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arxiv: 2510.03559 · v2 · pith:Y4VDIMW3new · submitted 2025-10-03 · 💻 cs.CR · cs.HC

PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design

Zeya Chen , Jianing Wen , Yaxing Yao , Toby Jia-Jun Li , Tianshi Li This is my paper

Pith reviewed 2026-05-22 13:25 UTC · model grok-4.3

classification 💻 cs.CR cs.HC
keywords privacyUX designdesign reviewspersonasLLMempathymotivationuser experience
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The pith

PrivacyMotiv uses LLM-generated persona journeys to increase empathy and help UX designers identify 59 percent more privacy issues.

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

The paper tries to show that low motivation keeps UX professionals from catching privacy problems early, because they often lack specific knowledge, feel little connection to affected users, and have no clear starting point for spotting harms. PrivacyMotiv addresses this by turning simple user flow sketches into vulnerability-centered personas, their story journeys, and linked design diagnoses. In a study with sixteen professional UX practitioners, the system raised empathy and intrinsic motivation while producing 59 percent more privacy issues spotted and 70 percent more redesign proposals than when the same designers worked without it. A reader would care because privacy oversights in everyday apps can expose real people to data risks, and this offers one concrete way to fold privacy thinking into normal early design work.

Core claim

PrivacyMotiv is an LLM-powered system that generates vulnerability-centered personas, persona journey stories, and traceable design diagnoses grounded in lo-fi user flows to support privacy-oriented UX design review. In a within-subjects study with professional UX practitioners (N=16), PrivacyMotiv significantly improved empathy, intrinsic motivation, and perceived usefulness, with participants identifying 59% more privacy issues and proposing 70% more redesign solutions compared to self-proposed methods. This work contributes empirical insight into motivational barriers in privacy-aware UX and a structured, narrative-driven approach for integrating privacy review into early-stage UX.

What carries the argument

PrivacyMotiv, an LLM-powered generator of vulnerability-centered personas and persona journey stories grounded in lo-fi user flows, which supplies narrative structure to build empathy and motivation for privacy reviews.

Load-bearing premise

The measured gains in issue detection and motivation come specifically from the generated personas and journeys rather than from any structured review process or the general use of an AI tool.

What would settle it

A follow-up within-subjects study that replaces the persona journeys with generic structured prompts and checks whether the 59 percent and 70 percent gains in issues and solutions disappear.

Figures

Figures reproduced from arXiv: 2510.03559 by Jianing Wen, Tianshi Li, Toby Jia-Jun Li, Yaxing Yao, Zeya Chen.

Figure 1
Figure 1. Figure 1: PrivacyMotiv System Overview. This figure illustrates the end-to-end workflow of the PrivacyMotiv system for uncovering privacy harms through vulnerability-centered personas and speculative user journeys. Step 1: Persona Selection. Users begin by selecting from a set of vulnerability-centered personas. Each persona includes demographic context, privacy tensions, and privacy responses. Step 2: Narrative Jou… view at source ↗
Figure 2
Figure 2. Figure 2: The generation pipeline of PrivacyMotiv. Prompt1 is a prompt (Appendix F.1) used to generate initial structured personas (Appendix F.1). The results are further enriched by supporting literature, resulting in Output1: expanded personas with privacy tensions, responses, and costs. Combined with user flows and the Privacy Harms Typology reference, these form Prompt2 (Appendix F.2) for generating speculative … view at source ↗
Figure 3
Figure 3. Figure 3: Three formats of user flows (conceptually equivalent). [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Persona journey story page of Eva 3.4.2 Annotated Lo-fi User Flows for Linking Privacy Harms With Design. A core mechanism of our system is the visual presentation of privacy harms through annotated user flow storyboards. Each user flow is rendered as a low-fidelity UI sequence with step-by-step annotations, enabling designers to trace specific interaction points where privacy violations occur (D3). These … view at source ↗
Figure 5
Figure 5. Figure 5: Design Flaws Triggering Privacy Harms in Eva’s Case. This annotated user flow, presented in a low-fidelity wireframe format, illustrates how specific interface designs and default behaviors in the “Private Session” feature contributed to privacy harms for Eva. Annotations highlight problematic design decisions through color-coded callouts that identify specific interface flaws and their privacy implication… view at source ↗
Figure 6
Figure 6. Figure 6: Comparisons of Empathy, Motivation, Usefulness between the designer’s self-proposed method (baseline) and PrivacyMotiv. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparisons of the subscales of Empathy and Motivation between the designer’s self-proposed method (baseline) and [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Specificity of problems and suggestions across Garrett’s five planes (L1-L5). The PrivacyMotiv condition yielded more concrete [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Thematic distribution of identified problems across Privacy by Design (PbD) principles. PrivacyMotiv expanded coverage to [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: An example of a WeMusic’s function flow shared with participants [PITH_FULL_IMAGE:figures/full_fig_p032_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Persona Generation Prompt [PITH_FULL_IMAGE:figures/full_fig_p038_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Privacy-Invasive User Journey Narrative Generation Prompt for the WeMusic App [PITH_FULL_IMAGE:figures/full_fig_p040_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Privacy-Invasive User Journey Narrative Generation Prompt for the NeighborNet App [PITH_FULL_IMAGE:figures/full_fig_p042_13.png] view at source ↗
read the original abstract

UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked, not only due to limited tools, but more fundamentally from low intrinsic motivation, driven by limited privacy knowledge, weak empathy for unexpectedly affected users, and low autonomy in identifying harms. We present PrivacyMotiv, an LLM-powered system that generates vulnerability-centered personas, persona journey stories, and traceable design diagnoses grounded in lo-fi user flows to support privacy-oriented UX design review. In a within-subjects study with professional UX practitioners (N=16), PrivacyMotiv significantly improved empathy, intrinsic motivation, and perceived usefulness, with participants identifying 59% more privacy issues and proposing 70% more redesign solutions compared to self-proposed methods. This work contributes empirical insight into motivational barriers in privacy-aware UX and a structured, narrative-driven approach for integrating privacy review into early-stage UX practice.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces PrivacyMotiv, an LLM-powered tool that generates vulnerability-centered personas, persona journey stories, and traceable design diagnoses from lo-fi user flows to help UX practitioners conduct privacy-oriented design reviews. It reports results from a within-subjects study (N=16 professional UX practitioners) claiming statistically significant gains in empathy, intrinsic motivation, and perceived usefulness, plus 59% more privacy issues identified and 70% more redesign solutions proposed relative to participants' self-proposed methods.

Significance. If the empirical claims hold after addressing controls, the work would offer a concrete, narrative-driven method for lowering motivational barriers to privacy review in early UX practice. The contribution lies in the empirical demonstration with practitioners and the structured use of speculative personas rather than generic checklists; reproducible study materials or code would strengthen this.

major comments (1)
  1. [§5] §5 (User Study / Evaluation): The within-subjects protocol pits PrivacyMotiv against an unstructured 'self-proposed methods' baseline. This design does not isolate the effect of the LLM-generated vulnerability-centered personas and traceable journeys from the general presence of any structured scaffold or the novelty of an interactive AI interface. Consequently, the reported 59% and 70% lifts cannot be unambiguously attributed to the specific mechanism claimed in the abstract and strongest_claim; a matched control arm (e.g., static privacy checklist or non-narrative prompt) is required to support the causal interpretation.
minor comments (2)
  1. [Abstract] Abstract and §5: The claims of 'significantly improved' outcomes and the exact percentage lifts are presented without mention of statistical tests, effect sizes, order-effect controls, or inter-rater reliability for issue coding; these details must be added to allow readers to assess the quantitative results.
  2. [§4] §4 (System Description): The precise prompting strategy and grounding mechanism for generating 'traceable design diagnoses' from lo-fi flows should be illustrated with a concrete example to clarify how the output remains faithful to the input artifacts.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address the single major comment on the user study design below, acknowledging its validity while explaining our rationale and planned revisions.

read point-by-point responses

  1. Referee: [§5] §5 (User Study / Evaluation): The within-subjects protocol pits PrivacyMotiv against an unstructured 'self-proposed methods' baseline. This design does not isolate the effect of the LLM-generated vulnerability-centered personas and traceable journeys from the general presence of any structured scaffold or the novelty of an interactive AI interface. Consequently, the reported 59% and 70% lifts cannot be unambiguously attributed to the specific mechanism claimed in the abstract and strongest_claim; a matched control arm (e.g., static privacy checklist or non-narrative prompt) is required to support the causal interpretation.

