Responsible AI in energy: Balancing efficiency, sustainability and trust in a high-stakes industry

Alyssa Lefaivre Škopac is head of global partnerships and growth and Patrick McAndrew is member engagement and community manager at the Responsible AI Institute.

As AI revolutionizes the energy industry, unlocking new opportunities for efficiency, sustainability and innovation, the need for responsible AI, or RAI, practices has never been more critical. The energy sector’s unique characteristics demand a tailored approach to implementing RAI.

AI in the energy sector: Balancing potential and risk

The energy industry is rapidly adopting AI across various use cases, with a market opportunity estimated at up to $13 billion. However, the sector faces challenges in balancing AI’s potential for optimizing operations with mitigating risks, given energy’s classification as critical infrastructure. Stringent regulations, safety considerations and environmental expectations necessitate robust risk management as AI adoption accelerates in this high-stakes domain.

The imperative for responsible AI in energy

The energy sector’s role in sustaining reliable energy provisions and safeguarding the nation’s well-being sets it apart when implementing AI responsibly. Energy companies grapple with large volumes of sensitive data from diverse sources, and data modernization efforts have lagged, hindering effective AI adoption. Additionally, stakeholders expect energy firms to leverage AI for streamlining operations and offsetting emissions.

Navigating the RAI landscape: Best practices for energy companies

To navigate this complex landscape, energy firms must adopt a comprehensive RAI strategy, adhering to emerging global standards and best practices, including:

Aligning with emerging AI regulation: Comply with President Biden’s executive order, which tasks the Department of Energy and the National Institute of Standards and Technology to establish rules and regulatory compliance for AI systems in critical infrastructure. Implement rigorous AI governance standards to ensure safe AI deployment.

Establishing cross-functional RAI governance: Gather teams from IT, data, legal and diversity/inclusion domains to drive RAI strategy at strategic and tactical levels.

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