The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data

Priya Dhawka; Sayamindu Dasgupta

REVIEW 3 major objections 3 minor 73 references

The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data

T0 review · 3 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Visualizing race and gender data is a value-laden, political process shaped by the designer's own identity, values, and views of neutrality, according to interviews with 17 visualization practitioners.

desk verdict Useful first empirical study of visualization practitioners' own accounts of how identity and politics shape their work, but the central claim should be read as self-report, not demonstrated artifact-level causation. read the letter →

arxiv 2502.09048 v2 pith:STIXTZH3 submitted 2025-02-13 cs.HC

Priya Dhawka , Sayamindu Dasgupta This is my paper · ORCID

classification cs.HC

keywords visualizationdesignersvaluesfeministepistemologydemographicdataraceandgenderpositionalityneutralitysocialconstruction

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 sets out to establish that the production of visualizations of protected demographic data—race and gender—is not a neutral technical act but a value-laden and political process. Drawing on 17 semi-structured interviews with US-based visualization practitioners, it argues that designers' personal experiences, values, beliefs, positions in power structures, and understandings of neutrality interact to shape the charts they make. It interprets these accounts through the feminist concept of situated knowledges, which holds that all knowledge is produced from a particular position. If correct, the claim matters because it moves the debate about social construction in visualization from audiences and researchers to the practitioners who actually build the artifacts, and because it implies that tools, workflows, and best practices that assume designer neutrality are inadequate for race and gender data.

What carries the argument

The analytical engine is the feminist concept of situated knowledges, applied to visualization practitioners: the idea that what a designer can see, know, and communicate is limited and shaped by their position in the world. The empirical machinery is a 17-participant semi-structured interview study with a protocol that prompts designers to describe their identities, politics, values, and biases, followed by reflexive thematic analysis to synthesize themes about neutrality, power, politics, and positionality.

What would settle it

A content analysis of a large corpus of race/gender visualizations that found no systematic association between designers' stated positionality (for instance, explicit value-laden versus neutrality-claiming) and their design choices—icon style, color, category ordering, annotations, treatment of absent groups—would undercut the claim that positionality shapes artifacts. So would an ethnographic study of a visualization team's design process that found personal beliefs and politics playing no role in their decisions.

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Extended reading notes

Core claim

In the paper's own terms, the central discovery is that visualizing protected demographic data is a value-laden and political process as a result of interactions between designers' personal experiences, values, their understandings of power, politics, and neutrality, and the socially constructed nature of demographic categories themselves. The interview data show practitioners navigating tensions: they are acutely aware that census categories are socially constructed and can erase nonbinary and non-White people; they make consequential choices about how to visualize absence; they hold divergent definitions of neutrality and objectivity; they experience power as relational and political; and their identities—especially minoritized identities—shape how they approach and are treated in their work. The authors frame these findings as an empirical study of situated knowledges among visualization designers.

Load-bearing premise

The argument rests on the assumption that interviewees' direct, prompted answers about their identities, politics, values, and neutrality accurately reflect how they actually design and what they actually build, rather than socially desirable narratives; it also assumes that 17 mostly White, male, US-based volunteers can stand in for visualization practitioners broadly.

Editorial extensions

If this is right

Visualization tools and workflows that assume designer neutrality are poorly matched to protected demographic data; tools should support disclosure of the designer's positionality and the data's context.
Designers working with race and gender data need institutional safeguards against online harassment and against their visualizations being misappropriated as misinformation.
The visualization research community should treat designers' situated knowledges as a resource for equitable practice, including through participatory and co-design work with the groups being visualized.
The same dynamics likely extend to other politically consequential demographic categories, such as migration data used in public discourse.

Reading between the lines

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

If the paper is right, 'neutral' visualizations may systematically default to majority-group perspectives; one testable implication is that a corpus of visualizations labeled neutral would show systematic choices that align with dominant demographic positions.
A direct experimental extension would ask designers with different stated positionalities to encode the same race/gender dataset and compare their outputs on iconography, color, ordering, annotations, and handling of absent categories.
The paper's practitioner-side findings could be joined to audience-side research: in an experiment, adding a designer's positionality disclosure to a visualization could change audience trust, comprehension, or perceived bias.
The focus on self-report suggests a complementary observational study: shadowing designers in their workplaces to see whether stated values actually appear in their design processes and final artifacts.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

The takeaway: this is a genuinely useful empirical study, but its central claim—that visualizing race and gender data is a value-laden, political process—is best read as a description of what 17 practitioners told the authors, not as a demonstrated causal story about the artifacts themselves. That distinction matters more than the paper admits.

