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REVIEW 4 major objections 5 minor 50 references

Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Expert-written privacy answers outperform Amazon Alexa's live responses and policy excerpts on clarity and usability in a comparative user study, while aiming to remain legally precise.

desk verdict Genuinely new CAI privacy Q&A dataset with a well-documented expert process, but the usability claim rests on four participants and the legal-precision claim on a single lawyer. read the letter →

arxiv 2502.01306 v1 pith:G3EKU4GP submitted 2025-02-03 cs.HC

Anna Leschanowsky , Farnaz Salamatjoo , Zahra Kolagar , Birgit Popp This is my paper · ORCID
classification cs.HC
keywords privacyQAtransparencyconversationalAIpoliciesexpert-in-the-loopuserstudybest-worstscaling
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 conversational assistants can be transparent about personal-data processing only if they answer users' privacy questions with short, direct, human-crafted language, and that the current alternatives—Alexa's canned responses and verbatim policy excerpts—fail at this. The authors built a privacy Q&A dataset by collecting 400 questions from a scenario-driven survey and prior corpora, selecting 42 representative ones, and having privacy technologists, conversational designers, and a data-protection lawyer iteratively draft and review answers. A linguistic analysis and a user study with four linguistically trained participants found the designed answers easier to read and rated them higher in quality and usability than either baseline, while still sounding appropriately formal. If the finding holds, privacy question-answering systems should move from extracting policy text to drawing on expert-reviewed answer banks.

What carries the argument

The machinery is an experts-in-the-loop answer pipeline followed by a two-part evaluation. Questions from a scenario-driven survey and existing corpora are reduced from 400 to 42 representative items using Semantic Textual Similarity with Sentence-BERT embeddings. Draft answers are revised successively by a privacy technologist, three conversational designers, and a data-protection lawyer, producing 103 legally reviewed answers; sentence embeddings again select the two most distinct variants per question so the user study can compare answer styles. Evaluation combines objective linguistic indices (readability and lexical diversity) with a Best-Worst Scaling user study and inductive coding of participants' explanations.

What would settle it

Have a panel of independent data-protection lawyers score each designed answer against the source policy for accuracy and completeness; if the answers omit required disclosures or misstate the policy, the preciseness claim fails even though the usability results still stand. A complementary test is a larger user study with non-expert consumers, since the reported ratings come from four linguistically trained participants.

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Extended reading notes

Core claim

The paper's central claim is that answers authored through an iterative expert-in-the-loop process beat both existing solutions—Amazon Alexa's live responses and excerpts from Amazon's privacy policy and help pages—on the dimensions users care about: quality, usability, and difficulty, without losing the formal tone users associate with legal information. In the quantitative user study, Designed Answer 2 was rated best in 53% of trials for both quality and usability, while Alexa answers were rated worst in 73% and 87% of trials for those metrics. The qualitative interviews trace the advantage to stylistic specifics: participants described the designed answers as straightforward and praised imperative constructions ('do this' rather than 'you can') and consent-framing keywords such as 'permission' that make the user's control explicit. The paper also documents that Alexa, in January 2023, could answer only four out of 42 privacy questions correctly and defaulted to excuses or redirections for most of the rest.

Load-bearing premise

The legal-precision claim rests on the approval of a single data-protection lawyer, with no objective accuracy metric applied to the final answers; the authors note that different legal experts can interpret policy language differently, so another reviewer might have produced different answers.

Editorial extensions

If this is right

  • Voice assistants could substitute expert-crafted privacy answers for the current fallback of 'Sorry, I don't know that' or redirects to help pages.
  • The released dataset, with 42 questions and multiple expert-reviewed answers per question, gives researchers a benchmark for privacy Q&A that reflects conversational language rather than legal boilerplate.
  • Quality and usability were rated so similarly that future privacy-answer evaluations may be able to use a single combined metric instead of two separate ones.
  • Specific lexical choices—imperatives and consent keywords like 'permission'—appear to shape users' sense of control and should be treated as design decisions, not just wording.

