UK Government AI for Science Strategy and SSI Responsible AI in RSE study group

Today, the UK government announced its AI for Science Strategy, setting out actions to ensure the UK’s scientific ecosystem not only adapts to, but benefits from the AI for science revolution. The strategy focuses on developing a data landscape that facilitates transformative research, ensuring researchers have access to large-scale compute, building interdisciplinary research communities, and capitalising on rapid advances in autonomous labs and both general-purpose and specialist AI tools.

The strategy has two core objectives:

Develop frontier capability in AI-driven science: as companies and researchers build powerful AI science tools and autonomous lab systems, the UK aims to strengthen its capacity in these strategically vital areas.
Maintain the UK’s global scientific leadership: with AI reshaping research worldwide, the UK intends to adapt early, create growth and capture the benefits for public good.

The strategy builds on January’s AI Opportunities Action Plan and is backed by a government commitment of £2 billion between 2026 and 2030, including up to £137 million specifically for AI for science. Aligned with wider national initiatives, it targets frontier sectors such as advanced materials, nuclear fusion, medical research, engineering biology and quantum technology. Officials say the strategy signals the UK’s intent not only to keep pace with global advances in AI-enabled science, but to help shape its future direction.

Read the full AI for Science Strategy

Responsible AI in RSE

Discussions around the integration of AI into research practices focus on authorship, review, and hypothesis generation. However, community led discussions around the adoption of Generative AI into Research Software Engineering remain much needed.

The SSI is pleased to announce a new study group: Responsible AI in RSE. This group will explore how and where AI can be responsibly integrated into the creation of research software, while identifying the opportunities and risks posed by its adoption. Some potential themes include:

Reproducibility and transparency software developed with AI tools,
Environmental impact of AI-generated research code,
Equality, diversity and inclusion (EDI) implications,
Epistemic risks associated with AI adoption,
AI and research accessibility.

Our goal is to co-develop a community consensus position statement on Generative AI policy for RSE through open discussion, shared resources, and collaborative writing.

More information about this initiative will be published soon.