Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Constructing AI ethics narratives based on real-world data: Human-AI collaboration in data-driven visual storytelling

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read The paper argues that authentic AI ethics narratives can be built from documented real-world events by dividing the work between humans and generative AI across five story elements, and it demonstrates this workflow on the 2016 Uber…

desk verdict A clear, honest proof-of-concept for a five-element human-AI storytelling framework; the 'authentic real-world' claim is undercut by unlisted sources and admitted AI image errors. read the letter →

arxiv 2502.00637 v1 pith:XVHKVMIH submitted 2025-02-02 cs.HC

Mengyi Wei , Chenjing Jiao , Chenyu Zuo , Lorenz Hurni , Liqiu Meng This is my paper · ORCID
classification cs.HC
keywords AIethicsnarrativesdata-drivenvisualstorytellinghuman-AIcollaborationgenerativecomicboardingincidentdatabaseautonomousvehiclespromptengineering
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 tries to establish that AI ethics narratives can be grounded in documented events rather than science fiction, and that a human-AI division of labor can make that grounding practical. It proposes a five-element story model—structure, time, opinion, reality, yearning—with humans setting direction, theme, and emotional depth while generative AI enriches details and scenes. The authors implement the model on the 2016 Uber autonomous-vehicle red-light incident, piecing together ten news reports into a comic told from four stakeholder perspectives. If the framework works beyond this case, it gives storytellers and policy communicators a repeatable workflow for turning fragmented incident reports into accessible visual narratives.

What carries the argument

The central object is the five-element data-driven visual storytelling model (structure, time, opinion, reality, yearning) used as a division-of-labor scheme between human and AI. Humans act as directors for each element, deciding narrative framework, timeline, theme, emotional depth, and positive values, while a prompt-based image generator expands scenes and fills in concrete details. The machinery also includes a prompt-engineering loop in which structured news texts become image prompts and are iteratively refined through conversational edits until the generated panel matches the documented event; the comicboarding format, a multi-panel sequential-art presentation, then carries the story across panels to a broad audience.

What would settle it

For the 2016 Uber red-light incident, checking the original police report, court record, or full news archive against the comic's key claims would settle authenticity; if the documentary record contradicts any central panel, for example that the vehicle accelerated on its own or that the company's first public statement blamed the driver, then the workflow has not produced a faithful narrative.

Watch

Extended reading notes

Core claim

The central claim is that data-driven visual storytelling with human-AI collaboration can construct authentic AI ethics narratives from real-world news data, and that the five-element model is a valid way to organize that collaboration. In the demonstrated workflow, humans control the overall structure, the timeline, the thematic stance, and the emotional arc; generative AI supplies scene detail, visual realism, and plot progression. The resulting comic about the Uber red-light incident presents the event from the viewpoints of the company, eyewitnesses, former employees, and the government, leaving the question of responsibility open. The authors argue that this open, multi-perspective format counters the misleading effect of a single corporate press release and shifts autonomous-driving ethics discourse away from trolley-problem speculation toward documented events.

Load-bearing premise

The narrative stays authentic only if the ten news reports are themselves complete and accurate; if those reports omit or distort what happened, the comic cannot be a faithful account no matter how well the five-element framework is executed.

Editorial extensions

If this is right

  • The workflow can turn a pile of fragmented news reports into a coherent visual account without a professional illustrator, because the human side needs only direction-setting and prompt refinement.
  • An open-ended, multi-stakeholder comic can expose contradictions between a company's first public statement and later eyewitness and employee accounts, giving readers a reason to question any single report.
  • The five-element division of labor, where humans set structure, theme, timeline, and emotional depth while AI enriches details and scenes, offers a template for other data-storytelling tasks beyond AI ethics.
  • Because the story ends with unresolved responsibility and a government investigation, it supports public debate rather than a manufactured conclusion, which matches the unresolved state of autonomous-vehicle policy.
  • Using documented incidents re-centers autonomous-vehicle ethics on mundane regulatory questions, such as who is accountable when a self-driving car runs a red light, instead of hypothetical trolley problems.

