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REVIEW 4 major objections 7 minor 77 references

AI Trust Reshaping Administrative Burdens: Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that trust in an LLM along competence, integrity, and benevolence determines whether AI-assisted benefits systems like SNAP reduce administrative burdens or introduce new ones.

desk verdict A careful exploratory interview study worth refereeing, but the Discussion overstates actual effects: much of the 'new burden' evidence is participants' anticipated or hypothetical costs, not measured effects of using the prototype. read the letter →

arxiv 2505.22418 v1 pith:OLXGOTJS submitted 2025-05-28 cs.HC

Jeongwon Jo , He Zhang , Jie Cai , Nitesh Goyal This is my paper
classification cs.HC
keywords administrativeburdentrustinAIlargelanguagemodelsSNAPpublicbenefitscalibrationhuman-AIinteractionqualitativeinterviews
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that LLM-powered assistants for public benefits like SNAP do not simply reduce the hassle of applying for benefits; they change what counts as hassle. Drawing on interviews with ten SNAP applicants who had faced rejection, it proposes that trust in the AI along three lines—competence, integrity, and benevolence—determines whether the system lowers the familiar learning, psychological, and compliance costs of benefits administration or creates new ones. Some new costs arise from mistrust, such as extra effort spent double-checking answers; others arise from overtrust, such as accepting faulty guidance without verification. If this trust-burden dynamic holds, designing such systems is less about maximizing trust and more about calibrating it to the task and the evidence.

What carries the argument

The central object is the trust-burden framework that maps three trust dimensions—competence (perceived ability to perform), integrity (perceived adherence to principles), and benevolence (perceived good intentions toward the user)—onto the three administrative burden types from the literature on administrative burdens: learning costs (understanding rules and procedures), psychological costs (stress, stigma, loss of autonomy), and compliance costs (time and money spent meeting requirements). The framework is built from a prompt-engineered GPT-4o prototype, SNAP-LLM, grounded in Indiana's SNAP/TANF policy manual, which interview participants used briefly under controlled conditions; their open-ended responses were coded thematically, with trust emerging as the mediator that reorganizes the burden categories.

What would settle it

A study with a larger, demographically representative sample of SNAP applicants who use a deployed LLM assistant over several weeks could falsify the framework by showing that variations in competence, integrity, and benevolence trust do not line up with the reported learning, psychological, and compliance costs—for example, if high benevolence trust coincides with high psychological costs, or if users with low competence trust report no additional verification burden.

Watch

Extended reading notes

Core claim

The central claim is that an LLM-assisted benefits system should be understood as a trust-burden mediator: users' beliefs about the system's ability (competence), honesty (integrity), and goodwill (benevolence) shape each of the three administrative burden categories. The authors identify new burden subtypes introduced by LLM use—for example, learning costs about data security protocols, psychological costs from the perceived loss of human accompaniment, and compliance costs from verification of AI answers—and show that the same trust beliefs that reduce traditional burdens can produce these new ones. The paper frames this as an initial framework for trust-burden dynamics in AI-assisted administration and concludes that agencies should disclose evidence-based performance information rather than let applicants form preferences from beliefs.

Load-bearing premise

The framework rests on ten self-selected Reddit users who had been rejected for SNAP at least once, reported above-average digital literacy, and used a non-deployed prototype for a brief, researcher-facilitated session; if these users are not representative of typical SNAP applicants, the claimed trust-burden dynamics may not generalize.

Editorial extensions

If this is right

  • LLM assistants can lower traditional barriers—clearer policy explanations, 24/7 access, and reduced stigma—but agencies should plan for new burden types that appear only when AI is introduced.
  • Trust calibration, not trust maximization, is the design goal: features such as source attribution, currency indicators, and cognitive forcing functions can reduce overtrust-driven compliance costs.
  • Hybrid designs that let users choose human caseworkers for sensitive stages and AI for routine information tasks can preserve the psychological value of accompaniment while capturing efficiency gains.
  • Evidence-based information disclosure—comparing accuracy and consistency of human versus AI sources—could support more informed choices, though beliefs may override disclosed metrics.
  • The framework implies that evaluations of AI in public services should measure burden changes alongside accuracy, not accuracy alone.

