Pith. sign in

REVIEW 3 major objections 6 minor 42 references

Identifying Ethical Challenges in XR Implementations in the Industrial Domain: A Case of Off-Highway Machinery

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read This paper claims that five privacy and ethical challenges — bystander privacy, uneven digitalization, 24/7 data collection, over-reliance, and AI model training — are the most relevant for developing XR in off-highway machinery.

desk verdict A modest, preliminary challenge list for XR in off-highway machinery that is useful as a starting point but over-aggregates three heterogeneous machine domains. read the letter →

arxiv 2507.02868 v1 pith:HIQAIHCZ submitted 2025-04-21 cs.HC

Anastasia Sergeeva , Claudia Negri-Ribalta , Gabriele Lenzini This is my paper · ORCID
classification cs.HC
keywords extendedrealityprivacyethicsoff-highwaymachinerybystandervalue-sensitivedesignindustrialXRdataprotection
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 compiles the authors' experience building XR systems for off-highway machinery — excavators, snow groomers, and reach stackers — and distills it into a short list of the five privacy and ethical challenges designers should tackle first. The list is grounded in Value-Sensitive Design workshops with end-users, privacy threat-modeling sessions with stakeholders, GDPR-based questionnaires, and interviews with industry experts, triangulated with existing XR ethics frameworks. If the paper is right, developers in this niche industrial domain can use the list as a starting point for designing consent, data-minimization, and trust-preserving XR, rather than relying on generic XR guidelines that may not fit the workplace.

What carries the argument

The argument is carried by a multi-method elicitation procedure: Value-Sensitive Design workshops with end-users (n=11), Linddun Go privacy threat-modeling sessions with stakeholders and developers (n=10) using a GDPR-based questionnaire, and expert interviews (n=3 per use-case domain) across excavators, snow groomers, and reach stackers. The five challenges emerge from triangulating these participatory inputs with general XR ethics frameworks, which supplies the domain-specific grounding that generic XR guidelines lack.

What would settle it

Ask a representative sample of operators and experts from each of the three subdomains to rank the five challenges by relevance; if the rankings differ substantially among excavators, snow groomers, and reach stackers, the claim that these are the five challenges of off-highway machinery overall is falsified.

Watch

Extended reading notes

Core claim

The central claim is that the most relevant privacy and ethical challenges for XR in off-highway machinery are (1) protecting bystanders who may be unknowingly captured, (2) coping with the sector's uneven digitalization so new systems do not disrupt existing business processes or introduce new surveillance risks, (3) reconciling round-the-clock site security monitoring with data minimization and data-subject rights, (4) preventing operators from over-relying on XR outputs and losing the ability to cross-check with real-world indicators, and (5) fine-tuning AI algorithms without accumulating bystander data that cannot later be deleted. The authors present these as a preliminary, non-exhaustive list intended to start a discussion rather than close it.

Load-bearing premise

The load-bearing premise is that excavators, snow groomers, and reach stackers are similar enough to be treated as one off-highway machinery domain; if they are not, the aggregated list of five challenges may not accurately describe any one of them.

Editorial extensions

If this is right

  • XR systems for off-highway machinery should include explicit bystander notification and consent mechanisms, since capture without awareness is treated as a primary risk.
  • Designers should align XR data collection with the existing digitalization baseline and avoid introducing new performance surveillance on top of low-digitalization workflows.
  • Security-driven 24/7 monitoring needs to be reconciled with data-minimization principles and bystanders' rights to access and delete their data.
  • Interfaces must be designed to foster appropriate trust and cross-checking, especially for junior operators, because over-reliance on XR outputs can lead to physical harm.
  • AI fine-tuning of XR algorithms should avoid retaining bystander data without consent, and should respect data-protection regulations.

Reading between the lines

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

  • If the challenge list is used as a design checklist, the uneven digitalization among excavators, snow groomers, and reach stackers may require subdomain-specific versions; a comparative ranking exercise across the three sectors would test how uniformly the list applies.
  • Future work could turn the three challenge questions under each heading into concrete design probes or requirements for an XR headset demo, allowing researchers to measure whether addressing one challenge conflicts with another, for example data minimization versus security.
  • The same triangulated approach could be transferred to neighboring domains such as agricultural machinery or warehouse logistics, where similar low-digitalization and bystander questions are likely to appear.
  • The paper's emphasis on over-reliance suggests a measurable usability target: an XR interface should allow operators to complete a task without the system, or to detect system malfunction, without degrading performance; this could be tested in simulator studies.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper reports on the authors' experience developing XR solutions for off-highway machinery and proposes five privacy/ethical challenges: protecting bystander privacy, managing varying digitalization, addressing 24-hour data collection, preventing overreliance on XR outputs, and handling AI model training data. The method combines a literature review, Value-Sensitive Design workshops with end-users (n=11), stakeholder/developer workshops using Linddun Go and a GDPR-based questionnaire (n=10), and three industry-expert interviews, which are triangulated into a preliminary challenge list. The paper is explicitly positioned as a starting point for further discussion rather than an exhaustive or final analysis.