Reading between the lines

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

  • Because legal preciseness rests on a single reviewer, the dataset's answers are best described as 'expert-approved' rather than 'legally verified'; a multi-expert consensus process or a compliance checklist would make the claim testable.
  • The user study used linguistically trained participants, so the usability advantage may not transfer unchanged to typical consumers; a replication with naive users would tell whether the readability gains matter in practice.
  • The two 'most distinct' designed answers per question implicitly define a space of acceptable phrasings; mining those pairs could yield paraphrase templates that reduce the cost of scaling expert review to new products or jurisdictions.
  • Participants reacted strongly to the word 'permission,' which suggests that even single lexical choices carry legal weight for users; a controlled experiment varying only that keyword could quantify the effect.
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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 / 5 minor

Summary. The paper introduces an expert-generated privacy Q&A dataset for conversational AI, constructed from a scenario-driven question-collection survey supplemented with existing privacy QA corpora, and an iterative expert-in-the-loop answer-generation process involving a privacy technologist, conversational designers, and a data protection lawyer. The authors evaluate the resulting 'designed answers' against Amazon Alexa responses and privacy policy excerpts using linguistic readability/lexical-diversity metrics and a mixed-method user study (best-worst scaling plus semi-structured interviews) with four participants. The central claim, stated in the abstract and echoed in Section 6, is that the designed answers improve usability and clarity compared with existing solutions while achieving legal preciseness.

Significance. If the claims were fully supported, the dataset and the expert-in-the-loop method would be a useful contribution to privacy transparency research for conversational AI: the paper targets a real gap (privacy Q&A for voice-based assistants), uses a realistic Alexa baseline collected and re-checked over time, includes questions about voice recordings and other assistant-specific data types, and provides a qualitative codebook linking linguistic features to user perceptions. The authors also make the dataset publicly available. However, the headline comparative claim currently rests on very thin empirical evidence, and the legal-precision claim lacks a verifiable basis, so the significance of the contribution is conditional on substantial revision of both the evidence and the claims.

major comments (4)
  1. [§5, Figure 2] The central claim that 'designed answers improve usability and clarity compared to existing solutions' rests entirely on a user study with four participants, all internally recruited, uncompensated, and with professional linguistics expertise (§5.2). No significance tests, confidence intervals, or variance-partitioning models (e.g., mixed-effects models accounting for participant and item variance) are reported. With only four participants, each individual constitutes 25% of the panel, and the reported percentages in Figure 2 are highly sensitive to the choices of one or two individuals; the 53% 'best' rating for Designed Answer 2 on quality and usability is plausibly driven by a small number of participants. The abstract and Section 6 therefore overstate what the data can establish.
  2. [§3.2.3, §7] The claim that the proposed answers 'achieve legal preciseness' is not supported by the evidence presented. Legal validity rests on feedback from a single data protection lawyer (§3.2.3), with no objective legal-accuracy metric applied to the final answers. The authors themselves concede in Section 7 that 'legal experts can interpret policy language differently' and that different experts might have produced different answers. The user-study ratings measure perceived 'lawyerliness', which is a different construct from legal preciseness; the paper should either provide a rigorous legal-accuracy assessment or explicitly limit the claim to perceived legal tone.
  3. [§5.2, §3.1] The evaluation sample is not representative of the intended user population. All four user-study participants are linguistics experts, whose judgments of clarity, usability, and complexity may reflect professional norms rather than the experience of typical voice-assistant users. The privacy-question collection also relied on 11 internally recruited, uncompensated participants (§3.1). Although Section 7 acknowledges these limitations, the conclusion and abstract do not carry the necessary caveats, making the generalizable 'usability and clarity' claim stronger than the sampling warrants.
  4. [§3.2.3, §5.1] The comparison between designed answers and privacy policy excerpts is partly circular. Section 3.2.3 states that the designed answers were 'explicitly derived from the extracted excerpts', and the excerpts are used as one of the baseline conditions in the user study. The usability advantage of the designed answers may therefore reflect the distillation/simplification step rather than a general property of expert-generated answers, and the paper should discuss this interpretive limitation explicitly. In addition, the design should ideally include an independent baseline (e.g., answers generated by a non-expert paraphrasing process or an automated extractive system) to isolate the effect of expert revision.
minor comments (5)
  1. [Title page] The title page still contains ACM template placeholders ('Do Not Use This Code', 'Make sure to enter the correct conference title', and a 2018 copyright year) that should be cleaned before submission.
  2. [§5.1] The phrase 'see Appendix 3' appears to refer to Appendix F (the Mechanical Turk Sandbox interface); the reference should be corrected.
  3. [Figure 2] The caption does not state whether the percentages are conditional on each answer type being shown in a trial, nor does it report the denominator; this should be clarified so that readers can interpret the 'Not Chosen' category.
  4. [Table 1] The linguistic metrics are reported as medians only, without the number of texts or any dispersion measure; adding this information would help assess whether the differences between Designed Answers 1 and 2 are meaningful.
  5. [§3.2.1] The paper reports that Alexa answers were collected in January 2023 and re-evaluated in January 2025, but it does not specify collection dates for the policy excerpts or the designed answers in the dataset metadata; including per-answer-type collection dates would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central usability and clarity comparison rests on external baselines and human ratings, not on the paper's own fitted values.