Reading between the lines

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

  • The trust-burden mapping suggests a testable mechanism: interventions that increase one trust dimension (e.g., showing integrity through source citations) might reduce specific burden subtypes while leaving others unchanged, allowing agencies to target design features to the cost they most need to cut.
  • The authors' finding that participants conflated the informational LLM with a decision-making system implies that deployed systems may need explicit role disclosure (e.g., 'this system advises, it does not decide') to avoid misplaced integrity concerns—an extension not tested in the interviews.
  • If overtrust is as widespread as the five participants who skipped verification, then the framework predicts that purely user-side transparency tools may fail; burden reduction may require system-side safeguards such as automated verification of outputs against the policy manual.
  • The sample's high digital literacy suggests the framework may need recalibration for populations with lower digital fluency, where the learning burdens of using an LLM interface could dominate the traditional learning costs they replace.
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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

4 major / 7 minor

Summary. This paper reports a qualitative interview study with 10 SNAP applicants who had experienced at least one rejection. Participants discussed their prior experiences with SNAP, interacted with a GPT-4o-based prototype called SNAP-LLM, wrote hypothetical usage scenarios, and stated preferences between human caseworkers and LLM-based support. The authors use thematic analysis to organize findings around the three administrative-burden cost categories (learning, psychological, compliance) and the three trust dimensions (competence, integrity, benevolence). The paper claims that LLM-assisted benefits systems can alleviate traditional burdens but also introduce new psychological, learning, and compliance costs, and that users' trust in LLMs shapes these emerging costs. It concludes with design implications for calibrating trust and disclosing evidence-based performance information.

Significance. If the central claim holds, the paper makes a useful contribution by connecting the administrative-burden framework to citizen-facing LLM systems and by showing how trust dimensions may moderate those burdens. The use of a concrete prototype rather than purely abstract vignettes, the detailed interview protocol, and the inclusion of numerous participant quotes are strengths. The authors also state explicit limitations in Section 6.4, acknowledge that the quantitative measures in Section 5.7 are only descriptive, and frame the work as exploratory theory building. However, the central claim is stronger than the evidence: much of the reported 'new burden' evidence comes from hypothetical scenario responses rather than observed interaction effects, and the small, self-selected, high-digital-literacy sample limits generalizability. These issues make the framework plausible and interesting but not yet fully supported as stated.