What's new: this is the first interview study I know of that specifically asks visualization practitioners about how their positionality, politics, and views of neutrality shape their work with protected demographic data. Prior critical/feminist visualization work has mostly focused on audiences, researchers, or artifacts. The paper fills a real gap. It also does qualitative methods properly: the full interview protocol is in the appendix, the authors quote participants extensively, and they acknowledge their sample skew (12 White, 9 male, US-based) and the limits of reproducibility. That's honest.

The soft spots are real but not fatal. The protocol explicitly asks 'How would you describe the identities that are most important to you and your politics?' and 'How do your personal beliefs, values, biases or politics influence your design process?' So the finding that identity, politics, and bias matter is partly invited by the instrument. The stress-test note is right about that. Also, the authors ask participants to share visualizations, but no artifact-level analysis appears in the findings; RQ3's reference to 'resulting data visualizations' is not empirically checked. So the causal framing in Section 6 and the conclusion ('influence the end product') goes beyond what self-reports alone can establish.

That said, the concern is not devastating. Some of the most powerful material—P12's account of mapping the Pulse shooting as a queer journalist, P17's reflections on Gaza and trust in data—was clearly unprompted. And in a semi-structured interview, asking directly about values is normal practice. The paper would be stronger if it consistently framed the findings as practitioners' perceptions rather than as established effects on artifacts. That's a revision, not a rejection.

Bottom line: this paper deserves serious peer review. It is a solid empirical contribution for visualization researchers working on critical/feminist approaches, for tool designers, and for data journalism practitioners. I'd send it to a qualified qualitative HCI reviewer and ask for the claims to be calibrated to the evidence. I would not desk reject.

Referee Report

3 major / 3 minor

Summary. This paper reports a qualitative interview study of 17 US-based visualization practitioners who design visualizations of race and gender demographic data. The authors analyze semi-structured interviews to identify challenges around socially constructed demographic categories, tensions between neutrality and other design values, experiences of power and politics in workplace settings, and the role of designers' identities and positionality. They argue in Section 6 that visualizing protected demographic data is a value-laden and political process shaped by interactions among these factors, and they propose implications for safeguarding public visualizations, prioritizing designer safety, and designing visualization tools for epistemological pluralism.

Significance. If the central claim is accepted, the paper provides empirical grounding for feminist and critical visualization research by extending the concept of situated knowledges to visualization practitioners, a population that prior work has largely treated as neutral conduits. The study is transparently reported, includes rich verbatim participant accounts, and builds directly on prior work on anthropographics and critical visualization. The paper's strengths are its focus on practitioner perspectives, its use of a feminist analytical lens, and its explicit acknowledgment of sample limitations. However, the central claim rests primarily on self-reported accounts that the interview protocol itself invites, and the findings do not analyze the visualization artifacts mentioned in the research questions, so the empirical support is narrower than the Discussion suggests.

major comments (3)

[Appendix A, §5.3–§5.5] The interview protocol explicitly invites the paper's central vocabulary: Appendix A asks participants "How would you describe the identities that are most important to you and your politics?" and "How do your personal beliefs, values, biases or politics influence your design process?", and the neutrality questions directly elicit the construct analyzed in §5.3. The themes in §5.3, §5.4, and §5.5 therefore may be partly an artifact of the instrument rather than emergent from practice. The manuscript should distinguish prompted from unprompted evidence (e.g., P12's Pulse shooting account, P17's Gaza remarks) or report the prevalence of each theme across participants, and the Discussion in §6 should temper the claim accordingly.
[RQ3, §4.3, §5.1] RQ3 asks about influence on "the resulting data visualizations", and the protocol in §4.3 asks participants to share visualization artifacts, yet the Findings present no analysis of any artifact. All quoted evidence concerns designers' accounts of their process, not verified properties of the products. The central claim in §6 that visualizing protected demographic data "is a value-laden and political process" is therefore supported only at the level of perceived influence. Either add a small artifact-level analysis (e.g., comparing shared visualizations with the designers' descriptions) or revise RQ3 and the §6 claim to refer to designers' perceived influence on their process.
[§4.1–§4.2, §6.1] The sample is 17 self-selected US-based volunteers, with 12 White and 9 male participants, recruited through visualization subreddits, the Data Visualization Society, and targeted journalism outreach. The authors acknowledge this skew in §6.1, but the generalizing formulation in §6 ("visualizing protected demographic data is...") goes beyond what this sample can establish. Please add an explicit transferability argument or qualify the central claim to the practitioners studied.

