ParliView uses a large language model (a type of artificial intelligence) to help you explore information from the European Parliament. This page explains how that process works, where it can fall short, and how you can get the most reliable results from the platform.
At a Glance
- AI-generated responses can contain errors, even when they read confidently and cite real sources.
- Always check the original parliamentary sources linked alongside each response.
- ParliView is a research tool, not an authoritative record of parliamentary proceedings.
- Treat AI responses as a starting point for exploring parliamentary information, not as a definitive conclusion.
1. How ParliView Generates Responses
When you ask ParliView a question, the system follows a two-step process:
- Retrieval: The system searches its database of official European Parliament documents, including plenary debates, committee reports, voting records, and parliamentary questions, to find material relevant to your query.
- Generation: A large language model reads the retrieved documents and composes a natural language response that draws on their content.
Each response includes links to the original European Parliament source documents so that you can verify the information directly. This approach, known as retrieval-augmented generation, is designed to ground the AI’s output in real parliamentary data rather than relying solely on patterns learned during training.
2. What Are AI Inaccuracies?
Large language models generate text by predicting the most likely next words in a sequence. They do not understand information the way a person does; they recognise and reproduce patterns in language. This fundamental difference means that AI systems can produce text that reads fluently, sounds authoritative, and yet contains factual errors.
The AI research community refers to these errors as hallucinations or confabulations. They are not bugs in any specific product; they are an inherent characteristic of how current language models work. All major AI systems exhibit this behaviour to some degree.
In practice, this means a response might:
- State something that sounds plausible but is factually incorrect
- Attribute a statement or vote to the wrong MEP
- Conflate details from two separate parliamentary proceedings
- Present a summary that subtly misrepresents the source material
3. Why Inaccuracies Can Occur in a Parliamentary Context
Parliamentary information poses particular challenges for AI systems:
- Volume and complexity: The European Parliament produces a vast quantity of procedural, legislative, and committee material. Similar debates, amendments, and votes occur across different sessions and policy areas. The AI may conflate related but distinct proceedings.
- Procedural detail: Specific figures, such as vote counts, amendment numbers, and committee assignments, are especially prone to small errors. The AI may generate a plausible number that does not match the actual record.
- Timeliness: There may be a delay between parliamentary activity and its availability in ParliView’s data sources. Responses may not reflect the very latest proceedings.
- Nuance and interpretation: Parliamentary debates often involve contested positions and fine distinctions. The AI may inadvertently present one interpretation of a debate as though it were settled fact, or oversimplify a nuanced position.
4. How to Verify What You Read
We strongly encourage you to treat ParliView as a tool for discovering and navigating parliamentary information, not as a substitute for reading primary sources. The following practices will help you get the most reliable results:
Verification checklist:
- Follow the source links. Every ParliView response includes links to the European Parliament documents it drew from. Use them to check the underlying data directly.
- Cross-reference key claims. If a response states a specific vote outcome, committee decision, or MEP position, verify it against the Parliament’s own published records.
- Be cautious with numbers and dates. Specific figures are where AI errors are most common. Double-check any quantitative claims before relying on them.
- Watch for confident but unsupported statements. If a response makes a strong claim without a corresponding source link, treat it with particular caution.
- Consider the scope of the question. Broad or ambiguous questions are more likely to produce responses that oversimplify or conflate topics. More specific queries tend to yield more reliable results.
5. What We Do to Improve Accuracy
The ParliView team takes several steps to reduce the likelihood and impact of AI errors:
- Retrieval-augmented generation: Rather than relying on the AI model’s training data alone, the system retrieves relevant documents from official European Parliament sources and uses them to ground each response.
- Source citations: Links to the original parliamentary documents are provided alongside every response, giving you a direct path to verify the information.
- Content policy filtering: Automated filters are applied to both queries and responses to help prevent off-topic, harmful, or misleading outputs.
- Ongoing evaluation: As part of the academic research behind ParliView, the team conducts systematic evaluation of response quality. This work informs continuous improvements to the system.
6. Further Reading
This research has been approved by the UCD Human Research Ethics Committee (Reference: 025-HS-26-C-Cross).
ParliView website: https://www.parliview.org