Reading between the lines

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

  • This suggests a concrete stress test: applying the same five-element workflow to other AI incident types, such as bias, privacy, or automation failures, would show whether the human-direction/AI-detail split is general or specific to driving incidents.
  • A reader could go further and audit authenticity by attaching a source sentence from the news reports to each comic panel; the paper does not provide that provenance, but the framework would be stronger for it.
  • Because the paper does not run a user study, the claimed public-engagement benefit remains open: a controlled comparison of the comic against a single news article and a science-fiction scenario could measure shifts in understanding or policy preference.
  • As image generation improves, the iterative prompt-editing bottleneck should shrink, which would shift the human role toward theme selection and factual verification—a change the framework could absorb without redrawing its five elements.
Share X Bluesky LinkedIn Reddit HN

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 proposes a conceptual framework for human-AI collaboration in data-driven visual storytelling about AI ethics, organizing the story model into five elements: structure, time, opinion, reality, and yearning. Humans are described as directing overall narrative, theme, and emotional depth, while generative AI enriches details, expands scenes, and advances plot progression. The framework is implemented on a 2016 Uber self-driving vehicle incident drawn from the AI Incident Database: the authors collected news reports, coded them into stakeholder perspectives, used a GPT-based image generator with iterative prompt engineering to create comic panels, and presented the result as a comicboarding narrative. The paper claims this demonstrates a feasible, repeatable approach to constructing authentic AI ethics narratives based on real-world events, in contrast to science-fiction or corporate narratives.

Significance. If the central claim is supported, the framework offers a concrete workflow for moving AI ethics storytelling from speculative fiction toward documented events, and it usefully assigns complementary roles to humans and generative AI in visual storytelling. The strengths of the paper include its honest acknowledgment of limitations (single case, reliance on expert interpretation), its detailed walk-through of prompt engineering and image correction, and its choice of a multi-stakeholder, open-ended narrative format that is well suited to contested AI ethics incidents. However, the load-bearing claims about 'authenticity' and 'accurate public understanding' are not empirically tested: the story artifact is produced and evaluated by the same researchers, no independent raters or audience study is provided, and the evidentiary chain from news reports to comic panels is not auditable. The contribution is therefore best read as a proof-of-concept design case study, not as a validated method.

major comments (3)
  1. [Section 3.3.1 and Section 4] The 10 news reports that form the data basis are never listed, cited, or quoted with attribution. The text states that 'we collected all news articles related to this incident' but provides no URLs, report titles, or publication dates, so no reader can trace assertions in Figure 9 (e.g., 'Uber argued that pedestrians are not its customers' or 'former employees revealed that Uber's autonomous vehicles running red lights were not uncommon') back to a documented source. Because the paper's central claim is that the resulting narrative is authentic relative to real-world data, this unauditable chain leaves the central claim unsupported as presented. Please include a full source list with per-panel sourcing, an appendix with the structured texts and their provenance, or a clear reclassification of the comic as author interpretation rather than a documentary reconstruction.
  2. [Section 3.3.2] The paper admits that AI-generated images contain mistaken or blurred details, such as 'directions and colors of traffic lights, texts on the car,' yet these images are retained as satisfactory and constitute the visual story. Since the paper's motivation is to give the public an accurate understanding of AI ethics issues, unverified and potentially misleading visual details directly undercut the authenticity claim. The authors should either verify the factual accuracy of every visual element against the source reports, explain why the admitted inaccuracies are immaterial to the ethical content, or modify the claim to acknowledge that the images are illustrative rather than factually accurate reconstructions.
  3. [Section 3.2 and Section 5] The framework's role division is derived from a literature review and the coding of two researchers, and the demonstration is built by the same authors using that framework; there is no independent evaluation of whether the resulting story is authentic, engaging, or informative. This creates a circularity in the evaluation sense: the artifact is judged satisfactory by its creators using criteria they also defined. The paper explicitly defers user research to future work, but then the abstract's claims about 'shaping public accurate understanding' and 'promoting active public engagement' are not backed by evidence in this manuscript. Please either add an evaluation component (independent raters, audience study, or expert assessment against pre-defined criteria) or explicitly scope the paper as a proof-of-concept that does not yet validate those outcome claims.
minor comments (6)
  1. [Abstract] The phrase 'shape the public accurate understanding' should be revised, for example to 'shape the public's accurate understanding' or 'shape accurate public understanding.'
  2. [Keywords] The keyword list mixes semicolons and commas inconsistently and repeats 'Human-AI' with different hyphenation; please standardize the punctuation and style.
  3. [Section 3.3.1] The event summary says the vehicle 'suddenly accelerated against a red light and sped toward the opposite junction,' while the later example text says the car 'ran a red light ... and almost hit a pedestrian who was running the red light'; please reconcile these descriptions or clarify that they come from different reports.
  4. [Section 3.3.3] The text says the story is presented 'from the perspectives of four primary stakeholders: eyewitnesses, Uber as a corporation, Uber employees, and the government,' but Section 3.3.1 earlier says the narrative is structured 'from the perspectives of three key stakeholders'; please make the number of stakeholders consistent.
  5. [Section 3.3.3] The phrase 'The key characteristic of this new case' and later 'our new case' appear to be typographical errors for 'news case'; please correct them throughout.
  6. [General] The text uses 'comicboarding' in some places and 'comicboardings' in others; please choose one consistent term and use it throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the framework is literature-derived and the case study is an explicit proof-of-concept; the auditability gap is an evidence limitation, not a circular derivation.