full rationale

The paper's central claim is an empirical evaluation claim, not a derivation from equations or fitted parameters. The designed answers are compared against externally sourced Amazon Alexa responses and manually extracted Amazon privacy policy excerpts, with usability, quality, difficulty, and lawyerliness ratings collected from human participants in a best-worst scaling study. Those ratings are independent of the answer-generation procedure: nothing in the construction of the designed answers numerically forces the observed preference percentages or linguistic metric values. The fact that designed answers were explicitly derived from the same policy excerpts they are compared against is a deliberate design choice for coherence, but it does not make the comparison circular, because the evaluation instruments measure readability and subjective preference rather than equivalence to the source text. The legal-preciseness claim is supported by expert revision rather than by an objective metric, and the authors acknowledge in Section 7 that different legal experts might produce different answers; this is a validity limitation, not a circular reduction. The only self-citation, reference [23], describes the expert-in-the-loop workflow and is not load-bearing: the present paper's data collection, linguistic analysis, and user study are independently described and checkable. No step in the paper's argument reduces by construction to its own inputs, so no circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper contains no free numeric parameters or invented entities. The central claims rest on domain assumptions about expert review, participant representativeness, and automated selection validity, all of which are acknowledged as limitations in Section 7.

assumptions (3)
  • domain assumption The designed answers are legally precise because one external data protection lawyer reviewed and revised them (Section 3.2.3).
    No legal accuracy metric or independent audit is applied; Section 7 concedes different legal experts may differ.
  • domain assumption Four participants, all linguistics experts, provide a user sample sufficient to evaluate usability and clarity of privacy answers (Section 5.2).
    The sample is internally recruited, not compensated, and not representative of typical conversational assistant users; Section 7 acknowledges this limits generalizability.
  • domain assumption Sentence-BERT semantic similarity selects representative questions without biasing the final dataset (Section 3.1).
    The selection step chooses one representative question per information type and data practice; the validity of the resulting coverage is assumed, not measured.

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Cite this review

Pith. "Pith review of Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights." pith.science (2026). https://pith.science/paper/G3EKU4GP

@misc{pith2026250201306,
  author       = {Pith},
  title        = {Pith review of: Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G3EKU4GP}},
  note         = {Machine review of arXiv:2502.01306}
}
read the original abstract

Conversational assistants process personal data and must comply with data protection regulations that require providers to be transparent with users about how their data is handled. Transparency, in a legal sense, demands preciseness, comprehensibility and accessibility, yet existing solutions fail to meet these requirements. To address this, we introduce a new human-expert-generated dataset for Privacy Question-Answering (Q&A), developed through an iterative process involving legal professionals and conversational designers. We evaluate this dataset through linguistic analysis and a user study, comparing it to privacy policy excerpts and state-of-the-art responses from Amazon Alexa. Our findings show that the proposed answers improve usability and clarity compared to existing solutions while achieving legal preciseness, thereby enhancing the accessibility of data processing information for Conversational AI and Natural Language Processing applications.

Figures

Figures reproduced from arXiv: 2502.01306 by the authors.