    Authors: We thank the referee for this observation. The within-subjects baseline of self-proposed methods was deliberately chosen to reflect authentic UX practice, where privacy reviews are typically performed without dedicated tools or external structure. This comparison demonstrates the practical gains PrivacyMotiv can deliver in real workflows. We agree, however, that the design does not isolate the contribution of the vulnerability-centered personas and traceable journeys from the effects of introducing any scaffold or an interactive AI interface. In the revised manuscript we will expand the Limitations section with an explicit discussion of this potential confound and will recommend future controlled experiments that add arms such as a static privacy checklist or a non-narrative prompt. We cannot conduct a new study for this revision but believe the added discussion will allow readers to interpret the reported improvements with appropriate caution. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical user study with independent measures

full rationale

The paper introduces an LLM-powered system for generating vulnerability-centered personas and journey stories to support privacy reviews in UX design, then reports results from a within-subjects empirical study (N=16 UX practitioners) measuring empathy, intrinsic motivation, issue identification, and redesign proposals. No mathematical derivations, equations, predictions, or first-principles results exist that could reduce to inputs by construction. Outcome measures and study protocol are defined separately from any cited prior work. No self-citation chains, uniqueness theorems, or ansatz smuggling support the central claims; the work remains self-contained against the reported empirical benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that LLM outputs can produce sufficiently realistic and unbiased privacy-vulnerability personas and that the within-subjects comparison fairly isolates the tool's contribution; no free parameters or new physical entities are introduced.

axioms (1)
  • domain assumption Large language models can generate useful, vulnerability-centered personas and journey stories from lo-fi user flows without introducing systematic biases that would invalidate designer empathy gains.
    Invoked when the system is described as producing the core artifacts that drive the measured improvements.

pith-pipeline@v0.9.0 · 5691 in / 1415 out tokens · 39732 ms · 2026-05-22T13:25:39.927856+00:00 · methodology

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Reference graph

Works this paper leans on

94 extracted references · 94 canonical work pages · 1 internal anchor

  1. [1]

    Ammar Ahmed and Ali Shariq Imran. 2025. The role of large language models in UI/UX design: A systematic literature review.arXiv preprint arXiv:2507.04469(2025)

    work page arXiv 2025

  2. [2]

    Abdulrahman Alhazmi and Nalin Asanka Gamagedara Arachchilage. 2021. I’m all ears! Listening to software developers on putting GDPR principles into software development practice.Personal and Ubiquitous Computing25, 5 (2021), 879–892

    work page 2021

  3. [3]

    Heba Aly, Yizhou Liu, Reza Ghaiumy Anaraky, Sushmita Khan, Moses Namara, Kaileigh Angela Byrne, and Bart Knijnenburg. 2024. Tailoring digital privacy education interventions for older adults: A comparative study on modality preferences and effectiveness.Proceedings on Privacy Enhancing Technologies(2024)

    work page 2024

  4. [4]

    Arjun Arunasalam, Habiba Farrukh, Eliz Tekcan, and Z Berkay Celik. 2024. Understanding the security and privacy implications of online toxic content on refugees. In33rd USENIX Security Symposium (USENIX Security 24). 4373–4390

    work page 2024

  5. [5]

    Reka Borka Balas, Lorena Elena Melit,, Daniel Sarkozi, Dana Valentina Ghiga, and Cristina Oana Mărginean. 2023. Cyberbullying in teenagers–a true burden in the era of online socialization.Medicine102, 25 (2023), e34051

    work page 2023

  6. [6]

    John Brooke et al. 1996. SUS-A quick and dirty usability scale.Usability evaluation in industry189, 194 (1996), 4–7

    work page 1996

  7. [7]

    Marion Buchenau and Jane Fulton Suri. 2000. Experience prototyping. InProceedings of the 3rd Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques (DIS ’00). Association for Computing Machinery, New York, NY, USA, 424–433. doi:10.1145/347642.347802

    work page doi:10.1145/347642.347802 2000

  8. [8]