minor comments (3)

[§5.3.1] The phrase "we highlight a smaller sample of these responses for brevity" is vague; please specify how many participants were asked about neutrality and how many distinct definitions were collected, so readers can assess the range of views.
[§4.4] A table summarizing the final themes, the number of participants contributing to each, and the distribution of prompted versus unprompted instances would substantially improve the transparency of the thematic analysis.
[§2.2] The statement that the visualization research community "has yet to critically engage" with researcher positionality is too strong given the critical and feminist work cited in §2.1; suggest "has not yet fully engaged" or similar.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a transparent qualitative interview study whose claims rest on participant accounts, not on a derivation from the instrument or on load-bearing self-citation.

full rationale

This paper makes no formal predictive or derivational claim; it reports a reflexive thematic analysis of 17 semi-structured interviews. The central argument in Section 6 that visualizing protected demographic data is value-laden and political is an interpretive synthesis of participant quotations rather than a quantity fitted from or defined by the data. The interview guide in Appendix A does directly ask about identity, politics, values, and bias, so some themes are solicited; but the paper is transparent about the protocol, and the findings also include unsolicited accounts (e.g., P12's Pulse shooting map and P13's anti-trans bills story) and an explicitly emergent theme on objectivity in Section 5.3.2. The self-citations to Dhawka et al. [25, 26] motivate and contextualize the study but are not load-bearing: the empirical content stands on the interview evidence, and the authors acknowledge their own positionality and the sample's skew toward White and male US volunteers. No equation, fitted parameter, or uniqueness theorem is invoked, and no result is equivalent to its input by construction. Methodological worries about prompted self-reports are a validity threat, not circularity.

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

The central claim rests on three domain assumptions that are reasonable for qualitative research but not independently verified: that interview self-reports track actual design practice, that the situated-knowledges lens is the right interpretive frame, and that US-centric protected categories define the phenomenon. No free parameters were fit to data, and the paper introduces no new entities, particles, forces, or mechanisms.

assumptions (3)

domain assumption Participants' self-reports in interviews accurately represent their actual design processes and the artifacts they produce.
Invoked throughout section 5, where quotes are treated as evidence about design practice. Interview data are self-reported and retrospective, and no artifact analysis or observational verification is used.
domain assumption The feminist concept of situated knowledges from Haraway (1988) is an appropriate interpretive lens for all participants' accounts.
Used in section 6.1 to frame findings. The paper does not test alternative interpretations of the interview data, which is a standard theory-informed qualitative assumption.
domain assumption US census categories and EEOC protected characteristics define the scope of protected demographic data relevant to all practitioners studied.
Established in sections 1 and 3. The findings are explicitly US-centric, and the authors acknowledge that transferability to other countries is limited.

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Cite this review

Pith. "Pith review of The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data." pith.science (2026). https://pith.science/paper/STIXTZH3

@misc{pith2026250209048,
  author       = {Pith},
  title        = {Pith review of: The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/STIXTZH3}},
  note         = {Machine review of arXiv:2502.09048}
}

read the original abstract

Data visualizations are increasingly seen as socially constructed, with several recent studies positing that perceptions and interpretations of visualization artifacts are shaped through complex sets of interactions between members of a community. However, most of these works have focused on audiences and researchers, and little is known about if and how practitioners account for the socially constructed framing of data visualization. In this paper, we study and analyze how visualization practitioners understand the influence of their beliefs, values, and biases in their design processes and the challenges they experience. In 17 semi-structured interviews with designers working with race and gender demographic data, we find that a complex mix of factors interact to inform how practitioners approach their design process, including their personal experiences, values, and their understandings of power, neutrality, and politics. Based on our findings, we suggest a series of implications for research and practice in this space.