major comments (4)
  1. [Section 4.4 and Section 5.6.1] The evidence for several newly introduced costs is based on anticipated or hypothetical behavior rather than observed behavior during the SNAP-LLM interaction. For example, Section 5.6.1 states that n=5 'readily accepted the generated information without additional verification,' but in a one-hour controlled Zoom session there was no real-world verification opportunity; the data can support at most an expressed willingness not to verify. Steps 5 and 6 of the interview asked participants to write scenarios and state preferences, which elicits projections about hypothetical use. The Discussion (Section 6) and Conclusion (Section 7) nonetheless state that these systems 'generate' new costs. This conflates anticipated burdens with experienced burdens and is load-bearing for the central 'reshaping' claim. I recommend explicitly coding and reporting which findings reflect actual interaction versus projected use, and revising the central claim to refer to 'reported potential for' or 'anticipated' new costs unless additional evidence is supplied.
  2. [Section 4 and Section 6.4] The claim in Section 4 that a sample of 10 is 'sufficient for us to achieve analytic generalization, reader generalization, and saturation' is not substantiated. Participants were self-selected Reddit users with at least one SNAP rejection and a mean self-reported digital literacy of 4 out of 5, so the sample is unlikely to represent the full spectrum of the SNAP applicant population. Section 6.4 acknowledges some of these limitations, but the abstract and Discussion still present the findings as a general framework. I recommend reframing the contribution as an exploratory, hypothesis-generating model and either removing the saturation assertion or supporting it with a theme-by-participant matrix and a more precise definition of what saturation means in this context.
  3. [Section 4.5] The coding process pivoted after initial coding revealed trust as a recurring theme, and the codes were then reorganized around established AI trust dimensions. Because the findings are presented through those same dimensions, there is a risk of interpretive circularity: the categories used to organize results were derived from, and then applied back to, the same data. Please describe what steps were taken to guard against forcing data into the trust framework, such as negative-case analysis, explicit comparison with alternative organizing schemes, or independent coding with discussion of disagreements. This is important because the paper's framework rests on the mapping between trust dimensions and burden types.
  4. [Section 4.1 and Section 5.7] The SNAP-LLM prototype was piloted with two domain experts but was not formally evaluated for output accuracy against the policy manual. Participants' trust judgments and reported burdens were formed in response to the prototype's actual answers, so any inaccuracies or hallucinated policy statements could directly influence the reported trust-burden dynamics. Section 6.4 acknowledges this as a limitation, but the body of the paper, especially Section 5.7 where quantitative trust ratings are reported, should state more clearly that the prototype's accuracy was not independently verified and that the ratings therefore characterize perceived rather than validated system performance.
minor comments (7)
  1. [Abstract and Section 1] The phrase '41 million federally determined low-income applicants' is unclear; 'federally determined' appears to mean income-eligible under federal rules, but the wording should be revised for precision.
  2. [Section 4.4] The interview structure includes scenario-writing and preference questions after the live interaction; please clarify in the protocol description whether the interviewer explicitly distinguished 'what you experienced with SNAP-LLM' from 'what you imagine would happen in a real situation,' since this distinction is central to interpreting the findings.
  3. [Section 5.7] Please state whether the post-interaction survey items were self-administered or read aloud by the interviewer, and note again in this section that the quantitative results are descriptive only, as the Methods section states.
  4. [Table 1] Table 1 is dense and not all rows are explicitly discussed in the text; consider adding a pointer in Section 6 that tells readers which rows correspond to which subsections of Section 5, and consider tightening the row labels so they are self-contained.
  5. [References] Some reference formatting is nonstandard, for example reference [65] lists 'the U.S. Digital Service, the Centers on Medicare, and Medicaid Services' as the author with a duplicated 'the'; please check the reference list against the venue's citation style.
  6. [Section 5.4.3 and Section 5.6.1] There is a typographical oddity in 'un( )intended' in Section 5.4.3, and the same P10 quote about punitive AI appears in both Section 5.4.1 and Section 5.6.1; consider consolidating repeated quotes to avoid redundancy.
  7. [Section 4.5] The paper does not describe how disagreements between the two coders were resolved; if the team used consensus discussion or a specific reflexive thematic analysis approach, please state this explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are grounded in interview data and external theory, not derived from its own inputs.

full rationale

This is a qualitative interview study, not a derivation-based paper. There are no fitted parameters, equations, or quantitative predictions whose values are forced by construction. The central framework combines two established external theories: administrative burden costs (learning, psychological, compliance) from Herd and Moynihan, and the three-dimensional trust model (competence, integrity, benevolence) from Mayer. The paper's findings are supported by participant quotes and a transparently described thematic analysis. The coding process did pivot to organize LLM-related codes around trust dimensions after trust emerged as a recurring theme (Section 4.5), but this is standard iterative qualitative analysis, not a case of defining an output in terms of an input; the reported sub-costs are evidenced with participant statements. The self-citations (e.g., [20], [36], [69]) are background references and do not carry the paper's load-bearing argument. The Discussion's claim that LLM systems 'generate new psychological, learning, and compliance costs' is an interpretive generalization from interview responses, many of which were hypothetical or anticipated; this is an internal-validity limitation that the paper itself acknowledges (Section 6.4), but it is not circularity. No step reduces to its own inputs by definition, by fitted-value construction, or by an unverified self-citation chain. Therefore the appropriate circularity score is 0.