full rationale

The paper's central derivation is a proposed framework for human-AI collaboration in data-driven visual storytelling, built from external story-model literature (e.g., [12,27,44,63] for the five criteria, and [37,49,54] for storytelling stages). The role division between humans and AI is produced by two researchers coding the literature and reaching consensus; it is not fitted from the target story output. The Uber red-light comic in Figure 9 is an implementation of that framework, demonstrating feasibility rather than testing a prediction. The paper explicitly labels itself a proof-of-concept and acknowledges limitations: only a single case, reliance on human interpretation, and the need for future generalization. The admitted AI image inaccuracies and the unlisted 10 news reports undermine the verifiability of the 'authentic' claim, but that is a sourcing and evidence problem, not a circular reduction of the result to its inputs. The one self-citation ([65]) supports the choice of comicboarding for reflection and emotional resonance, but that claim is not load-bearing for the central human-AI collaboration framework, which stands on the literature-derived structure and the demonstrated workflow. Therefore, no definitional, fitted-input, or self-citation chain forces the paper's conclusions.

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

No free parameters or invented entities are introduced. The central claim rests on several domain assumptions about the story model, data fidelity, coding validity, and comic effectiveness; all are plausible but unverified.

assumptions (4)
  • domain assumption The five criteria (structural clarity, temporal constraints, insightful perspective, realistic plot, aspirational emotion) are sufficient to define a good data-driven visual story.
    Section 3.1 states a good story should meet these five criteria based on a literature review [12,27,44,63], but no empirical validation shows this set is complete or that the derived five elements are the right decomposition.
  • domain assumption The AI Incident Database and the 10 collected news reports provide a faithful record of the 2016 Uber event.
    Section 3.3.1 selects the case and collects reports, but the reports are not listed and no verification or cross-check is described.
  • domain assumption The two researchers' literature-based coding and consensus accurately identifies the appropriate division of labor between humans and AI.
    Section 3.2 describes independent coding followed by consensus, but no inter-rater reliability metric or external validation is reported.
  • domain assumption Comicboarding is an inclusive visual analysis tool that enhances emotional resonance and accessibility.
    Section 3.3.3 cites prior uses [35,55] but does not evaluate whether the specific comic achieves these effects.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Constructing AI ethics narratives based on real-world data: Human-AI collaboration in data-driven visual storytelling." pith.science (2026). https://pith.science/paper/XVHKVMIH

@misc{pith2026250200637,
  author       = {Pith},
  title        = {Pith review of: Constructing AI ethics narratives based on real-world data: Human-AI collaboration in data-driven visual storytelling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XVHKVMIH}},
  note         = {Machine review of arXiv:2502.00637}
}
read the original abstract

AI ethics narratives have the potential to shape the public accurate understanding of AI technologies and promote communication among different stakeholders. However, AI ethics narratives are largely lacking. Existing limited narratives tend to center on works of science fiction or corporate marketing campaigns of large technology companies. Misuse of "socio-technical imaginary" can blur the line between speculation and reality for the public, undermining the responsibility and regulation of technology development. Therefore, constructing authentic AI ethics narratives is an urgent task. The emergence of generative AI offers new possibilities for building narrative systems. This study is dedicated to data-driven visual storytelling about AI ethics relying on the human-AI collaboration. Based on the five key elements of story models, we proposed a conceptual framework for human-AI collaboration, explored the roles of generative AI and humans in the creation of visual stories. We implemented the conceptual framework in a real AI news case. This research leveraged advanced generative AI technologies to provide a reference for constructing genuine AI ethics narratives. Our goal is to promote active public engagement and discussions through authentic AI ethics narratives, thereby contributing to the development of better AI policies.