    Christoph Bösch, Benjamin Erb, Frank Kargl, Henning Kopp, and Stefan Pfattheicher. 2016. Tales from the dark side: privacy dark strategies and privacy dark patterns.Proceedings on Privacy Enhancing Technologies(2016). https://petsymposium.org/popets/2016/popets-2016-0038.php

    work page 2016

  9. [9]

    Ann Cavoukian et al. 2009. Privacy by design: The 7 foundational principles.Information and privacy commissioner of Ontario, Canada5, 2009 (2009), 12

    work page 2009

  10. [10]

    Zeya Chen, Jianing Wen, Toby Jia-Jun Li, Yaxing Yao, and Tianshi Li. 2025. Speculating Unintended Creepiness: Exploring LLM-Powered Empathy Building for Privacy-Aware UX Design. InProceedings of the 2025 Workshop on Human-Centered Al Privacy and Security (HAIPS ’25). Association for Computing Machinery, New York, NY, USA. doi:10.1145/3733816.3760759

    work page doi:10.1145/3733816.3760759 2025

  11. [11]

    DaEun Choi, Sumin Hong, Jeongeon Park, John Joon Young Chung, and Juho Kim. 2024. CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, NY, USA, Article 10...

    work page doi:10.1145/3613904.3642794 2024

  12. [12]

    Yoonseo Choi, Eun Jeong Kang, Seulgi Choi, Min Kyung Lee, and Juho Kim. 2025. Proxona: supporting creators’ sensemaking and ideation with LLM-powered audience personas. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, 1–32. doi:10.1145/3706598.3714034

    work page doi:10.1145/3706598.3714034 2025

  13. [13]

    Danielle Keats Citron and Daniel J Solove. 2022. Privacy harms.BUL Rev.102 (2022), 793

    work page 2022

  14. [14]

    George Danezis, Josep Domingo-Ferrer, Marit Hansen, Jaap-Henk Hoepman, Daniel Le Metayer, Rodica Tirtea, and Stefan Schiffner. 2015. Privacy and data protection by design-from policy to engineering.arXiv preprint arXiv:1501.03726(2015). 28 Chen et al

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  15. [15]

    Sanchari Das. 2024. Design of secure, privacy-focused, and accessible e-payment applications for older adults. In2024 Conference on Building a Secure & Empowered Cyberspace (BuildSEC). IEEE, 74–78

    work page 2024

  16. [16]

    Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology.MIS quarterly(1989), 319–340

    work page 1989

  17. [17]

    Mark H Davis. 1983. Measuring individual differences in empathy: evidence for a multidimensional approach.Journal of personality and social psychology44, 1 (1983), 113

    work page 1983

  18. [18]

    Peitong Duan, Jeremy Warner, and Bjoern Hartmann. 2023. Towards generating UI design feedback with LLMs. InAdjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1–3

    work page 2023

  19. [19]

    KJ Kevin Feng, Q Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy X Zhang, and David W McDonald. 2025. Canvil: Designerly Adaptation for LLM-Powered User Experiences. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–22

    work page 2025

  20. [20]

    Firm_Alfalfa_6017. 2024. do you stalk your ex on spotify? do men do that? https://www.reddit.com/r/ExNoContact/comments/1g0ypeq/do_you_ stalk_your_ex_on_spotify_do_men_do_that/

    work page 2024

  21. [21]

    2018.Every step you take: How deceptive design lets Google track users 24/7

    ForbrukerrÅdet. 2018.Every step you take: How deceptive design lets Google track users 24/7. Tech. report. Norwegian Consumer Council (ForbrukerrÅdet), Oslo, Norway. https://www.forbrukerradet.no/rapporter/every-step-you-take/ Report on deceptive design and location tracking by Google

    work page 2018

  22. [22]

    Forbrukerrådet. 2018. Deceived by design — How tech companies use dark patterns to discourage us from exercising our rights to privacy. Recommended reading; Publication via ConPolicy (The Institute for Consumer Policy). https://www.conpolicy.de/en/news-detail/deceived-by- design-how-tech-companies-use-dark-patterns-to-discourage-us-from-exercising-our-rig...

    work page 2018

  23. [23]

    2022.The elements of user experience

    Jesse James Garrett. 2022.The elements of user experience. Peachpit Pr

    work page 2022

  24. [24]

