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Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations ...

This paper describes work practices at a public, non-profit healthcare business intelligence unit involved in creating BI reports and sharing these with healthcare management and professionals. Based on ethnographic fieldwork, we detail the various work ...

Panelists outline three qualitative methodologies: stasis networks, interlocking surveillance, and rhetorical platform analysis. Each methodology guides researchers and practitioners to identify and resolve different types of issues with the ...

Managing error has become an increasingly central and contested arena within data science work. While recent scholarship in artificial intelligence and machine learning has focused on limiting and eliminating error, practitioners have long used error as ...

Critical refusal is an active process; an informed practice of investigating power differences in order to generate more just and equitable alternatives to the status quo. In this paper, we examine what it means to utilize critical refusal as a tool for ...

The application of artificial intelligence (AI) to the behavioral health domain has led to a growing interest in the use of machine learning (ML) techniques to identify patterns in people's personal data with the goal of detecting-and even predicting-...

The fields of Information and Communication Technologies for Development (ICT4D) and critical data studies have been historically distinct from each other, each bearing different histories and research foci. However, recent evolutions of ICT4D have ...

Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the ...

This workshop proposes a space for Latin American academics and activists engaging with data to think critically about the legitimacy and power dynamics of knowledge production. Given that most research on data, as well as its area of application, have ...

Information systems increasingly shape our knowledge of crises such as disasters and climate change. While these tools improve our capacity to understand, prepare for, and mitigate such challenges, critical questions are being raised about how their ...

This paper provides insight into the use of data tools in the American labor movement by analyzing the practices of staff employed by unions to organize alongside union members. We interviewed 23 field-level staff organizers about how they use data tools ...

There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the ...

The capture, aggregation, and analysis of student data is becoming ubiquitous at all levels of education—from primary to post‐secondary—as institutions increase their adoption of information technologies to serve administrative and educational ...

Meta/data are not neutral terms and mean various things for different communities and demographics. One such community that lacks much discussion about their ontologies of meta/data are the LGBTQ communities. With current practices of meta/data ...

This paper replicates, extends, and refutes conclusions made in a study published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify substantial levels of conflict between automated software agents (or bots) in Wikipedia using purely ...

My research examines new ways of approaching the contentious and uncertain knowledge politics surrounding natural disasters and climate change. Using three case studies, focused on different locations and type of hazards, I show how current information ...

The use of data and metrics on a professional and personal level has led to considerable discourse around the performative power and politics of 'big data' and data visualization, with academia being no exception. We have developed a university system, ...