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Works this paper leans on

73 extracted references · 46 canonical work pages

[1]

Philip E Agre. 1995. Institutional circuitry: Thinking about the forms and uses of information. Information Technology and Libraries 14, 4 (1995), 225–230

work page 1995
[2]

Philip E Agre. 2014. Toward a critical technical practic e: Lessons learned in trying to reform AI. In Social science, technical systems, and cooperative work . Psychology Press, 131–157

work page 2014
[3]

Derya Akbaba, Lauren Klein, and Miriah Meyer. 2024. Enta nglements for Visualization: Changing Research Outcomes through Femini st Theory. IEEE Transactions on Visualization and Computer Graphics 31, 1 (2024), 11 pages. https://doi.org/10.1109/TVCG.2024.3456171

arXiv 2024
[4]

Derya Akbaba, Devin Lange, Michael Correll, Alexander L ex, and Miriah Meyer

work page
[5]

McKane Andrus, Elena Spitzer, Jeﬀrey Brown, and Alice Xi ang. 2021. What We Can’t Measure, We Can’t Understand: Challenges to Demogr aphic Data Procurement in the Pursuit of Fairness. In Proceedings of the 2021 ACM Con- ference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York,...

work page arXiv 2021
[6]

McKane Andrus and Sarah Villeneuve. 2022. Demographic- Reliant Algo- rithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 17...

arXiv 2022
[7]

Foster Osei Baah, Anne M Teitelman, and Barbara Riegel. 2 019. Marginaliza- tion: Conceptualizing Patient Vulnerabilities in the Fram ework of Social Deter- minants of Health—An integrative Review. Nursing Inquiry 26, 1 (2019), e12268. https://doi.org/10.1111/nin.12268

work page doi:10.1111/nin.12268 2019
[8]

Bako, Xinyi Liu, Leilani Battle, and Zhicheng L iu

Hannah K. Bako, Xinyi Liu, Leilani Battle, and Zhicheng L iu. 2023. Under- standing how Designers Find and Use Data Visualization Exam ples. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2023), 1048–1058. https://doi.org/10.1109/TVCG.2022.3209490

arXiv 2023

Show all 73 references

[9]

Shaowen Bardzell and Jeﬀrey Bardzell. 2011. Towards a fe minist HCI method- ology: social science, feminism, and HCI. In Proceedings of the SIGCHI Confer- ence on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). Association for Computing Machinery, New Yor...

2011
[10]

Charles Berret and Tamara Munzner. 2024. Iceberg Sense making: A Process Model for Critical Data Analysis. IEEE Transactions on Visualization and Com- puter Graphics (2024), 1–18. https://doi.org/10.1109/TVCG.2024.34866 13

2024
[11]

Jeremy Boy, Anshul Vikram Pandey, John Emerson, Margar et Satterthwaite, Oded Nov, and Enrico Bertini. 2017. Showing People Behind Da ta: Does An- thropomorphizing Visualizations Elicit More Empathy for H uman Rights Data?. In Proceedings of the 2017 CHI Conference on Human Fa...

2017
[12]

Philip Bump. 2024. Analysis | A look at Trump’s misleadi ng, inac- curate graph of U.S. immigration. The Washington Post (May 2024). https://www.washingtonpost.com/politics/2024/05/23/look-trumps-misleading-inaccurate-graph-us-

2024
[13]

Florent Cabric, Margrét Vilborg Bjarnadóttir, Anne-F lore Cabouat, and Petra Isenberg. 2023. Open Questions about the Visualization of S ociodemographic Data. In 2023 IEEE Workshop on Visualization for Social Good (VIS4Go od). IEEE, 16–20. https://doi.org/10.1109/VIS4Good60218...

2023
[14]

Florent Cabric, Margrét Vilborg Bjarnadóttir, Meng Li ng, Guðbjörg Linda Rafnsdóttir, and Petra Isenberg. 2024. Eleven Years of Gend er Data Vi- sualization: A Step Towards More Inclusive Gender Represen tation. IEEE Transactions on Visualization and Computer Graphics 30, 1 (2...