    Aakash Gautam. 2020. Usable, acceptable, appropriable: Towards practicable privacy.arXiv preprint arXiv:2004.07359(2020)

    work page arXiv 2020

  25. [25]

    Colin M Gray, Yubo Kou, Bryan Battles, Joseph Hoggatt, and Austin L Toombs. 2018. The dark (patterns) side of UX design. InProceedings of the 2018 CHI conference on human factors in computing systems. 1–14

    work page 2018

  26. [26]

    Paul Graßl, Hanna Schraffenberger, Frederik Zuiderveen Borgesius, and Moniek Buijzen. 2021. Dark and bright patterns in cookie consent requests. Journal of Digital Social Research3, 1 (Feb. 2021), 1–38. doi:10.33621/jdsr.v3i1.54 Number: 1

    work page doi:10.33621/jdsr.v3i1.54 2021

  27. [27]

    Johanna Gunawan, Amogh Pradeep, David Choffnes, Woodrow Hartzog, and Christo Wilson. 2021. A comparative study of dark patterns across web and mobile modalities.Proc. ACM Hum.-comput. Interact.5, CSCW2 (Oct. 2021), 377:1–377:29. doi:10.1145/3479521

    work page doi:10.1145/3479521 2021

  28. [28]

    Johanna Gunawan, Cristiana Santos, and Irene Kamara. 2022. Redress for dark patterns privacy harms? A case study on consent interactions. In Proceedings of the 2022 Symposium on Computer Science and Law. ACM, Washington DC USA, 181–194. doi:10.1145/3511265.3550448

    work page doi:10.1145/3511265.3550448 2022

  29. [29]

    Morten Hertzum and Niels Ebbe Jacobsen. 2003. The Evaluator Effect: A Chilling Fact About Usability Evaluation Methods.International Journal of Human–Computer Interaction15, 2 (2003), 183–204. doi:10.1207/S15327590IJHC1501_14

    work page doi:10.1207/s15327590ijhc1501_14 2003

  30. [30]

    Jaap-Henk Hoepman. 2014. Privacy design strategies. InIFIP International Information Security Conference. Springer, 446–459

    work page 2014

  31. [31]

    Daniel Kahneman and Amos Tversky. 1984. Choices, values, and frames.American psychologist39, 4 (1984), 341

    work page 1984

  32. [32]

    Kate Kaplan. 2023. User Journeys vs. User Flows. https://www.nngroup.com/articles/user-journeys-vs-user-flows/

    work page 2023

  33. [33]

    Thora Knight, Xiaojun Yuan, and DeeDee M Bennett Gayle. 2023. Privacy and security concerns: A systematic review of older adults’ perceptions surrounding the use of technology.Journal of Data Protection & Privacy5, 4 (2023), 388–400

    work page 2023

  34. [34]

    Christopher Lazik, Christopher Katins, Charlotte Kauter, Jonas Jakob, Caroline Jay, Lars Grunske, and Thomas Kosch. 2025. The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?arXiv preprint arXiv:2501.04543(2025)

    work page arXiv 2025

  35. [35]

    I don’t know if we’re doing good. I don’t know if we’re doing bad

    Hao-Ping (Hank) Lee, Lan Gao, Stephanie Yang, Jodi Forlizzi, and Sauvik Das. 2024. "I don’t know if we’re doing good. I don’t know if we’re doing bad": investigating how practitioners scope, motivate, and conduct privacy work when developing {AI} products. 4873–4890. https: //www.usenix.org/conference/usenixsecurity24/presentation/lee

    work page 2024

  36. [36]

    2023.Privacy annotations: Designing privacy support for developers

    Tianshi Li. 2023.Privacy annotations: Designing privacy support for developers. Ph. D. Dissertation. University of Washington

    work page 2023

  37. [37]

    Tianshi Li, Yuvraj Agarwal, and Jason I. Hong. 2018. Coconut: An IDE Plugin for Developing Privacy-Friendly Apps.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.2, 4, Article 178 (Dec. 2018), 35 pages. doi:10.1145/3287056

    work page doi:10.1145/3287056 2018

  38. [38]

