Data/Work in Crisis

This one-day workshop aims to map data and its inherent connections to work (of all kinds) across a landscape of ongoing crises. The workshop brings together researchers and practitioners with an interest in data work that underpins automation, algorithmic systems and organizational and societal strives toward datafication. The workshop provides a forum for interdisciplinary discussions around controversies related to data and work – and data work in particular – with the aim to expand the toolbox for working with data by proposing and developing critical approaches, drawing on the rich contributions of the growing body of literature on data work and datafication. Through spatial and temporal mapping exercises, the workshop intends to both trace paths through past crises into a contemporary moment, and towards more hopeful futures.

Illustrative controversies

Rather than defining specific conference themes, we offer here some illustrative controversies for opening up the conversation. The work of the workshop will be to expand and deepen this list and produce a generative mapping, along with thinking through what we can take from the past decade of scholarship and build upon.

Erosion of skill and the meaning of work

Working with an expansive definition of data work as “any human activity related to creating, collecting, managing, curating, analyzing, interpreting, and communicating data” [6], we are all becoming data workers. Data-driven moves to outsource and automate work commonly lead to the transformation of work rather than machines replacing human effort [19, 26]. This raises controversy over how we might (or fail to) protect values of skill, care, and experienced meaning in work. Within the HCI/CSCW literature, there are at least four meanings of data work that are of interest for this workshop: (1) data work in pre-existing work practice that is getting datafied (e.g. [14, 19]), (2) the data work that data scientists do (and would like to avoid doing) to clean and prepare data sets (e.g. [1, 4, 20, 21]), (3) data work that is outsourced to click workers (e.g. [9, 28], and (4) data work that is delegated to algorithms (e.g. [13]).

Invisibility of data/work and its emotional burdens

As a related controversy, narratives of automation and datafication risk losing sight of work practice and the emotional burdens it may involve, especially when it comes to data practices like content moderation or red-teaming. Data work is often invisible, uncompensated work that disproportionately burdens those low in organizational hierarchies [3, 19]. Data work tasks are assigned to workers at all levels without attention to the labor data work requires. In the wake of Big Data and AI, research on data work has paid disproportionate attention to the practices of data scientists and data analytics [24]. However, data work entails multiple forms of labor carried out by professionals and laypeople who do data work adjacent to their core work tasks and data work professionals alike [5].

Inequalities around data/work

There is a need to understand how power relations surrounding data work operate at both micro as well at macro levels of scale. Research shows there are differences in global experiences of data work [12, 18, 25, 27], with data work in the global south often following the historical fault lines of colonialism [23]. Thus, there is a pressing need for interdisciplinary scholarly attention to data work that attends to power at multiple levels of analysis, from the micro (individual, organizational) to the macro (global). Moreover, these inequalities are echoed in data and their production.Data often reflect social inequalities along historical, geographical and socio-economic lines. For instance, geographies of previously colonised lands may be valued less for their use than they are valued as sites for extraction [10], with datafication reflecting economic rather than social needs. The systems and logics of datafication are imposed even in humanitarian contexts [15] and at local levels, marginalised communities may have and be given less access to data and derive less value from its political power [11].

Submitting

The submission deadline is TBC.

Use this button to submit to the workshop.

Those interested in the workshop are invited to fill in the submission form, including a 250-300 word provocation on the state of crisis within data/work, participant's name, email address, affiliation and title, 50-100-word bio, and a list of three favourite books/papers/websites related to data/work. Submissions will be reviewed by the organisers and accepted based on the relevance and development of their chosen topic, as well as participants’ potential to contribute to the workshop.

Selection will rely on an inclusive model, where we will as organisers especially welcome work that represents a diverse community of scholarship and practice.

Notifications of acceptance will be sent by TBC.

Organisers

Naja Holten Møller explores how adaptive data-driven technologies introduce continual forms bureaucracies and public decision-, but also for citizens and others who engage with these work processes. In this context, her work raises questions about the role of professional discretion as automation is introduced to decision-making. She is an Associate Professor in Computer-Supported Cooperative Work at the University of Copenhagen.