Figure 1
Figure 1. Overview of the data collection process in the study and a privacy question-answering example. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Quantitative results comparing the four answer types across the metrics of quality, difficulty, usability, and lawyerliness. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Example of the questions interface in the Amazon Mechanical Turk Sandbox as seen by participants during the interviews. [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The figure displays the three most frequently occurring codes in the interview feedback for each metric, demonstrating which [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]

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Reference graph

Works this paper leans on

50 extracted references · 41 canonical work pages

  1. [1]

    Wasi Ahmad, Jianfeng Chi, Yuan Tian, and Kai-Wei Chang. 2020. PolicyQA: A Reading Comprehension Dataset for Privacy Policies. In Findings of the Association for Computational Linguistics: EMNLP 2020 , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 743–749. https://doi.org/10.18653/v1/2020.findings-emnlp.66

    doi:10.18653/v1/2020.findings-emnlp.66 2020

  2. [2]

    Amazon. 2023. Amazon.com Privacy Notice. https://www.amazon.com/gp/help/customer/display.html?nodeId=468496

    work page 2023

  3. [3]

    Siddhant Arora, Henry Hosseini, Christine Utz, Vinayshekhar Bannihatti Kumar, Tristan Dhellemmes, Abhilasha Ravichander, Peter Story, Jasmine Mangat, Rex Chen, Martin Degeling, Thomas Norton, Thomas Hupperich, Shomir Wilson, and Norman Sadeh. 2022. A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus. In Proceedings...

    work page 2022

  4. [4]

    Article 29 Data Protection Working Party. 2018. Guidelines on Transparency under Regulation 2016/679. https://ec.europa.eu/newsroom/article29/ redirection/document/51025 Accessed: 2024-11-22

    work page 2018

  5. [5]

    Susanne Barth, Dan Ionita, and Pieter Hartel. 2022. Understanding Online Privacy—A Systematic Review of Privacy Visualizations and Privacy by Design Guidelines. Comput. Surveys 55, 3 (Feb. 2022), 63:1–63:37. https://doi.org/10.1145/3502288

    work page doi:10.1145/3502288 2022

  6. [6]

    Rochelle A Cadogan et al. 2004. An imbalance of power: the readability of internet privacy policies. Journal of Business & Economics Research (JBER) 2, 3 (2004)

    work page 2004

  7. [7]

    Fred H. Cate. 2010. The Limits of Notice and Choice. IEEE Security & Privacy 8, 02 (March 2010), 59–62. https://doi.org/10.1109/MSP.2010.84

    doi:10.1109/msp.2010.84 2010

  8. [8]

    Steve Cohen et al. 2003. Maximum difference scaling: improved measures of importance and preference for segmentation. In Sawtooth software conference proceedings, Vol. 530. Sawtooth Software, Inc. Provo, UT, USA, 61–74

    work page 2003

Show all 50 references
  1. [9]

    Council of Europe. 2018. Modernised Convention for the Protection of Individuals with regard to the Processing of Personal Data (Convention 108+). https://rm.coe.int/modernised-convention-108/16808e943c

    2018

  2. [10]

    Lorrie Faith Cranor. 2012. Necessary but Not Sufficient: Standardized Mechanisms for Privacy Notice and Choice. Journal on Telecommunications and High Technology Law 10 (2012), 273

    2012

  3. [11]

    nutrition

    Pardis Emami-Naeini, Janarth Dheenadhayalan, Yuvraj Agarwal, and Lorrie Faith Cranor. 2021. An informative security and privacy “nutrition” label for internet of things devices. IEEE Security & Privacy 20, 2 (2021), 31–39

    2021

  4. [12]

    European Commission. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Gener...

    2016

  5. [13]

    Rudolf Franz Flesch and Alan J Gould. 1949. The art of readable writing

    1949

  6. [14]

    FTC. 2012. Protecting Consumer Privacy in an Era of Rapid Change: Recommendations For Businesses and Policymakers. https://www.ftc.gov/ reports/protecting-consumer-privacy-era-rapid-change-recommendations-businesses-policymakers. Manuscript submitted to ACM 10 Leschanowsky et al

    2012

  7. [15]

    Sanjana Gautam. 2022. In Alexa, We Trust. Or Do We? : An Analysis of People’s Perception of Privacy Policies. arXiv:arXiv:2209.00086

    2022 arXiv

  8. [16]

    Halkenhäußer, Nicola Leschke, Johanna Washington, Cristina Paupini, and Frank Pallas

    Elias Grünewald, Johannes M. Halkenhäußer, Nicola Leschke, Johanna Washington, Cristina Paupini, and Frank Pallas. 2023. Enabling Versatile Privacy Interfaces Using Machine-Readable Transparency Information. In Privacy Symposium 2023, Stefan Schiffner, Sébastien Ziegler, and M...