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

The paper contributes no free parameters or invented entities. Its central claims rest on importing the administrative burden framework and Mayer's trust dimensions as domain assumptions, on treating the prototype's outputs as sufficiently accurate despite no formal evaluation, and on treating 10 Reddit-recruited participants as adequate for analytic generalization. These assumptions are partly acknowledged in Section 6.4.

assumptions (4)
  • domain assumption The Mayer et al. framework of competence, integrity, and benevolence applies to AI systems and to LLM assistants specifically.
    Invoked in Section 3.3 and used to organize all findings in Section 5; no evidence is presented that these dimensions are exhaustive or uniquely appropriate for LLM systems.
  • domain assumption Administrative burden categories (learning, psychological, compliance) extend to AI-mediated interactions and can be assessed through participant self-report.
    Sections 3.1 and 4.5 rely on Herd and Moynihan's framework as a lens rather than deriving it from the data.
  • domain assumption SNAP-LLM's responses based on the Indiana policy manual are accurate enough that participant perceptions of the system are meaningful.
    Sections 4.1 and 4.2 describe expert feedback but no formal accuracy evaluation; Section 6.4 states that formal evaluation is a future step.
  • ad hoc to paper A sample of 10 self-selected Reddit users supports analytic generalization and saturation.
    Section 4.3 asserts saturation and generalization citing Polit and Beck, but the authors themselves list sample limitations in Section 6.4.

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Cite this review

Pith. "Pith review of AI Trust Reshaping Administrative Burdens: Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems." pith.science (2026). https://pith.science/paper/OLXGOTJS

@misc{pith2026250522418,
  author       = {Pith},
  title        = {Pith review of: AI Trust Reshaping Administrative Burdens: Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OLXGOTJS}},
  note         = {Machine review of arXiv:2505.22418}
}
read the original abstract

Supplemental Nutrition Assistance Program (SNAP) is an essential benefit support system provided by the US administration to 41 million federally determined low-income applicants. Through interviews with such applicants across a diverse set of experiences with the SNAP system, our findings reveal that new AI technologies like LLMs can alleviate traditional burdens but also introduce new burdens. We introduce new types of learning, compliance, and psychological costs that transform the administrative burden on applicants. We also identify how trust in AI across three dimensions--competence, integrity, and benevolence--is perceived to reduce administrative burdens, which may stem from unintended and untoward overt trust in the system. We discuss calibrating appropriate levels of user trust in LLM-based administrative systems, mitigating newly introduced burdens. In particular, our findings suggest that evidence-based information disclosure is necessary in benefits administration and propose directions for future research on trust-burden dynamics in AI-assisted administration systems.

Figures

Figures reproduced from arXiv: 2505.22418 by the authors.

Figure 1
Figure 1. SNAP-LLM’s User Interface and Configuration Components. On the left side of the image is the interface. In the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Works this paper leans on

77 extracted references · 72 canonical work pages

  1. [1]

    Abubakar Abid, Maheen Farooqi, and James Zou. 2021. Persistent anti-muslim bias in large language models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 298–306

    work page 2021

  2. [2]

    Sarmad Alshawi and Hamid Alalwany. 2009. E-government evaluation: Citizen’s perspective in developing countries. Information Technology for Development 15, 3 (2009), 193–208

    work page 2009

  3. [3]

    John W Ayers, Adam Poliak, Mark Dredze, Eric C Leas, Zechariah Zhu, Jessica B Kelley, Dennis J Faix, Aaron M Goodman, Christopher A Longhurst, Michael Hogarth, et al. 2023. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA internal medicine 183, 6 (2023), 589–596

    2023

  4. [4]

    Martin Baekgaard, Kim Sass Mikkelsen, Jonas Krogh Madsen, and Julian Chris- tensen. 2021. Reducing compliance demands in government benefit programs improves the psychological well-being of target group members.Journal of Public Administration Research and Theory 31, 4 (2021), 806–821

    work page 2021

  5. [5]

    Bernard Barber. 1983. The logic and limits of trust . Rutgers University Press, New Brunswick, NJ

    work page 1983

  6. [6]