    Neundorfer, Yuvraj Agarwal, and Jason I

    Tianshi Li, Elijah B. Neundorfer, Yuvraj Agarwal, and Jason I. Hong. 2021. Honeysuckle: annotation-guided code generation of In-app privacy notices.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.5, 3 (Sept. 2021), 112:1–112:27. doi:10.1145/3478097

    work page doi:10.1145/3478097 2021

  39. [39]

    Agn˙e Limant˙e and Art¯uras Tereškinas. 2022. Definition of vulnerable groups. InLegal protection of vulnerable groups in Lithuania, Latvia, Estonia and Poland: Trends and perspectives. Springer, 3–27

    work page 2022

  40. [40]

    Yuwen Lu, Chao Zhang, Yuewen Yang, Yaxing Yao, and Toby Jia-Jun Li. 2024. From awareness to action: exploring end-user empowerment interventions for dark patterns in UX.Proc. ACM Hum.-comput. Interact.8, CSCW1 (April 2024), 59:1–59:41. doi:10.1145/3637336

    work page doi:10.1145/3637336 2024

  41. [41]

    Anna Luusua, Johanna Ylipulli, Marko Jurmu, Henrika Pihlajaniemi, Piia Markkanen, and Timo Ojala. 2015. Evaluation Probes. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(Seoul, Republic of Korea)(CHI ’15). Association for Computing Machinery, New York, NY, USA, 85–94. doi:10.1145/2702123.2702466

    work page doi:10.1145/2702123.2702466 2015

  42. [42]

    Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020. Co-designing checklists to understand organizational challenges and opportunities around fairness in AI. InProceedings of the 2020 CHI conference on human factors in computing systems. 1–14

    work page 2020

  43. [43]

    Nicola Marsden and Maren Haag. 2016. Stereotypes and politics: reflections on personas. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). Association for Computing Machinery, New York, NY, USA, 4017–4031. doi:10.1145/2858036.2858151 PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Revie...

    work page doi:10.1145/2858036.2858151 2016

  44. [44]

    Arunesh Mathur, Mihir Kshirsagar, and Jonathan Mayer. 2021. What makes a dark pattern... Dark? Design attributes, normative considerations, and measurement methods. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, 1–18. doi:10.1145/3411764.3445610

    work page doi:10.1145/3411764.3445610 2021

  45. [45]

    Célestin Matte, Nataliia Bielova, and Cristiana Santos. 2020. Do cookie banners respect my choice? : measuring legal compliance of banners from IAB europe’s transparency and consent framework. IEEE Computer Society, 791–809. doi:10.1109/SP40000.2020.00076

    work page doi:10.1109/sp40000.2020.00076 2020

  46. [46]

    Tara Matthews, Kathleen O’Leary, Anna Turner, Manya Sleeper, Jill Palzkill Woelfer, Martin Shelton, Cori Manthorne, Elizabeth F Churchill, and Sunny Consolvo. 2017. Stories from survivors: Privacy & security practices when coping with intimate partner abuse. InProceedings of the 2017 CHI conference on human factors in computing systems. 2189–2201

    work page 2017

  47. [47]

    Nora McDonald, Karla Badillo-Urquiola, Morgan G Ames, Nicola Dell, Elizabeth Keneski, Manya Sleeper, and Pamela J Wisniewski. 2020. Privacy and power: Acknowledging the importance of privacy research and design for vulnerable populations. InExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1–8

    work page 2020

  48. [48]

    Nora McDonald and Andrea Forte. 2022. Privacy and vulnerable populations. InModern socio-technical perspectives on privacy. Springer International Publishing Cham, 337–363

    work page 2022

  49. [49]

    Yihan Mei, Zhao Wu, Junnan Yu, Wenan Li, and Zhibin Zhou. 2025. GeneyMAP: exploring the potential of GenAI to facilitate mapping user journeys for UX design. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, 1–22. doi:10.1145/3706598.3713479

    work page doi:10.1145/3706598.3713479 2025

  50. [50]

    Doyle, Benjamin R

    Thomas Mildner, Gian-Luca Savino, Philip R. Doyle, Benjamin R. Cowan, and Rainer Malaka. 2023. About engaging and governing strategies: a thematic analysis of dark patterns in social networking services. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA, 1–15. d...

    work page doi:10.1145/3544548.3580695 2023

  51. [51]