Significance. If the proposed challenges are robust, they provide a useful, domain-specific complement to general XR ethics frameworks and give practitioners in an under-studied industrial niche a concrete starting point for design and governance decisions. The paper's strengths are its focus on a real, non-office industrial setting, its transparent presentation of expert-interview summaries in Appendix B, and its honest hedging as preliminary work. However, the empirical grounding is currently too loose to fully support the domain-wide claim, and the aggregation of three heterogeneous machinery subsectors is a substantive limitation that must be addressed.

major comments (3)
  1. [Section 3.1, Appendices A and B] The paper does not describe how the workshop and interview data were analyzed to produce the five challenges in Section 3.2. No coding scheme, thematic analysis procedure, or traceability table is presented, so the central claim 'Based on our work, we identified key challenges...' (Section 3.2) is not verifiable from the reported methods. Please specify the analysis steps and, where possible, map each challenge to the specific workshop or interview statements that support it, or alternatively label the challenges as primarily literature-derived with pilot input.
  2. [Section 3.2.3 and Appendix B] The '24/7 data collection' challenge is motivated by container terminals and construction sites, but the expert-interview summaries in Appendix B show substantial divergence across the three subsectors: snow groomers collect GNSS and weather-telematics data, reachstackers collect GPS and performance metrics, and excavators mainly capture speed/direction data stored on the vehicle. The claim that this challenge applies uniformly to 'off-highway machinery' is therefore unsupported. The paper should either present the challenges per subsector or explicitly discuss which sectors are affected by each challenge and which are not.
  3. [Section 3.2.5 and Appendix B] The AI-model-training challenge rests almost entirely on the reachstacker expert's statement that anonymized data can be used for training AI; the snow groomer expert reports no personal operator data used for AI, and the excavator expert says AI use is 'just starting.' Generalizing this to a domain-wide challenge overstates the evidence. This challenge should be reframed as sector-specific or supported with additional data that shows cross-sector relevance.
minor comments (6)
  1. [Section 3.1] The phrase 'fist steps' is a typo and should be 'first steps'.
  2. [Abstract and title] The title and abstract contain the typo 'th e' (space between 'th' and 'e'); please correct these spacing errors.
  3. [Section 2.1] The text states 'without the user’s explicit content'; this appears to be a typo for 'explicit consent.'
  4. [References] Reference [28] is formatted as 'McGill and Mark' but should be 'Mark McGill' (and include co-author(s) if any); please correct the author list.
  5. [Acknowledgments] The acknowledgments contain the typo 'European Uninion'; it should be 'European Union.'
  6. [Section 3.2 and conclusion] The wording of the central claim fluctuates among 'main challenges', 'relevant challenges', 'most relevant', and 'first round of the main challenges'; please standardize this language to match the actual scope of the paper (preliminary, not exhaustive).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the challenge list is a qualitative triangulation of workshops, interviews, and external literature, with no fitted parameters or self-citation chain.

full rationale

The paper makes no mathematical or predictive claim; it reports a triangulated qualitative synthesis. Section 3.1 describes the method (Value-Sensitive Design workshops, stakeholder workshops, expert interviews, and analysis of privacy frameworks), and Section 3.2 presents the resulting five challenges explicitly as 'a preliminary list of identified challenges that aims to serve as a starting point but does not intend to be exhaustive.' There are no equations, no fitted parameters, and no target quantity that is defined in terms of the output. The only load-bearing empirical premise is the aggregation of excavators, snow groomers, and reach stackers into one 'off-highway machinery' domain, but this is a validity or representativeness concern, not a circularity concern: the challenge list would be the same list regardless of whether the three subdomains are fully homogeneous, and the paper does not claim to derive the list from the aggregation itself. No self-citation is used to support the central claim; the frameworks cited in Section 2 are external works, and the authors do not rely on their own prior publications as evidence. The paper also explicitly labels its findings as preliminary and non-exhaustive, which further shows the list is an interpretation of collected data rather than a disguised restatement of its inputs. Therefore, there is no circular step to report, and the appropriate score is 0.