Figures

Figures reproduced from arXiv: 2502.00637 by the authors.

Figure 1
Figure 1. A framework for human-AI collaboration in data-driven visual storytelling. Abstract AI ethics narratives have the potential to shape the public’s accurate understanding of AI technologies and promote communication among different stakeholders. However, AI ethics narratives are largely lacking. Existing limited narratives tend to center on works of science fiction or corporate marketing campaigns of large technology … view at source ↗
Figure 2
Figure 2. The role division between humans and generative AI in the model of data [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Case study: Data exploration and story ideation for AI news case. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: An example showing that clear, precise and specific texts in a prompt are the key to generate a satisfactory image. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 7
Figure 7. Figure 7: An example of how multiple attempts at prompt engineering generate a satisfactory image. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: The device on the top of the car is changed with the [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: AI ethics narrative based on a real news case as data. [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Red Light, Grey Zone: A Multi-Perspective Interactive Narrative for Autonomous Driving Ethics

    cs.CY 2026-07 conditional novelty 5.0 of 10

    A 12-participant study suggests that a multi-perspective interactive narrative can encourage non-experts to reason about autonomous-driving ethics as distributed responsibility rather than single-actor blame.

Reference graph

Works this paper leans on

79 extracted references · 68 canonical work pages · cited by 1 Pith paper

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt , Sam Altman, Shyamal Anadkat, et al. 2023. Gpt -4 technical report. arXiv preprint arXiv:2303.08774 (2023)

    arXiv 2023

  2. [2]

    Mireia Alemany-Pagès, Anabela Marisa Azul, and João Ramalho -Santos. 2022. The use of comics to promote health awareness: A template using nonalcoholic fatty liver disease. European Journal of Clinical Investigation 52, 3 (2022), e13642

    work page 2022

  3. [3]

    Ahmad Alshami, Moustafa Elsayed, Eslam Ali, Abdelrahman EE Eltoukhy, and Tarek Zayed. 2023. Harnessing the power of ChatGPT for automating systematic review process: Methodology, case study, limitations, and future directions. Systems 11, 7 (2023), 351

    work page 2023

  4. [4]

    Benjamin Bach, Nathalie Henry Riche, Sheelagh Carpendale, and Hanspeter Pfister. 2017. The emerging genre of data comics. IEEE computer graphics and applications 37, 3 (2017), 6–13

    work page 2017

  5. [5]

    Benjamin Bach, Zezhong Wang, Matteo Farinella, Dave Murray -Rust, and Nathalie Henry Riche. 2018. Design patterns for data comics. In Proceedings of the 2018 chi conference on human factors in computing systems. 1–12

    work page 2018

  6. [6]

    Clive Baldwin. 2005. Narrative, ethics and people with severe mental illness. Australian & New Zealand Journal of Psychiatry 39, 11-12 (2005), 1022–1029

    work page 2005

  7. [7]

    Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan. 2016. The social dilemma of autonomous vehicles. Science 352, 6293 (2016), 1573–1576

    work page 2016

  8. [8]

    Aras Bozkurt and Ramesh C Sharma. 2023. Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education 18, 2 (2023), i–vii

    work page 2023

Show all 79 references
  1. [9]

    Stephen Cave and Kanta Dihal. 2019. Hopes and fears for intelligent machines in fiction and reality. Nature machine intelligence 1, 2 (2019), 74–78

    2019

  2. [10]

    Stephen Cave, Kanta Dihal, and Sarah Dillon. 2020. AI narratives: A history of imaginative thinking about intelligent machines. Oxford University Press

    2020

  3. [11]

    Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15, 3 (2024), 1–45

    2024

  4. [12]

    Eugene Charniak. 1972. Toward a model of children’s story comprehension. Ph. D. Dissertation. Massachusetts Institute of Technology

    1972

  5. [13]

    Qing Chen, Shixiong Cao, Jiazhe Wang, and Nan Cao. 2023. How does automation shape the process of narrative visualization: A survey of tools. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  6. [14]

    Eric Chu, Jonathan Dunn, Deb Roy, Geoffrey Sands, and Russell Stevens. 2017. AI in storytelling: Machines as cocreators. McKinsey & Company Media & Entertainment (2017)

    2017

  7. [15]

    Jennifer Chubb, Darren Reed, and Peter Cowling. 2024. Expert views about missing AI narratives: is there an AI story crisis? AI & society 39, 3 (2024), 1107–1126

    2024

  8. [16]

    Mark Coeckelbergh. 2023. Narrative responsibility and artificial intelligence: How AI challenges human responsibility and sense-making. AI & SOCIETY 38, 6 (2023), 2437–2450

    2023

  9. [17]

    Sarah Dillon and Claire Craig. 2021. Storylistening: Narrative evidence and public reasoning. Routledge

    2021

  10. [18]

    Michael Feffer, Nikolas Martelaro, and Hoda Heidari. 2023. The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements. In Proceedings of the 3rd ACM Conference on Equity and Access in ...