Airi Lampinen studies algorithmic systems, with a particular focus on interpersonal and economic encounters. Her current research focuses on shared uses of intimate technology and the layered trust relationships prevalent in engagement with intimate digital health technologies. Lampinen is an Associate Professor in Human–Computer Interaction at Stockholm University, Sweden, and a Docent in Social Psychology at the University of Helsinki, Finland.

Rob Comber studies the social and environmental sustainability of civic technologies with local, national, and international civic organisations. Taking a critical perspective on what is asked of people in the work to make civic systems work, he has investigated questions on the political, emotional, legal, and recently (de)colonial dimensions of design. He is an Associate Professor in Communication at KTH Royal Institute of Technology, in Stockholm, Sweden.

Srrayva Chandhiramowuli examines the role of human values in data annotation and AI development. Her current research examines the work of data annotation for AI, paying particular attention to systemic challenges and frictions, to envision and inform just, equitable futures of AI. She is a PhD candidate in the Institute for Design Informatics at the University of Edinburgh.

Alex Taylor has been contributing to Science & Technology Studies and Human-Computer Interaction (HCI) for over twenty years. His interests are in how digital technologies are co-constitutive of forms of knowing and doing, and, as a consequence, provide a basis for fundamental transformations in society. He is a Reader at Design Informatics, University of Edinburgh.

Claus Bossen investigates the development and accommodation of IT in healthcare. Currently, he focuses on the different kinds of data work that emerges in connection with datafication of healthcare and efforts to become data-informed or -driven. This includes new data work tasks for healthcare professionals as well as new occupations such as clinical documentation integrity specialists. He is professor at Information Studies, Aarhus University.

Kathleen Pine studies data practices: the situated social, technical, and organizational practices through which data are created, managed, and deployed, as well as the social and organizational implications of digital information technologies in the realms of healthcare and community health. She is an Associate Professor in the College of Health Solutions at Arizona State University

Planning

Duration:

The workshop will be hosted in-person in Aarhus Denmark, on either August 18 or 19.

Activities:

The workshop is structured as a full-day, in-person event, centring on mapping the crises surrounding data/work using materials participants bring and present as a starting point. We will employ controversy mapping [16], a form of analysis that has been developed in the social sciences and has an established methodology for working on sociotechnical controversies in diverse groups and with creative resources [17]. A series of controversy mapping exercises will be geared towards community building and expanding the space in which to work critically on/with data, data work, and datafication.

Goals:

The key objective of this workshop is to bring together researchers within (and where possible beyond) the HCI/CSCW community with an interest in data/work, with the aims of sharing ongoing research, facilitating relationships around shared research interests, and collectively reflecting on the roles and contributions of scholars have made over the past decade and can hope to make in the decade to come. More concretely, we intend this workshop to deliver a set of literature recommendations on data/crisis and an articulation of grand challenges to pursue in the coming decade.

References

[1] Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, and Gina Neff. 2022 Human-centered data science: an introduction. MIT Press.

[2] Ruha Benjamin. 2022. Viral justice: How we grow the world we want. Princeton University Press.

[3] Pernille S Bertelsen, Claus Bossen, Casper Knudsen, and Asbjørn M Pedersen. 2024. Data work and practices in healthcare: A scoping review.International Journal of Medical Informatics 184 (2024), 105348.

[4] Jeremy P Birnholtz and Matthew J Bietz. 2003. Data at work: supporting sharing in science and engineering. In Proceedings of the 2003 ACM International Conference on Supporting Group Work. 339–348.

[5] Claus Bossen, Yunan Chen, and Kathleen H Pine. 2019. The emergence of new data work occupations in healthcare: the case of medical scribes. International journal of medical informatics 123 (2019), 76–83.

[6] Claus Bossen, Kathleen H Pine, Federico Cabitza, Gunnar Ellingsen, and Enrico Maria Piras. 2019. Data work in healthcare: An Introduction. Health Informatics Journal 25, 3 (Sept. 2019), 465–474. doi:10.1177/1460458219864730 Publisher: SAGE Publications Ltd.