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

No free parameters or invented entities apply to this qualitative position paper. The central claim rests on domain assumptions about instrument validity, sample representativeness, domain coherence, and the completeness of cited frameworks. These assumptions are not empirically tested in the paper.

assumptions (4)
  • domain assumption Value-Sensitive Design and the short Schwartz Value Survey are appropriate instruments to surface privacy/ethics concerns in industrial XR workplaces.
    The methodology in Section 3.1 builds the workshops on these tools; if they miss domain-specific values, the resulting challenge list would be incomplete.
  • domain assumption Excavators, snow groomers, and reach stackers form a coherent 'off-highway machinery' domain.
    The paper aggregates findings across the three 'use case domains' (Section 3.1) into one challenge list, despite variation documented in Appendix B.
  • domain assumption The 24 participants across workshops and interviews are representative of relevant stakeholders in the domain.
    No sampling strategy or data saturation argument is given in Section 3.1; the central list depends on these responses.
  • domain assumption The cited frameworks (e.g., IEEE, XRSI, E3XR) are a sufficient baseline so that domain-specific gaps can be identified.
    The paper's gap analysis in Section 2.2 assumes these guidelines are complete for general XR but insufficient for this specific domain.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Identifying Ethical Challenges in XR Implementations in the Industrial Domain: A Case of Off-Highway Machinery." pith.science (2026). https://pith.science/paper/HIQAIHCZ

@misc{pith2026250702868,
  author       = {Pith},
  title        = {Pith review of: Identifying Ethical Challenges in XR Implementations in the Industrial Domain: A Case of Off-Highway Machinery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HIQAIHCZ}},
  note         = {Machine review of arXiv:2507.02868}
}
read the original abstract

Although extended reality(XR)-using technologies have started to be discussed in the industrial setting, it is becoming important to understand how to implement them ethically and privacy-preservingly. In our paper, we summarise our experience of developing XR implementations for the off-highway machinery domain by pointing to the main challenges we identified during the work. We believe that our findings can be a starting point for further discussion and future research regarding privacy and ethical challenges in industrial applications of XR.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 40 canonical work pages

  1. [1]

    Melvin Abraham, Mark Mcgill, and Mohamed Khamis. 2024. W hat You Experi- ence is What We Collect: User Experience Based Fine-Grained Permissions for Everyday Augmented Reality. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–24

    work page 2024

  2. [2]

    Melvin Abraham, Pejman Saeghe, Mark Mcgill, and Mohamed Khamis. 2022. Im- plications of XR on Privacy, Security and Behaviour: Insigh ts from Experts. In Nordic Human-Computer Interaction Conference . 1–12

    work page 2022

  3. [3]

    Alessandro Acquisti, Ralph Gross, and Fred Stutzman. 20 11. Privacy in the age of augmented reality. Proc. National Academy of Sciences (2011)

    work page 2011

  4. [4]

    Devon Adams, Alseny Bah, Catherine Barwulor, Nureli Mus aby, Kadeem Pitkin, and Elissa M Redmiles. 2018. Ethics emerging: the story of pr ivacy and security perceptions in virtual reality. In SOUPS@ USENIX Security Symposium. 427–442

    work page 2018

  5. [5]

    Muhammad Aizat, Nurakasyah Qistina, and Wan Rahiman. 20 23. A Comprehen- sive Review of Recent Advances in Automated Guided Vehicle T echnologies: Dynamic Obstacle Avoidance in Complex Environment Toward A utonomous Capability. IEEE Transactions on Instrumentation and Measurement (2023)

    work page 2023

  6. [6]

    Simon Albrecht, Andrew Marty, and Nicholas J Brandon-Jo nes. 2020. Measuring values at work: Extending existing frameworks to the contex t of work. Journal of Career Assessment 28, 4 (2020), 531–550

    work page 2020

  7. [7]

    A’aeshah Alhakamy. 2024. Extended Reality (XR) Toward B uilding Immersive Solutions: The Key to Unlocking Industry 4.0. Comput. Surveys 56, 9 (2024), 1– 38

    work page 2024

  8. [8]

    Peter Casey, Ibrahim Baggili, and Ananya Yarramreddy. 2 019. Immersive virtual reality attacks and the human joystick. IEEE Transactions on Dependable and Secure Computing 18, 2 (2019), 550–562

    work page 2019

Show all 42 references
  1. [9]

    Brendan David-John, Diane Hosfelt, Kevin Butler, and Ea kta Jain. 2021. A privacy-preserving approach to streaming eye-tracking da ta. IEEE Transactions on Visualization and Computer Graphics 27, 5 (2021), 2555–2565

