GA 5: Data Science in Indian Country
GA5 Conference “Data Science in Indian Country” brought together geoscientists, Native leaders, educators, and students for the purpose of elevating Native American voices in the geosciences, promoting participation, advancing research, and fostering collaboration. This conference focused on addressing the shift in science towards big data and how Native American perspectives can be incorporated in data collection and analysis of the Earth system. The conference led to knowledge-sharing on best practices for preparing students at all levels in using data in the geoscience.
Technological innovations allow large environmental datasets to be created and disseminated with unprecedented efficiency. These datasets can transform environmental decision-making through new kinds of quantitative methods and analyses, including machine learning and data visualization methods. Nevertheless, these datasets – and the analyses that they enable – pose ethical risks and tradeoffs, especially for Indigenous peoples. For example, datasets collected on Indigenous lands or from Indigenous knowledge-holders raise wide-ranging questions, including: What safeguards are in place to protect and steward culturally sensitive data and knowledge? What role does Traditional Ecological Knowledge (TEK) play in study design and data interpretation? How might environmental or cultural resources be imperiled by sharing their data with non-Native entities? How can aesthetic and spiritual values for clean water, air, and soils be quantified? These types of questions involve data sovereignty and may fall outside the scope of IRB approvals, regardless of whether such approvals are issued by academic or Tribal entities.
Other datasets may raise ethical concerns even if they are not specifically focused on Indigenous peoples and their territories. For example, widespread availability of standardized datasets (e.g., US Census data) can lead to analyses that – knowingly or not – contribute to the exclusion of Indigenous peoples from regulatory processes that affect their lands and communities (e.g., Emanuel, 2017). This type of exclusion raises concerns about participatory justice, a key area of environmental justice policy. Similarly, extensive and freely-available remote sensing datasets can be used to draw scientific or policy inferences about Indigenous lands and communities, sometimes without Tribal involvement in experimental design or interpretation of results. Given the widespread availability of big data and advanced analytical tools such as machine learning, the time is ripe for a GA conference that focuses on these emerging resources and some of the important ethical concerns that they raise for Indigenous peoples.
Location: University of Minnesota, Minneapolis
Hosted by the St. Anthony Falls Laboratory
Dates: July 28-30, 2022