2024
[15]

Census Bureau. 1997. About the Topic of Race. Retrieved September 14, 2023 from https://perma.cc/WH3H-X76F

1997
[16]

Census Bureau. 2021. About Age and Sex. Retrieved Decem ber 10, 2024 from https://www.census.gov/topics/population/age-and-se x/about.html

2021
[17]

Chen, Angela D

Yiqun T. Chen, Angela D. R. Smith, Katharina Reinecke, a nd Alexandra To
[18]

Matthew Clair and Jeﬀrey S Denis. 2015. Sociology of Rac ism. The Inter- national Encyclopedia of the Social and Behavioral Science s 19 (2015), 857–63. https://projects.iq.harvard.edu/ﬁles/deib-explorer/ ﬁles/sociology_of_racism.pdf

2015
[19]

In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23)

Why, when, and from whom: considerations for collecti ng and report- ing race and ethnicity data in HCI. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Asso- ciation for Computing Machinery, New York, NY, USA, Arti...

2023
[20]

Victoria Clarke and Virginia Braun. 2017. Thematic ana lysis. The journal of positive psychology 12, 3 (2017), 297–298

2017
[21]

Eva Clark, Karla Fredricks, Laila Woc-Colburn, Maria E lena Bottazzi, and Jill Weatherhead. 2020. Disproportionate Impact of the COVID-1 9 Pandemic on Immigrant Communities in the United States. PLoS Neglected Tropical Diseases 14, 7 (2020), e0008484. https://doi.org/10.1371/j...

2020 doi
[23]

United States Equal Employment Opportunity Commissio n. 1966. EEO- 1 Component 1 Data Collection. Retrieved September 9, 2024 f rom https://perma.cc/388J-JZV5

1966
[24]

Priya Dhawka, Helen Ai He, and Wesley Willett. 2022. Rep resenting marginalized populations: Challenges in anthropographic s. arXiv preprint arXiv:2210.02660 (2022)

2022 arXiv
[25]

Nick Couldry and Ulises Mejias. 2019. Making data colon ialism liveable: how might data’s social order be regulated? Internet Policy Review 8, 2 (2019), 1–16

2019
[26]

Priya Dhawka, Lauren Perera, and Wesley Willett. 2024. Better Little People Pictures: Generative Creation of Demographically Diverse Anthropographics. In Proceedings of the CHI Conference on Human Factors in Computi ng Systems (Honolulu, HI, USA) (CHI ’24). Association for Com...

2024
[27]

Priya Dhawka, Helen Ai He, and Wesley Willett. 2023. We A re the Data: Challenges and Opportunities for Creating Demograph ically Diverse Anthropographics. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Associatio...

2023
[28]

Marian Dörk, Patrick Feng, Christopher Collins, and Sh eelagh Carpendale. 2013. Critical InfoVis: Exploring the Politics of Visualization . In CHI ’13 Extended Abstracts on Human Factors in Computing Systems (Paris, France) (CHI EA ’13). Association for Computing Machinery, Ne...

2013
[29]

Catherine D’Ignazio and Lauren F Klein. 2020. 1. The Pow er Chapter. In Data Feminism. The MIT Press, Cambridge, Massachusetts

2020
[30]

Catherine D’Ignazio and Rahul Bhargava. 2020. 13. Data visualization liter- acy: A feminist starting point. In Data Visualization in Society , Martin En- gebretsen and Helen Kennedy (Eds.). Amsterdam University P ress, 207–222. https://doi.org/10.1515/9789048543137-017

2020 doi
[31]

Cecília C

Emanuel Felipe Duarte and M. Cecília C. Baranauskas. 20 16. Revisiting the Three HCI Waves: A Preliminary Discussion on Philosophy of Science and Research Paradigms. In Proceedings of the 15th Brazilian Symposium on Human Factors in Computing Systems (São Paulo, Brazil) (IHC ’...
[32]

Melanie Feinberg. 2022. Everyday Adventures with Unruly Data . MIT Press, Cambridge, Massachusetts

2022
[33]

Tommaso Elli, Adam Bradley, Uta Hinrichs, and Christop her Collins. 2022. Visualizing stories of sexual harassment in the academy: Co mmunity empow- erment through qualitative data. DRS Biennial Conference Series (June 2022). https://dl.designresearchsociety.org/drs-conference...

