CFP for Special Issue: Systemic Racism in Data Practices

2020-11-17

Systemic Racism in Data Practices

Inspired by the work of Black scholars, technologists, and activists including Dr. Safiya Noble,
Yeshimabeit Milner, and Joy Buolamwini, IASSIST Quarterly is publishing a special issue focusing on
systemic racist practices in data. We invite you to submit a proposal that discusses anti-Blackness, antiindigeneity, white supremacy, and racism against minoritized and marginalized communities in data,
research, tools, and practices. Case studies, essays, and articles will be considered.


Topics of this issue include but are not limited to:

• Implicit and explicit bias in AI and Machine Learning
• Data activism
• Anti-Blackness in big data
• Decolonizing data and data science
• Decolonizing scholarly data
• Bias in data collection practices
• White supremacy and data practices
• Data and racial disparities in health
• Race and precision medicine
• Racist practices in data reporting
• Ethics of algorithm design
• Equity in data education

 

Guidelines

Submissions should be received by April 2, 2021. This issue is co-edited by Jonathan Cain,
University of Oregon and Trevor Watkins, George Mason University. Authors should adhere to IASSIST
Quarterly instructions for authors and upload manuscripts according to the IASSIST Quarterly submission
guidelines. When submitting make a note that you are submitting for the special issue. Questions should
be directed to: jocain@uoregon.edu and twatkin8@gmu.edu.

IASSIST (International Association for Social Science Information Service and Technology) is
an international organization of professionals working with information technology and data
services to support research and teaching in the social sciences.

IASSIST Quarterly (iassistquarterly.com) is a peer-reviewed, indexed, open access quarterly
publication of articles dealing with social science information and data services. ISSN: 2331-
4141