Interrogating human-centered data science:
Taking stock of opportunities and limitations

Introduction

In the Interrogating Human-Centered Data Science workshop at CHI'22, we seek to take stock of the goal, broadly conceived, to humanize data science. Data science has become an important topic for the CHI conference and community, as shown by many papers and a series of workshops. Previous workshops have taken a critical view of data science from an HCI perspective, working toward a more human–centered treatment of the work of data science and the people who perform the many activities of data science. However, those approaches have not thoroughly examined their own grounds of criticism.

In this remote workshop, we deepen that critical view by turning a reflective lens on the HCI work itself that addresses data science. We invite new perspectives from the diverse research and practice traditions in the broader CHI community, and we hope to co-create a new research agenda that addresses both data science and human-centered approaches to data. We invite participation from individuals who have been involved in reading, writing, teaching, designing, and organizing with an aim to problematize data-intensive technologies and methods, and propose solutions to those problems. Overarching questions of the workshop include:

• What successes have been won in humanizing data science, and what challenges thus far have proved intractable?

• What are the strengths of a human-centered lens for interrogating data science, and what possibilities does it obscure or preclude?

• What fresh or overlooked theories, ideas, and methods can complement human-centered approaches or further the cause of making data science more ethical, responsible, equitable, and beneficial?

Our intent is to push against the current boundaries of human-centered data science and establish an agenda for furthering this area of work. We plan to use this workshop as a launchpad to develop a slate of journal articles for a special issue or to seed one or more funding proposals for innovative research.

Organizers

Anissa Tanweer (primary contact), University of Washington, eScience Institute, US. Anissa Tanweer is a research scientist at the University of Washington’s eScience Institute focused on human-centered data science. She conducts ethnographic research on the practice and culture of data science, and brings a sociotechnical lens to bear on the design and implementation of data science training programs. She serves as Program Chair for the UW Data Science for Social Good internship and Associate Editor for the newly launched Data Analytics for Social Impact section of Frontiers in Big Data.

Cecilia Aragon, University of Washington, Department of Human Centered Design and Engineering, US. Cecilia Aragon is a Professor in the Department of Human Centered Design Engineering and Director of the Human-Centered Data Science Lab at the University of Washington. Her research focuses on enabling humans to explore and gain insight from vast data sets. In 2008, she received the Presidential Early Career Award for Scientists and Engineers (PECASE). She is co-author of the book, Human-Centered Data Science: An Introduction, forthcoming in 2022 from MIT Press [2].

Michael Muller, IBM Research, Human Centered AI, US. Michael Muller studies work-practices of data science workers at IBM Research (Cambridge MA USA). With colleagues, he has analyzed how humans intervene (individually and collaboratively) between "the data" and "the model" as aspects of responsible and accountable data science work. He is co-author of the book, Human Centered Data Science: An Introduction, forthcoming in 2022 from MIT Press [2].

Shion Guha, University of Toronto, Faculty of Information, Canada. Shion Guha studies algorithms in the public sector with particular focus on child welfare and criminal justice systems. He is interested in the intersections of computing and critical methodologies. He is co-author of the book, Human-Centered Data Science: An Introduction, forthcoming in 2022 from MIT Press [2].

Samir Passi. Samir Passi studies the sociotechnical challenges with the development and use of Responsible AI systems at Microsoft, where he works as a Researcher with the AI, Ethics and Effects in Engineering and Research (Aether) initiative. He is particularly interested in mapping the relation between the human and organizational work involved in building AI systems and the social and normative implications emanating from the use of those systems.

Gina Neff, University of Cambridge, Minderoo Centre for Technology & Democracy; and University of Oxford, Oxford Internet Institute & Department of Sociology, UK. Gina Neff studies the effects of the rapid expansion of our digital information environment on workers, workplaces and in our everyday lives. Her books include Venture Labor [30], Self-Tracking [31] and Human-Centered Data Science: An Introduction [2]. She also led the team that won the 2021 Webby Award for the best educational website on the Internet, for the A to Z of AI [19], which has reached over 1 million people in 17 different languages.

Marina Kogan, University of Utah, US. Marina Kogan is an Assistant Professor in the School of Computing at the University of Utah. Her research interests are in Crisis Informatics, Social Computing, and Network Science. Her research centers around social group formation, dynamics, and cooperative work. More specifically, she focuses on the cooperative activities and the resulting large-scale social dynamics that emerge on social media in disruption events, such as disasters arising from natural hazards, collective action around political crises, and community disruptions. She is co-author of the book, Human-Centered Data Science: An Introduction, forthcoming in 2022 from MIT Press [2].