2022
[34]

Benjamin Forest. 2005. The changing demographic, lega l, and technological con- texts of political representation. Proceedings of the National Academy of Sciences 102, 43 (2005), 15331–15336

2005
[35]

Angelo Fichera. 2024. Fact-Checking the Immigration Chart That Trump Says ‘Saved My Life’. Available: https://www.nytimes.com/2024/07/19/us/politics/trump-immigration-chart-fact-check.html

2024
[36]

Donna Haraway. 1988. Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies 14, 3 (1988), 575–599. http://www.jstor.org/stable/3178066

1988
[37]

Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smit h-Loud. 2020. To- wards a critical race methodology in algorithmic fairness. In Proceedings of the 2020 conference on fairness, accountability, and trans parency. Association for Computing Machinery, New York, NY, USA, 501–512

2020
[38]

Helen Ai He, Jagoda Walny, Sonja Thoma, Sheelagh Carpen dale, and Wes- ley Willett. 2024. Enthusiastic and Grounded, Avoidant and Cautious: Un- derstanding Public Receptivity to Data and Visualizations . IEEE Trans- actions on Visualization and Computer Graphics 30, 1 (2024), ...

2024
[39]

Donna Haraway. 1998. Simians, cyborgs, and women: the reinvention of nature (reprinted ed.). FAB, Free Association Books, London

1998
[40]

Os Keyes. 2018. The Misgendering Machines: Trans/HCI I mplications of Auto- matic Gender Recognition. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 88 (Nov. 2018), 22 pages. https://doi.org/10.1145/327435 7

2018 doi
[41]

Nina G Jablonski. 2021. Skin Color and Race. American Journal of Physical Anthropology 175, 2 (2021), 437–447. https://doi.org/10.1002/ajpa.24 200

2021 doi
[42]

Elsie Lee-Robbins and Eytan Adar. 2022. Aﬀective Learn ing Objectives for Com- municative Visualizations. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 1–11. https://doi.org/10.1109/TVCG.2022.3 209500

2022 doi
[44]

Sebastian Linxen, Christian Sturm, Florian Brühlmann , Vincent Cassau, Klaus Opwis, and Katharina Reinecke. 2021. How WEIRD is CHI?. In Proceedings of the 2021 CHI Conference on Human Factors in Computing System s (Yokohama, Japan) (CHI ’21). Association for Computing Machiner...

2021 doi
[45]

Liang, Sean A

Calvin A. Liang, Sean A. Munson, and Julie A. Kientz. 202 1. Embracing Four Ten- sions in Human-Computer Interaction Research with Marginalized People. ACM Transactions on Computer-Human Interaction (TOCHI) 28, 2, Article 14 (2021), 47 pages. https://doi.org/10.1145/3443686

2021 doi
[46]

Maxim Lisnic, Cole Polychronis, Alexander Lex, and Mar ina Kogan. 2023. Mis- leading Beyond Visual Tricks: How People Actually Lie with C harts. In Proceed- ings of the 2023 CHI Conference on Human Factors in Computing Systems (Ham- burg, Germany) (CHI ’23). Association for Co...

2023 doi
[47]

Y eah, this graph doesn’t show that

Maxim Lisnic, Alexander Lex, and Marina Kogan. 2024. "Y eah, this graph doesn’t show that": Analysis of Online Engagement with Misleading Data Visualizations. In Proceedings of the CHI Conference on Human Factors in Computi ng Systems (Honolulu, HI, USA) (CHI ’24). Association...

