Track: Career Track
Is there a racial bias in citizen science and, if so, why does it persist? What are the consequences for data quality and for justice? How can we reduce the racial bias? Top-down, large-scale citizen science projects engage predominantly white participants. Grassroots, community-driven projects engage BIPOC. Citizen science activity is racially segregated across projects that differ by scale, goals, priorities, and power dynamics. Top-down, crowdsourcing projects with large numbers of volunteers, however, allow volunteers autonomy in selecting where and how frequently individuals collect data. Consequently, citizen science datasets can have significant spatial bias of data obtained from opportunistic and haphazard locations based on the preferences of the volunteers, and the bias is compounded by lack of standardized volunteer effort. Another common feature of volunteers in large-scale citizen science projects is that they are overwhelmingly white and affluent. Consequently, citizen science data has a racial-spatial bias….which results in data quality issues and environmental justice issues.
Presenting Author: Chris Hawn – University of Maryland
Presenting Author: Bryan I. Rodriguez – Latino Outdoors
Presenting Author: Na'Taki Osborne Jelks – Environmental and Health Sciences, Spelman College
Presenting Author: Caren Cooper – NC State University