IASSIST Quarterly is a peer-reviewed, indexed, open access quarterly publication of articles dealing with social science information and data services, including relevant societal, legal, and ethical issues.
ISSN: 2331-4141
Announcements
Final Call for Papers: Systemic Racism in Data Practices IASSIST Quarterly
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, anti-indigeneity, white supremacy, and racism against minoritized and marginalized communities in data, research, tools, and practices. Case studies, essays, book reviews, and articles will be considered.
Topics of this issue include but are not limited to:
- Implicit or explicit bias in AI and Machine Learning
- Data activism
- Anti-Indigeneity in big data
- Decolonizing data and data science
- Decolonizing scholarly data
- Bias in data collection practices
- Data and racial disparities in health sciences
- Race and precision medicine
- Racist practices in data reporting – Climate change, Covid-19, etc. reporting in marginalized communities
- Ethics of algorithm design
- Equity in data education – More inclusive (marginalized and underrepresented communities) -How do we improve this?
- Theories or suggestions on fair, ethical or trustworthy AI
Guidelines
Submissions should be received by August 31, 2021. This issue is co-edited by Jonathan Cain,
Columbia University, 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. If you have any questions or would like to pitch an idea of an article that you are working on and whether it would be a good fit for this special issue, please do not hesitate to contact both Jonathan Cain and Trevor Watkins: joc2122@columbia.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