2024
[48]

Ulises A Mejias and Nick Couldry. 2019. Dataﬁcation. Internet policy review 8, 4 (2019), 1–10

2019
[49]

Linda McDowell. 1997. ‘Doing Gender: Feminism, Femini sts and Research Meth- ods in Human Geography’. In Space, Gender, Knowledge: Feminist Readings. Rout- ledge. Num Pages: 10

1997
[50]

Miriah Meyer and Jason Dykes. 2020. Criteria for Rigor i n Visualization Design Study. IEEE Transactions on Visualization and Computer Graphics 26, 1 (2020), 87–97. https://doi.org/10.1109/TVCG.2019.2934539

2020
[51]

Amanda Menking and Jon Rosenberg. 2021. WP: NOT, WP: NPO V, and other sto- ries Wikipedia tells us: A feminist critique of Wikipedia’s epistemology. Science, Technology, & Human Values 46, 3 (2021), 455–479

2021
[52]

Luiz Morais, Yvonne Jansen, Nazareno Andrade, and Pier re Dragicevic
[53]

Luiz Morais, Yvonne Jansen, Nazareno Andrade, and Pier re Dragicevic. 2020. Showing Data about People: A Design Space of Anthropographi cs. IEEE Transactions on Visualization and Computer Graphics 28, 3 (2020), 1661–1679. https://doi.org/10.1109/TVCG.2020.3023013

2020
[54]

Lisa Charlotte Muth. 2024. What to Consider When Choos- ing Colors for Race, Ethnicity, and World Regions. Availabl e: https://blog.datawrapper.de/colors-for-race-ethnici ty-world-regions/

2024
[55]

NPR. 2024. Next U.S. census will have new boxes for ’Midd le East- ern or North African, ’ ’Latino’. Retrieved September 12, 20 24 from https://www.npr.org/2024/03/28/1237218459/census-race-categories-ethnicity-middle-east-north-africa

2024
[56]

Tamara Munzner. 2009. A Nested Model for Visualization Design and Validation. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 921–928. https://doi.org/10.1109/TVCG.2009.111

2009 doi
[57]

January O’Connor, Mark Parman, Nicole Bowman, and Step hanie Evergreen
[58]

Paul Parsons. 2021. Understanding data visualization design practice. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 665–675. https://doi.org/10.1109/TVCG.2021.3114959

2021
[59]

Smith, Al exandra To, and Ken- taro Toyama

Ihudiya Finda Ogbonnaya-Ogburu, Angela D.R. Smith, Al exandra To, and Ken- taro Toyama. 2020. Critical Race Theory for HCI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New Y...

2020
[60]

Peck, Soﬁa E

Evan M. Peck, Soﬁa E. Ayuso, and Omar El-Etr. 2019. Data i s Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvani a. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scot- land UK) (CHI ’19). Association for Co...

2019
[61]

Journal of MultiDisciplinary Evaluation 19, 44 (2023), 62–79

Decolonizing data visualization: A history and futur e of Indigenous data visualization. Journal of MultiDisciplinary Evaluation 19, 44 (2023), 62–79

2023
[62]

Andre MN Renzaho. 2023. The lack of race and ethnicity da ta in Australia—a threat to achieving health equity. International Journal of Environmental Re- search and Public Health 20, 8 (2023), 5530

2023
[63]

Paul Parsons, Prakash Shukla, and Chorong Park. 2021. F ixation and Creativity in Data Visualization Design: Experiences and Perspective s of Practitioners. In 2021 IEEE Visualization Conference (VIS) . IEEE Computer Society, IEEE, 76–80

2021
[64]

Jonathan Schwabish and Alice Feng. 2021. Do No Harm Guid e: Ap- plying Equity Awareness in Data Visualization. Urban Insti tute. https://www.urban.org/research/publication/do-no-ha rm-guide-applying-equity-awareness-data-visualiza

2021
[65]

Pine and Max Liboiron

Kathleen H. Pine and Max Liboiron. 2015. The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Hu- man Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15) . Association for Computing Machinery, New York, NY, USA, 314 7–3156. ...