    Susan Bartlett, Nancy Burstein, William Hamilton, Ryan Kling, and M Andrews

    work page

  7. [7]

    Ahmed Belkhir and Fatiha Sadat. 2023. Beyond information: is ChatGPT em- pathetic enough?. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing . 159–169

    work page 2023

  8. [8]

    Hayley Bennett, Morgan Currie, and Lena Podoletz. 2024. Universal Credit: administrative burdens of automated welfare. Journal of Social Policy (2024), 1–19

    work page 2024

Show all 77 references
  1. [9]

    Glen Berman, Nitesh Goyal, and Michael Madaio. 2024. A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evalu- ations. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–24

    2024

  2. [10]

    Jayson G Boubin, Christina F Rusnock, and Jason M Bindewald. 2017. Quantifying compliance and reliance trust behaviors to influence trust in human-automation teams. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 61. SAGE Publications Sage CA: L...

    2017

  3. [11]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/ 1478088706qp063oa

    2006

  4. [12]

    Eric Breit and Robert Salomon. 2015. Making the technological transition– citizens’ encounters with digital pension services. Social Policy & Administration 49, 3 (2015), 299–315

    2015

  5. [13]

    Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos. 2021. To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-computer Interaction 5, CSCW1 (2021), 1–21

    2021

  6. [14]

    Julian Christensen, Lene Aarøe, Martin Baekgaard, Pamela Herd, and Donald P Moynihan. 2020. Human capital and administrative burden: The role of cognitive resources in citizen-state interactions. Public Administration Review 80, 1 (2020), 127–136

    2020

  7. [15]

    Mariana Chudnovsky and Rik Peeters. 2021. The unequal distribution of admin- istrative burden: A framework and an illustrative case study for understanding variation in people’s experience of burdens. Social Policy & Administration 55, 4 (2021), 527–542

    2021

  8. [16]

    Ziqing Dai. 2024. Applications and Challenges of Large Language Models in Smart Government-From technological Advances to Regulated Applications. In Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning . 275–280

    2024

  9. [17]

    Pierre-Marc Daigneault. 2024. Reconceptualizing administrative burden around onerous experiences. Perspectives on Public Management and Governance 7, 4 (2024), 124–136

    2024

  10. [18]

    Not my Priority:

    Hana Darling-Wolf and Elizabeth Patitsas. 2024. " Not my Priority:" Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education. Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024), 1–28

    2024

  11. [19]

    Amy Finkelstein and Matthew J Notowidigdo. 2019. Take-up and targeting: Experimental evidence from SNAP. The Quarterly Journal of Economics 134, 3 (2019), 1505–1556

    2019

  12. [20]

    Nitesh Goyal, Minsuk Chang, and Michael Terry. 2024. Designing for Human- Agent Alignment: Understanding what humans want from their agents. In Ex- tended Abstracts of the CHI Conference on Human Factors in Computing Systems . 1–6

    2024

  13. [21]

    Nitesh Goyal and Susan R Fussell. 2016. Effects of sensemaking translucence on distributed collaborative analysis. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing . 288–302

    2016

  14. [22]

    Nitesh Goyal, Ian D Kivlichan, Rachel Rosen, and Lucy Vasserman. 2022. Is your toxicity my toxicity? exploring the impact of rater identity on toxicity annotation. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–28

    2022

  15. [23]

    Nitesh Goyal, Leslie Park, and Lucy Vasserman. 2022. ” You have to prove the threat is real”: Understanding the needs of Female Journalists and Activists to Document and Report Online Harassment. In Proceedings of the 2022 CHI conference on human factors in computing systems . 1–17

    2022

  16. [24]

    Jerald Greenberg. 1987. A taxonomy of organizational justice theories. Academy of Management review 12, 1 (1987), 9–22

    1987

  17. [25]

    Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller. 2018. Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32

    2018

  18. [26]

    Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, and Kenneth Holstein. 2023. Ground (less) truth: A causal framework for proxy labels in human-algorithm decision-making. In Proceedings of the 2023 ACM Conference on Fairness, Account- ability, and Transparency. 688–704

    2023

  19. [27]

    Hanratty

    Maria J. Hanratty. 2006. Has the Food Stamp Program Become More Accessible? Impacts of Recent Changes in Reporting Requirements and Asset Eligibility Limits. Journal of Policy Analysis and Management 25, 3 (Summer 2006), 603–621

    2006

  20. [28]

    Hans-Tore Hansen, Kjetil Lundberg, and Liv Johanne Syltevik. 2018. Digitaliza- tion, street-level bureaucracy and welfare users’ experiences. Social policy & administration 52, 1 (2018), 67–90. Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems FAccT ’25, Jun...

    2018

  21. [29]

    Carolyn J Heinrich. 2016. The bite of administrative burden: A theoretical and empirical investigation. Journal of Public Administration Research and Theory 26, 3 (2016), 403–420

    2016

  22. [30]

    Carolyn J Heinrich and Robert Brill. 2015. Stopped in the name of the law: Ad- ministrative burden and its implications for cash transfer program effectiveness. World Development 72 (2015), 277–295

    2015

  23. [31]

    Pamela Herd, Thomas DeLeire, Hope Harvey, and Donald P Moynihan. 2013. Shifting administrative burden to the state: The case of medicaid take-up. Public Administration Review 73, s1 (2013), S69–S81

    2013

  24. [32]

    2019.Administrative burden: Policymaking by other means

    Pamela Herd and Donald P Moynihan. 2019.Administrative burden: Policymaking by other means. Russell Sage Foundation

    2019

  25. [33]

    V Hernanz. 2004. Take-Up of Welfare Benefits in OECD Countries: A Review of the Evidence. (2004)

    2004

  26. [34]

    Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021. 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. 624–635

    2021

  27. [35]

    Sebastian Jilke, Martin Bækgaard, Pamela Herd, and Donald Moynihan. 2023. Short and sweet: Measuring experiences of administrative burden. Journal of Behavioral Public Administration (2023)

    2023

  28. [36]

    Jeongwon Jo, Jingyi Xie, and John M Carroll. 2023. Food with Dignity: Public Values in the Supplemental Nutrition Assistance Program Mobile Applications. In Proceedings of the 2023 ACM Conference on Information Technology for Social Good. 246–256

    2023

  29. [37]

    Nader S Kabbani and Parke E Wilde. 2003. Short recertification periods in the US Food Stamp Program. Journal of Human Resources (2003), 1112–1138

    2003

  30. [38]

    Naveena Karusala, Sohini Upadhyay, Rajesh Veeraraghavan, and Krzysztof Z Gajos. 2024. Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services. In Proceedings of the CHI Conference on Human Factors in Computing ...

    2024

  31. [39]

    Dae-Eun Kim, Healyim Lee, Sue-kyeong Lee, and Seok-Jin Eom. 2024. Conditions for AI systems adoption in public sector: From an accountability perspective. In Proceedings of the 25th Annual International Conference on Digital Government Research. 42–51

    2024

  32. [40]

    I’m Not Sure, But

    Sunnie SY Kim, Q Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, and Jen- nifer Wortman Vaughan. 2024. "I’m Not Sure, But... ": Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust. In The 2024 ACM Conference on Fairness, Accountab...

    2024

  33. [41]

    Sunnie SY Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, and Andrés Monroy-Hernández. 2023. Humans, ai, and context: Understanding end- users’ trust in a real-world computer vision application. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, a...

    2023

  34. [42]

    Walter Korpi and Joakim Palme. 1998. The paradox of redistribution and strategies of equality: Welfare state institutions, inequality, and poverty in the Western countries. American sociological review (1998), 661–687

    1998

  35. [43]

    Linnea Laestadius, Andrea Bishop, Michael Gonzalez, Diana Illenčík, and Celeste Campos-Castillo. 2024. 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 (2024), ...

