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ABSTRACT Artificial Intelligence (AI) methods are valued for their ability to predict outcomes from dynamically complex data. Despite this virtue, AI is widely criticized as a “black box” i.e., lacking mechanistic explanations to accompany predictions. We introduce a novel interdisciplinary approach that balances the predictive power of data-driven methods with theory-driven explanatory power by presenting a shared use case from four disciplinary perspectives. The use case examines scientific career trajectories through temporally complex, heterogeneous bibliographic big data. Topics addressed include: data representation in complex problems, trade-offs between theoretical, hypothesis driven, and data-driven approaches, AI trustworthiness, model fairness, algorithm explainability and AI adoption/usability. Panelists and audience members will be prompted to discuss the value of approach presented versus other ways to address the challenges raised by the panel, and to consider their limitations and remaining challenges.
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ADOPTION OF ARTIFICIAL INTELLIGENCE BY ELECTRIC UTILITIES
Adopting Artificial Intelligence (AI) in electric utilities signifies vast, yet largely untapped potential for accelerating a clean energy transition. This requires tackling complex challenges such as trustworthiness, explainability, pri- vacy, cybersecurity, and governance, balancing these against AI’s benefits. This article aims to facilitate dialogue among regulators, policymakers, utilities, and other stakeholders on navigating these complex issues, fostering a shared under- standing and approach to leveraging AI’s transformative power responsibly. The complex interplay of state and federal regulations necessitates careful coordina- tion, particularly as AI impacts energy markets and national security. Promoting data sharing with privacy and cybersecurity in mind is critical. The article advo- cates for ‘realistic open benchmarks’ to foster innovation without compromising confidentiality. Trustworthiness (the system’s ability to ensure reliability and per- formance, and to inspire confidence and transparency) and explainability (ensur- ing that AI decisions are understandable and accessible to a large diversity of par- ticipants) are fundamental for AI acceptance, necessitating transparent, accountable, and reliable systems. AI must be deployed in a way that helps keep the lights on. As AI becomes more involved in decision-making, we need to think about who’s responsible and what’s ethical. With the current state of the art, using generative AI for critical, near real-time decision-making should be approached carefully. While AI is advancing rapidly both in terms of technology and regula- tion, within and beyond the scope of energy specific applications, this article aims to provide timely insights and a common understanding of AI, its opportunities and challenges for electric utility use cases, and ultimately help advance its adop- tion in the power system sector, to accelerate the equitable clean energy transition.
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- Award ID(s):
- 2133284
- PAR ID:
- 10544704
- Editor(s):
- Reiter, Harvey L
- Publisher / Repository:
- Energy Bar Association
- Date Published:
- Journal Name:
- Energy law journal
- Volume:
- 45
- Issue:
- 1
- ISSN:
- 0270-9163
- Subject(s) / Keyword(s):
- Energy AI Privacy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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