Machine Learning and Artificial Intelligence to Advance Earth System Science Opportunities and Challenges: Proceedings of a Workshop (2022) / Chapter Skim
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Challenges and Risks of Using ML/AI for Earth System Science
Challenges and Risks of Using ML/AI for Earth System Science
Pages 19-30
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From page 19... ...
Relatedly, as expertise at the intersection of ML/AI and Earth system science emerges, consideration may be required to equip the current workforce and the next generation of workers with new skill sets, and employers may need to build capacity to tackle emerging problems. In addition, as new ML/AI tools are developed, strategies may be needed to interface these new tools with existing Earth system science hardware, software, and data.
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Priya Donti, Carnegie Mellon University, added that the use of ML/AI can accelerate existing systems and can create feedback loops that exacerbate inequalities and ethical failures, and the use of ML/AI tools may obscure or obfuscate important value discussions and divorce the models from important underlying value judgments and human decisions. Danks agreed and added that the use of ML/AI can introduce new opportunities to answer scientific questions in areas of Earth system science where difficult discussions about how to handle ethical issues have not yet been had.
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From page 21... ...
Donti added that the ML community is accustomed to using clean benchmark data to do algorithmic innovation and will require a culture shift to focus on questions around imperfect Earth science and climate change–related data. While existing methods already deployed in Earth sciences such as interpolation and imputation could be used to address sparse data problems, Danks explained that assumptions in those methods would also require thought so as not to introduce new biases.
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From page 22... ...
For example, ensuring the safety and robustness of AI methods would be critical when optimizing electric power systems, and interpretability and auditability would be important in the context of high-stakes decisions about climate policy. Donti also explained how AI can be used in ways that directly facilitate climate change mitigation and adaptation strategies; however, AI can also be used in ways that accelerate or increase energy consumption and greenhouse gas emissions, and there is an energy cost associated with using computationally intensive AI models.
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From page 23... ...
ML/AI journals and conferences could require broader impact statements that would force scientists to consider the ethical impacts of the systems they are developing. Ebert-Uphoff added that journals and editors have an important role to play in setting standards for disclosure and transparency in algorithms and value decisions as part of the article submission and acceptance process.
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Gaps in Training and Opportunities for Continuing Education The panelists began by discussing gaps in education among students just entering the workforce. Hamed Alemohammad, Radiant Earth Foundation, echoed the gap discussed throughout the workshop between domain scientists who have only used ML tools "off the shelf" and computer scientists who are not knowledgeable about the Earth science domain, forcing employers to choose between the two in the hiring process.
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Panelists discussed existing continuing education resources as both experiential learning programs that are part of learning communities, as well as online courses or certificates that can be done independently (Box 4)
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While the volume of Earth science datasets is expected to grow dramatically, the current data infrastructure assumes scientists will download and analyze data on their local computers (Figure 9)
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ML/AI tools are exacerbating existing infrastructure challenges because researchers now want to download entire datasets to train their ML models. Jason Hickey, Google, outlined trends in Earth science technology including sustained investment in Earth observing systems and environmental data systems -- satellites, ground stations, airplanes, vehicles, buoys, etc.
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Gentemann explained that the objectives of the program are to increase understanding and adoption of open science, accelerate major scientific discoveries, and broaden participation by underrepresented communities. Privette shared the example of how ASDI is using cloud computing to democratize the ML/AI space by significantly reducing the cost, time, and technical barriers associated with analyzing large datasets to generate sustainability insights.
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Opportunities to Shift Culture Participants discussed existing barriers and strategies to overcome them to realize the open science future imagined by the panelists. Given the significant computing skills that ML/AI require, Dagon suggested creating and sharing analysis workflows for ML tasks using a set of best practices and examples, as well as developing benchmark datasets to facilitate wider application of ML/AI tools.
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Relatedly, with respect to the large amount of ML/AI courses and trainings available, Dagon identified the challenge to provide accessibility and entry points for both domain scientists and ML/AI experts. Abernathey suggested offering data engineering courses and degrees and working with industry to identify needed skills and training.
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