    2023

  11. [19]

    Andrea Ferracani, Marco Bertini, Pietro Pala, Gabriele Nannotti, Filippo Principi, and Giuseppe Becchi. 2024. Personalized Generative Storytelling with AI -Visual Illustrations for the Promotion of Knowledge in Cultural Heritage Tourism. In Proceedings of the 6th workshop on t...

    2024

  12. [20]

    Stefan Feuerriegel, Jochen Hartmann, Christian Janiesch, and Patrick Zschech

  13. [21]

    Arthur W Frank and Clive Seale. 1996. The wounded storyteller. Ageing and Society 16, 5 (1996), 634–635

    1996

  14. [22]

    Xin Fu, Yun Wang, Haoyu Dong, Weiwei Cui, and Haidong Zhang. 2019. Visualization assessment: A machine learning approach. In 2019 IEEE Visualization Conference (VIS). IEEE, 126–130

    2019

  15. [23]

    Eduardo C Garrido-Merchán, Jose Luis Arroyo-Barrigüete, Francisco Borrás-Pala, Leandro Escobar-T orres, Carlos Martínez de Ibarreta, Jose María Ortíz-Lozano, and Antonio Rua -Vieites. 2024. Real Customization or Just Marketing: Are Customized Versions of Generative AI Useful? ...

    2024

  16. [24]

    Nahum Gershon and Ward Page. 2001. What storytelling can do for information visualization. Commun. ACM 44, 8 (2001), 31–37

    2001

  17. [25]

    Tripat Gill. 2021. Ethical dilemmas are really important to potential adopters of autonomous vehicles. Ethics and Information Technology 23, 4 (2021), 657–673

    2021

  18. [26]

    Jonathan Gottschall. 2012. The Storytelling Animal: How Stories Make Us Human. Houghton Mifflin Harcourt

    2012

  19. [27]

    Arthur C Graesser and Katja Wiemer -Hastings. 1999. Situation models and concepts in story comprehension. In Narrative comprehension, causality, and coherence. Routledge, 83–98

    1999

  20. [28]

    Rajan Gupta, Gaurav Pandey, and Saibal Kumar Pal. 2024. Automating government report generation: A generative ai approach for efficient data extraction, analysis, and visualization. Digital Government: Research and Practice (2024)

    2024

  21. [29]

    Isabella Hermann. 2020. Beware of fictional AI narratives. Nature Machine Intelligence 2, 11 (2020), 654–654

    2020

  22. [30]

    Marion A Hersh. 2016. Engineers and the other: the role of narrative ethics. AI & society 31 (2016), 327–345

    2016

  23. [31]

    Leslie J Hinyard and Matthew W Kreuter. 2007. Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview. Health education & behavior 34, 5 (2007), 777–792

    2007

  24. [32]

    Andrew Dana Hudson, Ed Finn, and Ruth Wylie. 2023. What can science fiction tell us about the future of artificial intelligence policy? AI & SOCIETY (2023), 1– 15

    2023

  25. [33]

    NZ Jhanjhi, Imdad Ali Shah, and Sarfraz Nawaz. 2025. Generative AI for Visual- ization. In Generative AI for Web Engineering Models. IGI Global, 63–82

    2025

  26. [34]

    Jeff JH Kim, Richard S Um, James WY Lee, and Olusola Ajilore. 2024. Generative AI can fabricate advanced scientific visualizations: ethical implications and strategic mitigation framework. AI and Ethics (2024), 1–13. Constructing AI ethics narratives based on real-world data: ...

    2024

  27. [35]

    Tzu-Sheng Kuo, Hong Shen, Jisoo Geum, Nev Jones, Jason I Hong, Haiyi Zhu, and Kenneth Holstein. 2023. Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services. In Proceedings of the 2023 CHI Conference on Human Factors in Computin...