    Justin M Mittelstädt, Julia Maier, Panja Goerke, Frank Zinn, and Michael Hermes. 2024. Large language models can outperform humans in social situational judgments.Scientific reports14, 1 (2024), 27449

    work page 2024

  52. [52]

    Liming Nie, Yangyang Zhao, Chenglin Li, Xuqiong Luo, and Yang Liu. 2024. Shadows in the interface: a comprehensive study on dark patterns. Proc. ACM Softw. Eng.1, FSE (July 2024), 10:204–10:225. doi:10.1145/3643736

    work page doi:10.1145/3643736 2024

  53. [53]

    Karger, and Lalana Kagal

    Midas Nouwens, Ilaria Liccardi, Michael Veale, David R. Karger, and Lalana Kagal. 2020. Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, Honolulu, HI, USA, 1–13. doi:10.1145/3313831.3376321

    work page doi:10.1145/3313831.3376321 2020

  54. [54]

    Wilma Numans, Tine Van Regenmortel, René Schalk, and Juliette Boog. 2021. Vulnerable persons in society: an insider’s perspective.International Journal of Qualitative Studies on Health and Well-being16, 1 (2021), 1863598

    work page 2021

  55. [55]

    Ibrahim Obaidat, Aseel Al-zou’bi, Ala Mughaid, and Laith Abualigah. 2023. Investigating the cyberbullying risk in digital media: protecting victims in school teenagers.Social Network Analysis and Mining13, 1 (2023), 139

    work page 2023

  56. [56]

    Sunyoung Oh and Sang-Choong Roh. 2022. Intrinsic motivation for work activities is associated with empathy: Investigating the indirect relationship between intrinsic motivation for work activities and social support through empathy and prosocial behavior.Personality and Individual Differences 189 (2022), 111487

    work page 2022

  57. [57]

    Srishti Palani, David Ledo, George Fitzmaurice, and Fraser Anderson. 2022. ” I don’t want to feel like I’m working in a 1960s factory”: The Practitioner Perspective on Creativity Support Tool Adoption. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–18

    work page 2022

  58. [58]

    2022.Spotify is developing ‘Community, ’ a new place to see your friends’ activity in the mobile app

    Sarah Perez. 2022.Spotify is developing ‘Community, ’ a new place to see your friends’ activity in the mobile app. https://techcrunch.com/2022/06/22/ spotify-is-developing-community-a-new-place-to-see-your-friends-activity-in-the-mobile-app/ TechCrunch. Accessed: 2025-08-18

    work page 2022

  59. [59]

    Maxwell Prybylo, Sara Haghighi, Sai Teja Peddinti, and Sepideh Ghanavati. 2024. Evaluating privacy perceptions, experience, and behavior of software development teams. InTwentieth Symposium on Usable Privacy and Security (SOUPS 2024). 101–120

    work page 2024

  60. [60]

    Aunam Quyoum, Mark Wong, Sebati Ghosh, and Siamak F Shahandashti. 2025. Minoritised Ethnic People’s Security and Privacy Concerns and Responses towards Essential Online Services.arXiv preprint arXiv:2506.06062(2025)

    work page arXiv 2025

  61. [61]

    Bogdana Rakova, Jingying Yang, Henriette Cramer, and Rumman Chowdhury. 2021. Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices.Proceedings of the ACM on Human-Computer Interaction5, CSCW1 (2021), 1–23

    work page 2021

  62. [62]

    2013.Social networks and cyber-bullying among teenagers

    Caroline Rizza and Ângela Guimarães Pereira. 2013.Social networks and cyber-bullying among teenagers. EUR-OP

    work page 2013

  63. [63]

    2014.Vulnerable Populations

    Deden Rukmana. 2014.Vulnerable Populations. Springer Netherlands, Dordrecht, 6989–6992. doi:10.1007/978-94-007-0753-5_3184

    work page doi:10.1007/978-94-007-0753-5_3184 2014

  64. [64]

    Richard M Ryan and Edward L Deci. 2000. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist55, 1 (2000), 68

    work page 2000

  65. [65]

    Richard M Ryan, Valerie Mims, and Richard Koestner. 1983. Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive evaluation theory.Journal of personality and Social Psychology45, 4 (1983), 736

    work page 1983

  66. [66]