    2023

  9. [17]

    Simon Gunst and Ferdi De Ville. 2021. The Brussels Effect: How the GDPR Conquered Silicon Valley. European Foreign Affairs Review 26, 3 (2021), 437–458. https://kluwerlawonline.com/journalarticle/European+Foreign+Affairs+Review/26.3/EERR2021036

    2021

  10. [18]

    Aamir Hamid, Hemanth Reddy Samidi, Tim Finin, Primal Pappachan, and Roberto Yus. 2023. PrivacyLens: A Framework to Collect and Analyze the Landscape of Past, Present, and Future Smart Device Privacy Policies. arXiv preprint arXiv:2308.05890 (2023)

    2023 arXiv

  11. [19]

    Shin, and Karl Aberer

    Hamza Harkous, Kassem Fawaz, Kang G. Shin, and Karl Aberer. 2016. PriBots: Conversational Privacy with Chatbots. In Twelfth Symposium on Usable Privacy and Security (SOUPS 2016) . USENIX Association, Denver, CO. https://www.usenix.org/conference/soups2016/workshop- program/wfp...

    2016

  12. [20]

    Elfrieda H Hiebert. [n. d.]. Unique Words Require Unique Instruction. https://textproject.org/paper/unique-words-require-unique-instruction/. Accessed: 2025-01-02

    2025

  13. [21]

    nutrition label

    Patrick Gage Kelley, Joanna Bresee, Lorrie Faith Cranor, and Robert W. Reeder. 2009. A "nutrition label" for privacy. In Proceedings of the 5th Symposium on Usable Privacy and Security (Mountain View, California, USA) (SOUPS ’09). Association for Computing Machinery, New York,...

    2009

  14. [22]

    Rie Koizumi and Yo In’nami. 2012. Effects of text length on lexical diversity measures: Using short texts with less than 200 tokens. System 40, 4 (2012), 554–564

    2012

  15. [23]

    Zahra Kolagar, Anna Leschanowsky, and Birgit Popp. 2023. Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&A. In CONVERSATIONS 2023 - The 7th International Workshop on Chatbot Research and Design . Oslo

    2023

  16. [24]

    California State Legislature. 2018. California Consumer Privacy Act (CCPA). https://oag.ca.gov/privacy/ccpa

    2018

  17. [25]

    2015.Best-worst scaling: Theory, methods and applications

    Jordan J Louviere, Terry N Flynn, and Anthony Alfred John Marley. 2015.Best-worst scaling: Theory, methods and applications . Cambridge University Press

    2015

  18. [26]

    Dorian Lynskey. 2019. ’Alexa, are you invading my privacy?’ – the dark side of our voice assistants. https://www.theguardian.com/technology/ 2019/oct/09/alexa-are-you-invading-my-privacy-the-dark-side-of-our-voice-assistants. [Online; accessed 2023-09-12]

    2019

  19. [27]

    Eric Martínez, Francis Mollica, and Edward Gibson. 2023. Even lawyers do not like legalese. Proceedings of the National Academy of Sciences 120, 23 (2023), e2302672120. https://doi.org/10.1073/pnas.2302672120 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.2302672120

    2023 doi

  20. [28]

    Aleecia M McDonald and Lorrie Faith Cranor. 2008. The cost of reading privacy policies. Isjlp 4 (2008), 543

    2008

  21. [29]

    George R Milne, Mary J Culnan, and Henry Greene. 2006. A longitudinal assessment of online privacy notice readability. Journal of Public Policy & Marketing 25, 2 (2006), 238–249

    2006

  22. [30]

    Moore and Raphael Arar

    Robert J. Moore and Raphael Arar. 2019. Conversational UX Design: A Practitioner’s Guide to the Natural Conversation Framework . Association for Computing Machinery, New York, NY, USA

    2019

  23. [31]

    Victor Morel and Raúl Pardo. 2020. SoK: Three Facets of Privacy Policies. In Proceedings of the 19th Workshop on Privacy in the Electronic Society (WPES’20). Association for Computing Machinery, New York, NY, USA, 41–56. https://doi.org/10.1145/3411497.3420216

    2020

  24. [32]

    Government of Brazil. 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). http://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709. htm

    2018

  25. [33]

    John O’hayre. 1966. Gobbledygook has gotta go . US Department of the Interior, Bureau of Land Management

    1966

  26. [34]

    Abhilasha Ravichander, Alan W Black, Thomas Norton, Shomir Wilson, and Norman Sadeh. 2021. Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International ...