    2021

  2. [10]

    Jaybie A De Guzman, Kanchana Thilakarathna, and Aruna S eneviratne. 2019. Security and privacy approaches in mixed reality: A literat ure survey. ACM Computing Surveys (CSUR) 52, 6 (2019), 1–37

    2019

  3. [11]

    Luís Fernando de Souza Cardoso, Flávia Cristina Martin s Queiroz Mariano, and Ezequiel Roberto Zorzal. 2020. A survey of industrial augme nted reality. Com- puters & Industrial Engineering 139 (2020), 106159

    2020

  4. [12]

    Parth Dipakkumar Patel and Prem Trivedi. 2022. A system atic literature review on Virtual Reality and Augmented Reality in terms of privacy, authorization and data-leaks. arXiv e-prints (2022), arXiv–2212

    2022

  5. [13]

    Vladimir A Dokuchaev, Victoria V Maklachkova, and V Yu S tatev. 2020. Data subject as augmented reality. Synchroinfo journal 6, 1 (2020), 11–15

    2020

  6. [14]

    Åsa Fast-Berglund, Liang Gong, and Dan Li. 2018. Testin g and validating Ex- tended Reality (xR) technologies in manufacturing. Procedia Manufacturing 25 (2018), 31–38

    2018

  7. [15]

    Spyros Fountas, Borja Espejo-García, Aikaterini Kasi mati, Marilena Gemtou, Hercules Panoutsopoulos, and Evangelos Anastasiou. 2024. Agriculture 5.0: Cutting-Edge Technologies, Trends, and Challenges. IT Professional 26, 1 (2024), 40–47

    2024

  8. [16]

    Batya Friedman. 1996. Value-sensitive design. interactions 3, 6 (1996), 16–23

    1996

  9. [17]

    Alberto Giaretta. 2022. Security and Privacy in Virtua l Reality–A Literature Survey. arXiv preprint arXiv:2205.00208 (2022)

    2022 arXiv

  10. [18]

    Hilda Hadan, Derrick M Wang, Lennart E Nacke, and Leah Zhang-Kennedy. 2024. Privacy in immersive extended reality: Exploring user perc eptions, concerns, and coping strategies. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–24

    2024

  11. [19]

    Andreas Halbig, Sooraj K Babu, Shirin Gatter, Marc Eric h Latoschik, Kirsten Brukamp, and Sebastian von Mammen. 2022. Opportunities and challenges of virtual reality in healthcare–a domain experts inquiry.Frontiers in Virtual Reality 3 (2022), 14

    2022

  12. [20]

    Yan Huang, Yi Joy Li, and Zhipeng Cai. 2023. Security and privacy in metaverse: A comprehensive survey. Big Data Mining and Analytics 6, 2 (2023), 234–247

    2023

  13. [21]

    XR Safety Initiative. 2020. The XRSI privacy framework

    2020

  14. [22]

    Mohamed Khamis and Florian Alt. 2021. Privacy and secur ity in augmentation technologies. Technology-Augmented Perception and Cognition (2021), 257–279

    2021

  15. [23]

    Joey J Lee and Elliot Hu-Au. 2021. E3XR: An analytical fr amework for ethical, educational and eudaimonic XR design. Frontiers in Virtual Reality 2 (2021), 697667

    2021

  16. [24]

    Marjaana Lindeman and Markku Verkasalo. 2005. Measuri ng values with the short Schwartz’s value survey. Journal of personality assessment 85, 2 (2005), 170–178

    2005

  17. [25]

    Rui Luo, Mark Zolotas, Drake Moore, and Taskin Padir. 20 24. User-customizable Shared Control for Fine Teleoperation via Virtual Reality. arXiv preprint arXiv:2403.13177 (2024)

    2024 arXiv

  18. [26]

    Yifang Ma, Zhenyu Wang, Hong Yang, and Lin Yang. 2020. Ar tificial intelligence applications in the development of autonomous vehicles: A s urvey. IEEE/CAA Journal of Automatica Sinica 7, 2 (2020), 315–329

    2020

  19. [27]

    Daniel L Marino, Javier Grandio, Chathurika S Wickrama singhe, Kyle Schroeder, Keith Bourne, Afroditi V Filippas, and Milos Manic. 2020. AI augmentation for trustworthy AI: Augmented robot teleoperation. In 2020 13th International Con- ference on Human System Interaction (HSI)...

