Approaching the integration of large language models in the parliamentary workspace

Abstract

The integration of large language models (LLMs) into parliamentary workspaces offers transformative potential but also significant challenges. This contribution provides a structured analysis of LLM adoption in legislative environments, examining theoretical foundations, identifying implementation options, and assessing broader implications. It situates LLMs within the wider AI landscape in parliamentary processes, exploring their unique characteristics and applicability. A comprehensive SWOT analysis evaluates strengths, weaknesses, opportunities, and threats, highlighting potential efficiency gains in legislative drafting and policy analysis, alongside risks such as bias, misinformation, political manipulation, and ethical and regulatory challenges. Key integration options are examined, including external LLM service providers, in-house deployment, and training models on parliamentary data for enhanced relevance and accuracy. Performance benchmarks tailored to parliamentary needs are also discussed. The paper emphasizes the importance of LLM literacy and capacity building among MPs, parliamentary staff, and scientific services for responsible and effective use. Finally, it reflects on the implications of LLM use in parliaments and offers recommendations for actions, stressing the need for a balanced approach that leverages technological innovation while ensuring democratic integrity, transparency, and accountability. By providing a comprehensive overview, this contribution advances the discourse on AI in parliament and offers strategic recommendations for integrating LLMs into parliamentary operations effectively and responsibly.

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