679, 2004. doi:https://doi.org/10.1037/0022-3514.86.5.668

    2004 doi

  57. [65]

    The value of distrust

    Yaacov Schul, Ruth Mayo, and Eugene Burnstein. The value of distrust. Journal of Experimental Social Psychology, 44 0 (5): 0 1293--1302, 2008. doi:10.1016/j.jesp.2008.05.003

    2008 doi

  58. [66]

    Anthropomorphization and beyond: conceptualizing humanwashing of ai-enabled machines

    Gabriela Scorici, Mario D Schultz, and Peter Seele. Anthropomorphization and beyond: conceptualizing humanwashing of ai-enabled machines. AI & SOCIETY, 39 0 (2): 0 789--795, 2024. doi:10.1007/s00146-022-01492-1

    2024 doi

  59. [67]

    The Routledge handbook of trust and philosophy

    Judith Simon. The Routledge handbook of trust and philosophy. Routledge, Milton Park, UK, 2020

    2020

  60. [68]

    Ethics guidelines for trustworthy AI

    Nathalie Smuha. Ethics guidelines for trustworthy AI . Technical report, EU High-Level Expert Group on Artificial Intelligence, 2019. URL https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

    2019

  61. [69]

    How the EU can achieve legally trustworthy AI : a response to the European Commission 's proposal for an artificial intelligence act

    Nathalie A Smuha, Emma Ahmed-Rengers, Adam Harkens, Wenlong Li, James MacLaren, Riccardo Piselli, and Karen Yeung. How the EU can achieve legally trustworthy AI : a response to the European Commission 's proposal for an artificial intelligence act. SSRN, 2021. doi:10.2139/ssrn.3899991

    2021 doi

  62. [70]

    On the quest for effectiveness in human oversight: Interdisciplinary perspectives

    Sarah Sterz, Kevin Baum, Sebastian Biewer, Holger Hermanns, Anne Lauber-R\" o nsberg, Philip Meinel, and Markus Langer. On the quest for effectiveness in human oversight: Interdisciplinary perspectives. In Proceedings of the ACM Conference on Fairness, Accountability, and Tran...

    2024

  63. [71]

    Isabel Thielmann and Benjamin E. Hilbig. Generalized dispositional distrust as the common core of populism and conspiracy mentality. Political Psychology, 44 0 (4): 0 789--805, 2023. doi:https://doi.org/10.1111/pops.12886

    2023 doi

  64. [72]

    Do we trust in ai? role of anthropomorphism and intelligence

    Indrit Troshani, Sally Rao Hill, Claire Sherman, and Damien Arthur and. Do we trust in ai? role of anthropomorphism and intelligence. Journal of Computer Information Systems, 61 0 (5): 0 481--491, 2021. doi:10.1080/08874417.2020.1788473

    2021

  65. [73]

    Jessica Udry and Sarah J. Barber. The illusory truth effect: A review of how repetition increases belief in misinformation. Current Opinion in Psychology, 56: 0 101736, 2024. doi:10.1016/j.copsyc.2023.101736

    2024

  66. [74]

    Peters, Ingrid Scharlau, and Barbara Hammer

    Roel Visser, Tobias M. Peters, Ingrid Scharlau, and Barbara Hammer. Trust, distrust, and appropriate reliance in (X)AI : a survey of empirical evaluation of user trust. Cognitive Systems Research, 2025. accepted

    2025

  67. [75]

    Nathan Walter and Riva Tukachinsky. A meta-analytic examination of the continued influence of misinformation in the face of correction: How powerful is it, why does it happen, and how to stop it? Communication Research, 47 0 (2): 0 155--177, 2020. doi:https://doi.org/10.1177/0...

    2020 doi

  68. [76]

    On certainty, volume 174

    Ludwig Wittgenstein. On certainty, volume 174. Basil Blackwell, Oxford, UK, 1969

    1969

  69. [77]

    Linda M. G. Zerilli. Doing without knowing: Feminism's politics of the ordinary. Political Theory, 26 0 (4): 0 435--458, 1998. doi:10.1177/0090591798026004001

    1998 doi

  70. [78]

    Briefing Right to repair

    Nikolina Šajn. Briefing Right to repair. Technical Report PE 698.869, European Parliamentary Research Service, Brussels, 2022. URL https://www.europarl.europa.eu/thinktank/de/document/EPRS_BRI(2022)698869

    2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.