    2020

  20. [28]

    McGill and Mark. 2021. White Paper-The IEEE Global Init iative on Ethics of Extended Reality (XR) Report–Extended Reality (XR) and t he Erosion of Anonymity and Privacy. Extended Reality (XR) and the Erosion of Anonymity and Privacy-White Paper (2021), 1–24

    2021

  21. [29]

    Abu Jafar Md Muzahid, Syafiq Fauzi Kamarulzaman, Md Araf atur Rahman, Say- dul Akbar Murad, Md Abdus Samad Kamal, and Ali H Alenezi. 2023 . Multiple vehicle cooperation and collision avoidance in automated v ehicles: Survey and an AI-enabled conceptual framework. Scientific repo...

    2023

  22. [30]

    Chris Norval, Richard Cloete, and Jatinder Singh. 2023 . Navigating the Au- dit Landscape: A Framework for Developing Transparent and A uditable XR. In Proceedings of the 2023 ACM Conference on Fairness, Accounta bility, and Trans- parency. 1418–1431

    2023

  23. [31]

    Joseph O’Hagan, Pejman Saeghe, Jan Gugenheimer, Danie l Medeiros, Karola Marky, Mohamed Khamis, and Mark McGill. 2023. Privacy-Enha ncing Tech- nology and Everyday Augmented Reality: Understanding Byst anders’ Varying Needs for Awareness and Consent. Proceedings of the ACM on ...

    2023

  24. [32]

    Suchismita Pahi and Calli Schroeder. 2023. Extended Pr ivacy for Extended Re- ality: XR Technology Has 99 Problems and Privacy Is Several o f Them. Notre Dame J. on Emerging Tech. 4 (2023), 1

    2023

  25. [33]

    Timo Partala, Maria Jokiniemi, and Veikko Surakka. 200 0. Pupillary responses to emotionally provocative stimuli. In Proceedings of the 2000 symposium on Eye tracking research & applications . 123–129

    2000

  26. [34]

    Greig Paul and James Irvine. 2014. Privacy implication s of wearable health de- vices. In Proceedings of the 7th International Conference on Security of Information and Networks. 117–121

    2014

  27. [35]

    Derek Reilly, Mohamad Salimian, Bonnie MacKay, Niels M athiasen, W Keith Edwards, and Juliano Franz. 2014. SecSpace: prototyping us able privacy and security for mixed reality collaborative environments. In Proceedings of the 2014 ACM SIGCHI symposium on Engineering interactiv...

    2014

  28. [36]

    Shalom H Schwartz. 2012. An overview of the Schwartz the ory of basic values. Online readings in Psychology and Culture 2, 1 (2012), 11

    2012

  29. [37]

    Frank Ursin, Cristian Timmermann, Lasse Benzinger, Sa bine Salloch, and Fabian- Alexander Tietze. 2024. Intraoperative application of mixed and augmented real- ity for digital surgery: a systematic review of ethical issu es. Frontiers in Surgery 11 (2024), 1287218

    2024

  30. [38]

    Minna Vasarainen, Sami Paavola, and Liubov Vetoshkina . 2021. A systematic literature review on extended reality: Virtual, augmented and mixed reality in working life. International Journal of Virtual Reality 21, 2 (2021), 1–28

    2021

  31. [39]

    Binbin Wang, Lianyu Zheng, Yiwei Wang, Wei Fang, and Lih ui Wang. 2024. To- wards the industry 5.0 frontier: Review and prospect of XR in product assembly. Journal of Manufacturing Systems 74 (2024), 777–811

    2024

  32. [40]

    Kim Wuyts, Laurens Sion, and Wouter Joosen. 2020. Lindd un go: A lightweight approach to privacy threat modeling. In 2020 IEEE European Symposium on Se- curity and Privacy Workshops (EuroS&PW) . IEEE, 302–309

    2020

  33. [41]

    Ruide Zhang, Ning Zhang, Changlai Du, Wenjing Lou, Y Tho mas Hou, and Yuichi Kawamoto. 2017. From electromyogram to password: ex ploring the pri- vacy impact of wearables in augmented reality. ACM Transactions on Intelligent Systems and Technology (TIST) 9, 1 (2017), 1–20

    2017

  34. [42]

    Mohammad Javad Zoleykani, Hamidreza Abbasianjahromi , Saeed Banihashemi, Seyed Amir Tabadkani, and Aso Hajirasouli. 2023. Extended r eality (XR) tech- nologies in the construction safety: systematic review andanalysis. Construction Innovation (2023). CHI’25, April 2025, Yokoha...

    2023

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.