    2023

  28. [36]

    Xingyu Lan, Yanqiu Wu, and Nan Cao. 2023. Affective visualization design: Leveraging the emotional impact of data. IEEE Transactions on Visualization and Computer Graphics (2023)

    2023

  29. [37]

    Bongshin Lee, Nathalie Henry Riche, Petra Isenberg, and Sheelagh Carpendale

  30. [38]

    Haotian Li, Yun Wang, and Huamin Qu. 2024. Where are we so far? understanding data storytelling tools from the perspective of human -ai collaboration. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–19

    2024

  31. [39]

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre -train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1–35

    2023

  32. [40]

    Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2024. GPT understands, too. AI Open 5 (2024), 208–215

    2024

  33. [41]

    Leo S Lo. 2023. The art and science of prompt engineering: a new literacy in the information age. Internet Reference Services Quarterly 27, 4 (2023), 203–210

    2023

  34. [42]

    Paula Maddigan and Teo Susnjak. 2023. Chat2VIS: generating data visualizations via natural language using ChatGPT, codex and GPT-3 large language models. Ieee Access 11 (2023), 45181–45193

    2023

  35. [43]

    Fernando Marmolejo -Ramos, Thomas Workman, Clint Walker, Don Lenihan, Sarah Moulds, Juan C Correa, Anca M Hanea, and Belona Sonna. 2022. AIpowered narrative building for facilitating public participation and engagement. Discover Artificial Intelligence 2, 1 (2022), 7

    2022

  36. [44]

    Allyssa McCabe and Carole Peterson. 1984. What makes a good story. Journal of Psycholinguistic Research 13, 6 (1984), 457–480

    1984

  37. [45]

    Chunyao Qian, Shizhao Sun, Weiwei Cui, Jian -Guang Lou, Haidong Zhang, and Dongmei Zhang. 2020. Retrieve -then-adapt: Example -based automatic generation for proportion -related infographics. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 443–452

    2020

  38. [46]

    Ahmed Y Radwan, Khaled M Alasmari, Omar A Abdulbagi, and Emad A Alghamdi

  39. [47]

    Jonathan Robinson, Joseph Smyth, Roger Woodman, and Valentina Donzella

  40. [48]

    Alister Scott. 2002. Assessing public perception of landscape: the LANDMAP experience. Landscape research 27, 3 (2002), 271–295

    2002

  41. [49]

    In International Conference on Human-Computer Interaction

    SARD: A Human-AI Collaborative Story Generation. In International Conference on Human-Computer Interaction. Springer, 94–105

  42. [50]

    Anca Serbanescu and Franck Nack. 2023. Human -AI system co -creativity for building narrative worlds. (2023)

    2023

  43. [51]

    Leixian Shen, Enya Shen, Yuyu Luo, Xiaocong Yang, Xuming Hu, Xiongshuai Zhang, Zhiwei Tai, and Jianmin Wang. 2022. Towards natural language interfaces for data visualization: A survey. IEEE transactions on visualization and computer graphics 29, 6 (2022), 3121–3144

    2022

  44. [52]

    Danqing Shi, Xinyue Xu, Fuling Sun, Yang Shi, and Nan Cao. 2020. Calliope: Automatic visual data story generation from a spreadsheet. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 453–463

    2020

  45. [53]

    Edward Segel and Jeffrey Heer. 2010. Narrative visualization: Telling stories with data. IEEE transactions on visualization and computer graphics 16, 6 (2010), 1139–1148

    2010

  46. [54]

    Charles D Stolper, Bongshin Lee, Nathalie Henry Riche, and John Stasko. 2018. Data-driven storytelling techniques: Analysis of a curated collection of visual stories. In Data-driven storytelling. AK Peters/CRC Press, 85–105

    2018

  47. [55]

    Ningjing Tang, Jiayin Zhi, Tzu -Sheng Kuo, Calla Kainaroi, Jeremy J Northup, Kenneth Holstein, Haiyi Zhu, Hoda Heidari, and Hong Shen. 2024. AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate AI Failures in Homeless Services. In The 2024 ACM Conferen...

