Week 8 - DB advanced topics & Ethical and Responsible Data Management
Learning objectives
Understand core ethical and responsible data management principles, particularly those related to self-governance, conflict management, mitigating bias and harm, and appropriately handling sensitive data.
Articulate these principles in the context of real-world scenarios relevant to environmental sciences research.
Slides
Suggested readings
Boté, J. J., & Térmens, M. (2019). Reusing data: Technical and ethical challenges. DESIDOC Journal of Library & Information Technology, 39(6) http://hdl.handle.net/2445/151341
McGovern, A., Ebert-Uphoff, I., Gagne, D., & Bostrom, A. (2022). Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science, 1, E6. https://doi.org/10.1017/eds.2022.5
Additional suggested readings are noted in the slides.
Case-based discussion:
- Case Study A: Containing the flames of bias in machine learning
- Case Study B: The caveat is the caviar: navigating ethics to protect endangered river wildlife
- Case Study C: To reuse or not reuse, that is the key question!
- Case Study D: Navigating the complexities of ownership zones