    2021 doi

  27. [35]

    Abhilasha Ravichander, Alan W Black, Shomir Wilson, Thomas Norton, and Norman Sadeh. 2019. Question Answering for Privacy Policies: Combining Computational and Legal Perspectives. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the...

    2019 doi

  28. [36]

    Joel R Reidenberg, Travis Breaux, Lorrie Faith Cranor, Brian French, Amanda Grannis, James T Graves, Fei Liu, Aleecia McDonald, Thomas B Norton, Rohan Ramanath, et al. 2015. Disagreeable privacy policies: Mismatches between meaning and users’ understanding. Berkeley Tech. LJ 3...

    2015

  29. [37]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJC...

    2019 doi

  30. [38]

    Daniel Reinhardt, Johannes Borchard, and Jörn Hurtienne. 2021. Visual Interactive Privacy Policy: The Better Choice?. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY,...

    2021

  31. [39]

    Brianne Sandorf. 2021. Smart Assistant Privacy: What Data Is Collected and How to Protect Yourself. https://www.reviews.org/home-security/smart- assistant-privacy-what-data-is-collected-and-how-to-protect-yourself/. [Online; accessed 2023-09-12]. Manuscript submitted to ACM Ex...

    2021

  32. [40]

    Kanthashree Mysore Sathyendra, Abhilasha Ravichander, Peter Garth Story, Alan W Black, and Norman Sadeh. 2017. Helping Users Understand Privacy Notices with Automated Query Answering Functionality: An Exploratory Study . Technical Report. Carnegie Mellon University

    2017

  33. [41]

    Durity, and Lorrie Faith Cranor

    Florian Schaub, Rebecca Balebako, Adam L. Durity, and Lorrie Faith Cranor. 2018. A Design Space for Effective Privacy Notices*. In The Cambridge Handbook of Consumer Privacy (first ed.), Evan Selinger, Jules Polonetsky, and Omer Tene (Eds.). Cambridge University Press, 365–393...

    2018 doi

  34. [42]

    Eva-Maria Schomakers, Chantal Lidynia, Dirk Müllmann, and Martina Ziefle. 2019. Internet Users’ Perceptions of Information Sensitivity – Insights from Germany. International Journal of Information Management 46 (June 2019), 142–150. https://doi.org/10.1016/j.ijinfomgt.2018.11.018

    2019 doi

  35. [43]

    Daniel J. Solove. 2013. Privacy Self-Management and the Consent Dilemma. Harvard Law Review 126, 7 (2013), 1880–1903. https://harvardlawreview. org/2013/05/privacy-self-management-and-the-consent-dilemma/

    2013

  36. [44]

    Akchay Srivastava and Atif Memon. 2024. Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models. IEEE Access (2024)

    2024

  37. [45]

    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

  38. [46]

    Cameron Russell, Thomas B

    Shomir Wilson, Florian Schaub, Aswarth Abhilash Dara, Frederick Liu, Sushain Cherivirala, Pedro Giovanni Leon, Mads Schaarup Andersen, Sebastian Zimmeck, Kanthashree Mysore Sathyendra, N. Cameron Russell, Thomas B. Norton, Eduard Hovy, Joel Reidenberg, and Norman Sadeh

  39. [47]

    Fred Zenker and Kristopher Kyle. 2021. Investigating minimum text lengths for lexical diversity indices. Assessing Writing 47 (2021), 100505

    2021

  40. [48]

    Sebastian Zimmeck, Peter Story, Daniel Smullen, Abhilasha Ravichander, Ziqi Wang, Joel R Reidenberg, N Cameron Russell, and Norman Sadeh

  41. [2016]

    In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    The Creation and Analysis of a Website Privacy Policy Corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Berlin, Germany, 1330–1340. https://doi.org/10.18653/v1...

    doi

  42. [2019]

    but your mom doesn’t like roses

    Maps: Scaling privacy compliance analysis to a million apps. Proc. Priv. Enhancing Tech. 2019 (2019), 66. Manuscript submitted to ACM 12 Leschanowsky et al. A Scenario-Driven Survey for Privacy Question Collection My Perfect Voice Assistant You and your husband have recently b...

    2019

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