2015
[66]

Haimson, and Danielle Lottridge

Katta Spiel, Oliver L. Haimson, and Danielle Lottridge . 2019. How to do better with gender on surveys: a guide for HCI researchers. Interactions 26, 4 (June 2019), 62–65. https://doi.org/10.1145/3338283

2019 doi
[67]

Morgan Klaus Scheuerman, Alex Hanna, and Emily Denton. 2021. Do Datasets Have Politics? Disciplinary Values in Computer Vision Data set Development. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–37

2021
[68]

United States Equal Employment Opportunity Commissio n. 2023. Who is pro- tected from employment discrimination? Retrieved Septemb er 12, 2024 from https://perma.cc/5XAQ-9763

2023
[69]

Hannah Schwan, Jonas Arndt, and Marian Dörk. 2022. Disc losure as a critical- feminist design practice for Web-based data stories. First Monday 27, 11 (Nov. 2022), 27 pages. https://doi.org/10.5210/fm.v27i11.127 12

2022 doi
[70]

Yixuan Zhang, Yifan Sun, Joseph D Gaggiano, Neha Kumar, Clio Andris, and Andrea G Parker. 2022. Visualization design practices in a c risis: Behind the scenes with COVID-19 dashboard creators. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 1037–1047

2022
[71]

Natkamon Tovanich, Pierre Dragicevic, and Petra Isenb erg. 2022. Gender in 30 Years of IEEE Visualization. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2022), 497–507. https://doi.org/10.1109/TVCG.202 1.3114787

2022 doi
[73]

Langdon Winner. 1980. Do Artifacts Have Politics? Daedalus 109, 1 (1980), 121– 136

1980
[75]

Karen Zraick, Allison McCann, Sarah Almukhtar, Yuliya Parshina- kottas, Robert Gebeloﬀ, and Denise Lu. 2024. No Box to Check: When the Census Doesn’t Reﬂect You. Available: https://www.nytimes.com/interactive/2024/02/25/us/census-race-ethnicity-middle-east-north-africa.html A ...

2024
[2021]

In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21)

Can Anthropographics Promote Prosociality? A Review and Large- Sample Study. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 611, 18 p ages. https://do...

2021
[2023]

In Proceedings of the 2023 CHI Conference on Human Factors in Com puting Systems (Hamburg, Germany) (CHI ’23)

Troubling collaboration: Matters of care for visuali zation design study. In Proceedings of the 2023 CHI Conference on Human Factors in Com puting Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, 14 pages. https://doi.org/10.1145/35...

2023 doi

Pith tools

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

[1] [1]

Philip E Agre. 1995. Institutional circuitry: Thinking about the forms and uses of information. Information Technology and Libraries 14, 4 (1995), 225–230

work page 1995

[2] [2]

Philip E Agre. 2014. Toward a critical technical practic e: Lessons learned in trying to reform AI. In Social science, technical systems, and cooperative work . Psychology Press, 131–157

work page 2014

[3] [3]

Derya Akbaba, Lauren Klein, and Miriah Meyer. 2024. Enta nglements for Visualization: Changing Research Outcomes through Femini st Theory. IEEE Transactions on Visualization and Computer Graphics 31, 1 (2024), 11 pages. https://doi.org/10.1109/TVCG.2024.3456171

arXiv 2024

[4] [4]

Derya Akbaba, Devin Lange, Michael Correll, Alexander L ex, and Miriah Meyer

work page

[5] [5]

McKane Andrus, Elena Spitzer, Jeﬀrey Brown, and Alice Xi ang. 2021. What We Can’t Measure, We Can’t Understand: Challenges to Demogr aphic Data Procurement in the Pursuit of Fairness. In Proceedings of the 2021 ACM Con- ference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York,...

work page arXiv 2021

[6] [6]

McKane Andrus and Sarah Villeneuve. 2022. Demographic- Reliant Algo- rithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 17...

arXiv 2022

[7] [7]

Foster Osei Baah, Anne M Teitelman, and Barbara Riegel. 2 019. Marginaliza- tion: Conceptualizing Patient Vulnerabilities in the Fram ework of Social Deter- minants of Health—An integrative Review. Nursing Inquiry 26, 1 (2019), e12268. https://doi.org/10.1111/nin.12268

work page doi:10.1111/nin.12268 2019

[8] [8]

Bako, Xinyi Liu, Leilani Battle, and Zhicheng L iu

Hannah K. Bako, Xinyi Liu, Leilani Battle, and Zhicheng L iu. 2023. Under- standing how Designers Find and Use Data Visualization Exam ples. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2023), 1048–1058. https://doi.org/10.1109/TVCG.2022.3209490

arXiv 2023