[7] Simone Browne. 2015. Dark matters: On the surveillance of blackness. Duke University Press.

[8] Maciej Cegłowski. 2015. Haunted by Data. https://idlewords.com/talks/haunted_by_data.htm Accessed: 2025-03-05.

[9] Srravya Chandhiramowuli, Alex S. Taylor, Sara Heitlinger, and Ding Wang. 2024. Making Data Work Count. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 90 (April 2024), 26 pages. doi:10.1145/3637367

[10] Rob Comber and Elina Eriksson. 2023. Computing as Ecocide. In Computing within Limits. LIMITS. doi:10.21428/bf6fb269.9fcdd0c0 [11] Richard Heeks and Satyarupa Shekhar. 2019. Datafication, development and marginalised urban communities: an applied data justice frame- work. Information, Communication & Society 22, 7 (June 2019), 992–1011. doi:10.1080/1369118X.2019.1599039 Publisher: Routledge _eprint: https://doi.org/10.1080/1369118X.2019.1599039.

[12] Azra Ismail and Neha Kumar. 2018. Engaging solidarity in data collection practices for community health. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–24.

[13] Kristin Kaltenhäuser, Tijs Slaats, Michael Muller, and Naja Holten Møller. 2025. Beyond Accuracy: Rethinking Outlier Detection in Asylum Data. ACM J. Responsib. Comput. (Feb. 2025). doi:10.1145/3718986 Just Accepted.

[14] Kristian Helbo Kristiansen, Mathias A Valeur-Meller, Lynn Dombrowski, and Naja L Holten Moller. 2018. Accountability in the blue-collar data-driven workplace. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–12.

[15] Mirca Madianou. 2024. Technocolonialism: When Technology for Good is Harmful. Polity.

[16] Noortje Marres. 2015. Why map issues? On controversy analysis as a digital method. Science, Technology, & Human Values 40, 5 (2015), 655–686.

[17] Noortje Marres, Michael Castelle, Beatrice Gobbo, Chiara Poletti, and James Tripp. 2024. AI as super-controversy: Eliciting AI and society controversies with an extended expert community in the UK. Big Data & Society 11, 2 (2024), 20539517241255103.

[18] Milagros Miceli and Julian Posada. 2022. The data-production dispositif. Proceedings of the ACM on human-computer interaction 6, CSCW2 (2022), 1–37.

[19] Naja L. Holten Møller. 2018. The future of clerical work is precarious. Interactions 25, 4 (June 2018), 75–77. doi:10.1145/3231028

[20] Andrew B Neang, Will Sutherland, Michael W Beach, and Charlotte P Lee. 2021. Data integration as coordination: The articulation of data work in an ocean science collaboration. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (2021), 1–25.

[21] Gina Neff, Anissa Tanweer, Brittany Fiore-Gartland, and Laura Osburn. 2017. Critique and contribute: A practice-based framework for improving critical data studies and data science. Big data 5, 2 (2017), 85–97.

[22] Cathy O’neil. 2017. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. [23] Julian Posada. 2021. The coloniality of data work in Latin America. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 277–278.

[24] Annabel Rothschild, Amanda Meng, Carl DiSalvo, Britney Johnson, Ben Rydal Shapiro, and Betsy DiSalvo. 2022. Interrogating data work as a community of practice. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–28.

[25] Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. 2021. Re-imagining algorithmic fairness in india and beyond. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 315–328.

[26] Angelika Strohmayer and Michael Muller. 2023. Data-ing and Un-Data-ing. Interactions 30, 3 (2023), 38–42.

[27] Paola Tubaro and Antonio A Casilli. 2024. Who bears the burden of a pandemic? COVID-19 and the transfer of risk to digital platform workers. American Behavioral Scientist 68, 8 (2024), 961–982.

[28] Ben Zhang, Tianling Yang, Milagros Miceli, Oliver Haimson, and Michaelanne Thomas. 2025. The Making of Performative Accuracy in AI Training: Precision Labor and Its Consequences. (2025)