Modification of the text of a law following the approval of an addition or amendment

Use case ID: 034

Author: Senate of Italy

Date: 12 June 2024  

Objective:

Generate the modified version of a legal text using generative AI following the application of an addition or amendment, producing a “side-by-side text” that adheres to the Senate’s drafting rules.

Actors:

• Senate users (e.g. documentalists and legislative drafters)

Prerequisites:

• Basic knowledge of computer tools by Senate users
• Knowledge of the drafting rules for “side-by-side texts” 

Scenario:

1. A Senate documentalist selects the original legislative text to be modified.
2. The documentalist applies the desired modification (addition or amendment) using a generative AI service.
3. The AI service processes the original text and the specified modification to produce the updated version of the legal text.
4. The documentalist compares the original text with the modified version to ensure the accuracy of the applied changes.
5. The AI service generates a visualization of the “side-by-side text”, displaying the original and modified versions according to the Senate’s precise drafting rules.
6. The documentalist reviews the “side-by-side text” for any discrepancies and finalizes the document.

Alternate flows:

• If multiple modifications affect the same section, the AI service prioritizes based on predefined rules or seeks user input for resolution.
• If the AI service has applied the modifications incorrectly, the documentalist can manually adjust the changes or reprocess the text using the AI service.

Expected results:

• The “side-by-side text” closely matches the manually created version, maintaining high fidelity to the Senate’s drafting standards.
• Efficiency in producing legally accurate and formatted texts is increased, with reduced manual effort.

Potential challenges:

• Ensuring high precision in the application of modifications to avoid errors in the legal text
• Resolving conflicts when multiple modifications affect the same section of the text, determining the correct order of application
• Managing iterative validation where user confirmation is required for each change, which can be time-consuming

Data requirements:

• The original legislative text and the specific modifications (additions or amendments) to be applied
• Historical “side-by-side texts” to train the AI model on the correct formatting and drafting rules

Integrations with other systems:

• Potential integration with legislative drafting tools and databases for seamless retrieval and application of modifications

Success metrics:

• Low rate of hallucinations (inaccuracies or fabrications by the AI)
• High level of matching to “side-by-side texts” created without generative AI
• User satisfaction scores from Senate documentalists and legislative drafters
• Reduction in the time required to produce modified legislative texts compared to traditional methods