    2024

  36. [44]

    Jessica Lasky-Fink and Elizabeth Linos. 2024. Improving delivery of the social safety net: The role of stigma. Journal of Public Administration Research and Theory 34, 2 (2024), 270–283

    2024

  37. [45]

    Q Vera Liao and S Shyam Sundar. 2022. Designing for responsible trust in AI systems: A communication perspective. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 1257–1268

    2022

  38. [46]

    Ida Lindgren and Christian Østergaard Madsen. 2022. Understanding citizen actions in public encounters: towards a multi-channel process model. In DG. O 2022: the 23rd Annual International Conference on Digital Government Research . 364–371

    2022

  39. [47]

    Lorna Lines, Oluchi Ikechi, and Kate S Hone. 2007. Accessing e-Government services: design requirements for the older user. In Universal Access in Human- Computer Interaction. Applications and Services: 4th International Conference on Universal Access in Human-Computer Interac...

    2007

  40. [48]

    Moira Maguire and Brid Delahunt. 2017. Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars.All Ireland journal of higher education 9, 3 (2017). https://doi.org/10.62707/aishej.v9i3.335

    2017 doi

  41. [49]

    Arianna Manzini, Geoff Keeling, Nahema Marchal, Kevin R McKee, Verena Rieser, and Iason Gabriel. 2024. Should Users Trust Advanced AI Assistants? Justified Trust As a Function of Competence and Alignment. In The 2024 ACM Conference on Fairness, Accountability, and Transparency...

    2024

  42. [50]

    RC Mayer. 1995. An Integrative Model of Organizational Trust. Academy of Management Review (1995)

    1995

  43. [51]

    D Harrison Mcknight, Michelle Carter, Jason Bennett Thatcher, and Paul F Clay

  44. [52]

    D Harrison McKnight, Vivek Choudhury, and Charles Kacmar. 2002. Devel- oping and validating trust measures for e-commerce: An integrative typology. Information systems research 13, 3 (2002), 334–359

    2002

  45. [53]

    Frederick B Mills. 1996. The ideology of welfare reform: Deconstructing stigma. Social Work 41, 4 (1996), 391–395

    1996

  46. [54]

    Donald Moynihan, Pamela Herd, and Hope Harvey. 2015. Administrative bur- den: Learning, psychological, and compliance costs in citizen-state interactions. Journal of Public Administration Research and Theory 25, 1 (2015), 43–69

    2015

  47. [55]

    Muhammad Naeem, Wilson Ozuem, Kerry Howell, and Silvia Ranfagni

  48. [56]

    Wan Ng. 2012. Can we teach digital natives digital literacy? Computers & education 59, 3 (2012), 1065–1078

    2012

  49. [57]

    Annette M O’Connor, Guy Tsafnat, James Thomas, Paul Glasziou, Stephen B Gilbert, and Brian Hutton. 2019. A question of trust: can we build an evidence base to gain trust in systematic review automation technologies? Systematic reviews 8 (2019), 1–8

    2019

  50. [58]

    Denise F Polit and Cheryl Tatano Beck. 2010. Generalization in quantitative and qualitative research: Myths and strategies. International journal of nursing studies 47, 11 (2010), 1451–1458

    2010

  51. [59]

    Caroline Ratcliffe, Signe-Mary McKernan, and Kenneth Finegold. 2008. Effects of food stamp and TANF policies on food stamp receipt. Social Service Review 82, 2 (2008), 291–334

    2008

  52. [60]

    Devansh Saxena and Shion Guha. 2024. Algorithmic harms in child welfare: Uncertainties in practice, organization, and street-level decision-making. ACM Journal on Responsible Computing 1, 1 (2024), 1–32

    2024

  53. [61]

    Daniel S Schiff, Kaylyn Jackson Schiff, and Patrick Pierson. 2022. Assessing public value failure in government adoption of artificial intelligence. Public Administration 100, 3 (2022), 653–673

    2022

  54. [62]

    Jannick Schou and Anja Svejgaard Pors. 2019. Digital by default? A qualitative study of exclusion in digitalised welfare. Social policy & administration 53, 3 (2019), 464–477

