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A. Ethics and Best Practices
When collecting and using data, especially data on minorities, researchers should be aware of possible biases. These biases often result from a power imbalance between those who collect data and the minority groups the data is about and failure to understand and include the groups’ perspective in the research. This has ethical implications such as skewed or one-sided reporting, resulting in decision-making that does not serve those minority groups.
Bias and ethics should be considered at all stages of collecting and using data. Researchers should be aware of who funded and collected the data and whether the groups in focus were included in designing the study.
Generally, when reusing data, researchers should be aware of their data source’s terms of use. They should also follow best practices for personally identifiable information, including deidentification, anonymization, or restricted access for sensitive datasets, and data security. They should also be mindful of institutional IRB requirements and principles of benefits, risks and harm, and laws and regulations on data privacy and data collection and processing, such as the General Data Protection Regulations. Additionally, when generating data, we recommend that data is separated from reports, in order to make it more findable. The below resources offer guidance and background information on ethics and best practices for all stages of research, from collecting, analyzing, using and disseminating data.
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Chicago Beyond, Why am I always being researched? A guidebook for community organizations, researchers, and funders to help us get from insufficient understanding to more authentic truth (PDF)
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Data Ethics Framework , UK Central Digital and Data Office
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University of Minnesota Libraries’ Conducting research through an anti-racism lens
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University of Pennsylvania’s A Toolkit for Centering Racial Equity Throughout Data Integration (PDF)
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University of North Carolina’s Data to Study Racial Inequity: Put the Data in Context
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Tableau’s Do No Harm Guide
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National Neighborhood Indicators Partnership, Furthering DEI through Data Products and Analytic Services
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Data Equity Framework . We All Count (Helen Krause, Founder)
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Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact . Calif. L. Rev., 104, 671.
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Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., Jagadish, H. V., Unsworth, K., Sahuguet, A., Venkatasubramanian, S., Wilson, C., Yu, C., & Zevenbergen, B. (2017). Principles for accountable algorithms and a social impact statement for algorithms . FAT/ML.
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Holland, S., Hosny, A., Newman, S., Joseph, J., Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards . MIT Media Lab and the Berkman Klein Center
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Mark Salling, Greg Babinski, and Nicole Franklin, “The Role of the GIS Professional in Issues of Equity and Social Justice (PDF)”, The GIS Professional 287 (January/February 2019): 1-5.
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