    Shruti Sannon and Andrea Forte. 2022. Privacy research with marginalized groups: what we know, what’s needed, and what’s next.Proceedings of the ACM on Human-Computer Interaction6, CSCW2 (2022), 1–33

    work page 2022

  67. [67]

    Joongi Shin, Michael A Hedderich, Bartłomiej Jakub Rey, Andrés Lucero, and Antti Oulasvirta. 2024. Understanding human-AI workflows for generating personas. InProceedings of the 2024 ACM Designing Interactive Systems Conference. 757–781

    work page 2024

  68. [68]

    The Conduit by which Change Happens

    Jaemarie Solyst, Lauren Wilcox, and Michael Madaio. 2025. " The Conduit by which Change Happens": Processes, Barriers, and Support for Interpersonal Learning about Responsible AI. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–15

    work page 2025

  69. [69]

    2023.Friend Activity

    Spotify. 2023.Friend Activity. https://support.spotify.com/us/article/friend-activity/ Accessed: 2025-08-18. 30 Chen et al

    work page 2023

  70. [70]

    Spotify Community. 2015. Mobile Friend Feed: Friends Activity on Mobile. https://community.spotify.com/t5/Live-Ideas/Mobile-Friend-Feed- Friends-Activity-on-Mobile/idi-p/1153990. Accessed: 2025-08-18

    work page arXiv 2015

  71. [71]

    Ramya Srinivasan and Beatriz San Miguel González. 2022. The role of empathy for artificial intelligence accountability.Journal of Responsible Technology9 (April 2022), 100021. doi:10.1016/j.jrt.2021.100021

    work page doi:10.1016/j.jrt.2021.100021 2022

  72. [72]

    2011.This Is Service Design Thinking: Basics, Tools, Cases

    Marc Stickdorn and Jakob Schneider. 2011.This Is Service Design Thinking: Basics, Tools, Cases. BIS Publishers, Amsterdam, The Netherlands

    work page 2011

  73. [73]

    Mohammad Tahaei, Tianshi Li, and Kami Vaniea. 2022. Understanding privacy-related advice on stack overflow.Proceedings on Privacy Enhancing Technologies(2022)

    work page 2022

  74. [74]

    Vulnerable Populations

    Xinru Tang, Gabriel Lima, Li Jiang, Lucy Simko, and Yixin Zou. 2025. Beyond" Vulnerable Populations": A Unified Understanding of Vulnerability From A Socio-Ecological Perspective.Proceedings of the ACM on Human-Computer Interaction9, 2 (2025), 1–30

    work page 2025

  75. [75]

    Bazarova

    Samuel Hardman Taylor, Dominic DiFranzo, Yoon Hyung Choi, Shruti Sannon, and Natalya N. Bazarova. 2019. Accountability and empathy by design: encouraging bystander intervention to cyberbullying on social media.Proc. ACM Hum.-comput. Interact.3, CSCW (Nov. 2019), 118:1–118:26. doi:10.1145/3359220

    work page doi:10.1145/3359220 2019

  76. [76]

    Phil Turner and Susan Turner. 2011. Is stereotyping inevitable when designing with personas?Design Studies32, 1 (Jan. 2011), 30–44. doi:10.1016/j. destud.2010.06.002

    work page doi:10.1016/j 2011

  77. [77]

    Amos Tversky and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice.science211, 4481 (1981), 453–458

    work page 1981

  78. [78]

    u/heylookitsme. 2021. Anyone who’s complaining about the Friend Activity... Reddit post on r/spotify. https://www.reddit.com/r/spotify/comments/ rhummg/anyone_whos_complaining_about_the_friend_activity/

    work page 2021

  79. [79]

    Aditya Vashistha, Richard Anderson, and Shrirang Mare. 2018. Examining security and privacy research in developing regions. InProceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. 1–14

    work page 2018

  80. [80]

    April Yi Wang, Dakuo Wang, Jaimie Drozdal, Michael Muller, Soya Park, Justin D Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. 2022. Documentation matters: Human-centered ai system to assist data science code documentation in computational notebooks.ACM Transactions on Computer-Human Interaction29, 2 (2022), 1–33

    work page 2022

Showing first 80 references.