    2024

  48. [56]

    A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)

    2017

  49. [57]

    Luke Stark. 2024. Animation and Artificial Intelligence. In The 2024 ACM Confer- ence on Fairness, Accountability, and Transparency. 1663–1671

    2024

  50. [58]

    Hong Wang, Amir Khajepour, Dongpu Cao, and Teng Liu. 2020. Ethical decision making in autonomous vehicles: Challenges and research progress. IEEE Intelligent Transportation Systems Magazine 14, 1 (2020), 6–17

    2020

  51. [59]

    Yun Wang, Zhida Sun, Haidong Zhang, Weiwei Cui, Ke Xu, Xiaojuan Ma, and Dongmei Zhang. 2019. Datashot: Automatic generation of fact sheets from tabular data. IEEE transactions on visualization and computer graphics 26, 1 (2019), 895–905

    2019

  52. [60]

    Zezhong Wang, Harvey Dingwall, and Benjamin Bach. 2019. Teaching data visualization and storytelling with data comic workshops. In Extended abstracts of the 2019 CHI conference on human factors in computing systems. 1–9

    2019

  53. [61]

    Juan David Velásquez -Henao, Carlos Jaime Franco -Cardona, and Lorena CadavidHiguita. 2023. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. Dyna 90, 230 (2023), 9–17

    2023

  54. [62]

    David S Watson, Jakob Mökander, and Luciano Floridi. 2024. Competing narratives in AI ethics: a defense of sociotechnical pragmatism. AI & SOCIETY (2024), 1–23

    2024

  55. [63]

    Yves Wautelet, Samedi Heng, Manuel Kolp, and Isabelle Mirbel. 2014. Unifying and extending user story models. In Advanced Information Systems Engineering: 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16 -20,

    2014

  56. [64]

    Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al

  57. [65]

    Zezhong Wang, Shunming Wang, Matteo Farinella, Dave Murray -Rust, Nathalie Henry Riche, and Benjamin Bach. 2019. Comparing effectiveness and engagement of data comics and infographics. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12

    2019

  58. [66]

    Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui, Wucai Wei, Wei Zhao, Guandong Xu, and Hai Jin. 2024. Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study. Proceedings of the ACM on Management of Data 2, 3 (2024), 1–28

    2024

  59. [67]

    Xue Xia, Chenjing Jiao, and Lorenz Hurni. 2023. Contrastive Pretraining for Railway Detection: Unveiling Historical Maps with Transformers. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. 30–33

    2023

  60. [68]

    Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, et al. 2023. Instruction -vit: Multi -modal prompts for instruction learning in vit. arXiv preprint arXiv:2305.00201 (2023)

    2023 arXiv

  61. [69]

    Yilin Ye, Jianing Hao, Yihan Hou, Zhan Wang, Shishi Xiao, Yuyu Luo, and Wei Zeng

  62. [70]

    arXiv preprint arXiv:2206.07682 (2022)

    Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)

    2022 arXiv

  63. [71]

    Mengyi Wei, Chenjing Jiao, Chenyu Zuo, Lorenz Hurni , and Liqiu Meng. 2024. How Generative AI Supports Understanding of An Ethically Sensitive AI-Induced Event. Abstracts of the ICA 8 (2024), 26

    2024

  64. [76]

    Visual Informatics (2024)

    Generative ai for visualization: State of the art and future directions. Visual Informatics (2024)

    2024

  65. [77]

    Chao Zhang, Cheng Yao, Jiayi Wu, Weijia Lin, Lijuan Liu, Ge Yan, and Fangtian Ying

  66. [78]

    In Proceedings of the 2022 CHI conference on human factors in computing systems

    StoryDrawer: a child–AI collaborative drawing system to support children’s creative visual storytelling. In Proceedings of the 2022 CHI conference on human factors in computing systems. 1–15

    2022

  67. [79]

    Zhenpeng Zhao, Rachael Marr, and Niklas Elmqvist. 2015. Data comics: Sequential art for data-driven storytelling. tech. report (2015)

    2015

  68. [2014]

    Springer, 211–225

    Proceedings 26. Springer, 211–225

  69. [2015]

    IEEE computer graphics and applications 35, 5 (2015), 84–90

    More than telling a story: Transforming data into visually shared stories. IEEE computer graphics and applications 35, 5 (2015), 84–90

    2015

  70. [2022]

    Theoretical issues in ergonomics science 23, 4 (2022), 435–452

    Ethical considerations and moral implications of autonomous vehicles and unavoidable collisions. Theoretical issues in ergonomics science 23, 4 (2022), 435–452

    2022

  71. [2024]

    Business & Information Systems Engineering 66, 1 (2024), 111–126

    Generative ai. Business & Information Systems Engineering 66, 1 (2024), 111–126

    2024

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.