A responsible approach to AI that ensures certain properties such as fairness, robustness, privacy respecting, and transparency are upheld during model development, deployment, and monitoring.
When developing AI systems for environmental sciences, we need to recognize that many of our data sources contain biases, errors, and other issues. By taking an ethical approach to AI, we can attempt to address these issues during model development. Otherwise, these issues can propagate downstream and potentially lead to unethical models (e.g., Facial recognition algorithms often perform poorly for individuals with darker skin complexions). The figure below (from McGovern et al. 2022) demonstrates how ethical issues can arise in environmental sciences.
Resources and References
- McGovern, I. Ebert-Uphoff, D.J. Gagne, A. Bostrom, Why we Need Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences, Environmental Data Science (2022), April 2022. https://doi.org/10.1017/eds.2022.5