Automated Review and Correction of Legal Case Summaries

• Day: 2025-11-27
• Time: 22:30 to 23:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Automation, Data Correction, Ai Improvements, Dataframe Manipulation

Description

Session Goal

The session aimed to automate the review and correction of legal case summaries, focusing on data extraction and error reduction.

Key Activities

• Developed a systematic approach for reviewing and correcting legal case summaries, including specific verdicts and detected problems.
• Proposed a JSON format for corrected data and established rules for automating the correction process.
• Addressed errors in the automatic ingestion of data for three specific cases, proposing both automatic corrections and necessary human reviews.
• Implemented improvements to AI prompts and schemas to reduce data extraction errors, including clarifying terms, adding confidence indicators, and normalizing formats.
• Expanded and normalized the person_core list in DataFrames using pandas, ensuring identifiers are repeated for each entry.
• Created a function to flatten nested DataFrames and save them as CSV files, handling both dictionary and JSON string formats.

Achievements

• Successfully outlined a workflow for legal case data correction and ingestion improvement.
• Enhanced AI data extraction processes with improved prompts and schemas.
• Developed robust methods for DataFrame manipulation and CSV export.

Pending Tasks

• Further testing and validation of the automated correction rules and AI prompt improvements.
• Integration of the new DataFrame functions into existing data processing pipelines.

Evidence

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• event_ids: []