    2019

  55. [63]

    Søren Skaarup. 2020. The role of domain-skills in bureaucratic service encounters. In Electronic Government: 19th IFIP WG 8.5 International Conference, EGOV 2020, Linköping, Sweden, August 31–September 2, 2020, Proceedings 19 . Springer, 179– 196

    2020

  56. [64]

    Jennifer Sykes, Katrin Križ, Kathryn Edin, and Sarah Halpern-Meekin. 2015. Dignity and dreams: What the Earned Income Tax Credit (EITC) means to low- income families. American Sociological Review 80, 2 (2015), 243–267

    2015

  57. [65]

    Digital Service, the Centers on Medicare, and Medicaid Services

    the U.S. Digital Service, the Centers on Medicare, and Medicaid Services. 2016. Mapping the Applicant Experience of Benefit Enrollment . Retrieved Accessed on September 30, 2024 from https://usds.github.io/benefits-enrollment-prototype/ assets/discovery-findings-mapping-enroll...

    2016

  58. [66]

    Scott Thiebes, Sebastian Lins, and Ali Sunyaev. 2021. Trustworthy artificial intelligence. Electronic Markets 31 (2021), 447–464

    2021

  59. [67]

    David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation 27, 2 (2006), 237–246. https: //doi.org/10.1177/1098214005283748

    2006 doi

  60. [68]

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

    2020

  61. [69]

    It is currently hodgepodge

    Rama Adithya Varanasi and Nitesh Goyal. 2023. “It is currently hodgepodge”: Examining AI/ML Practitioners’ Challenges during Co-production of Responsible AI Values. InProceedings of the 2023 CHI conference on human factors in computing systems. 1–17

    2023

  62. [70]

    Thomas M Vogl, Cathrine Seidelin, Bharath Ganesh, and Jonathan Bright. 2020. Smart technology and the emergence of algorithmic bureaucracy: Artificial in- telligence in UK local authorities. Public Administration Review 80, 6 (2020), 946–961

    2020

  63. [71]

    Ruotong Wang, Ruijia Cheng, Denae Ford, and Thomas Zimmermann. 2024. Investigating and designing for trust in AI-powered code generation tools. InPro- ceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. 1475–1493

    2024

  64. [72]

    Mireia Yurrita, Tim Draws, Agathe Balayn, Dave Murray-Rust, Nava Tintarev, and Alessandro Bozzon. 2023. Disentangling fairness perceptions in algorithmic decision-making: the effects of explanations, human oversight, and contestability. In Proceedings of the 2023 CHI Conferenc...

    2023

  65. [73]

    He Zhang, Xinyang Li, Xinyi Fu, Christine Qiu, Jiyuan Zhang, and John M. Carroll

  66. [2004]

    Economic Research Service (2004)

    Food Stamp Program access study-final report. Economic Research Service (2004)

    2004

  67. [2011]

    ACM Transactions on management information systems (TMIS) 2, 2 (2011), 1–25

    Trust in a specific technology: An investigation of its components and measures. ACM Transactions on management information systems (TMIS) 2, 2 (2011), 1–25

    2011

  68. [2023]

    International Journal of Qualitative Meth- ods 22 (2023), 16094069231205789

    A Step-by-Step Process of Thematic Analysis to Develop a Concep- tual Model in Qualitative Research. International Journal of Qualitative Meth- ods 22 (2023), 16094069231205789. https://doi.org/10.1177/16094069231205789 arXiv:https://doi.org/10.1177/16094069231205789

    2023 doi

  69. [2024]

    IEEE Transactions on Games (2024), 1–14

    Understanding Fear Responses and Coping Mechanisms in VR Horror Gaming: Insights From Semi-Structured Interviews. IEEE Transactions on Games (2024), 1–14. https://doi.org/10.1109/TG.2024.3403768 FAccT ’25, June 23–26, 2025, Athens, Greece Jo et al. A SNAP-LLM Prompt Purpose an...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.