Fines & Fees Data Researcher

The Fines & Fees Data Researcher is a product vision developed by Stanford Legal Design Lab and Legal Aid Services of Oklahoma (LASO) to streamline the process of gathering comprehensive court debt records for clients seeking hardship waivers. This tool automates data collection from key state and county databases, identifies outstanding fines, categorizes fee types, tracks payment history, and presents the findings in a structured format. It is designed to reduce the burden on legal teams, enhance accuracy, and improve case strategy by surfacing all relevant financial obligations across jurisdictions. The agent also flags uncertainties, such as data gaps or common name conflicts, and provides transparent, verifiable output for legal review.

Rubric: What Good vs. Bad Performance Looks Like

Signs of a Good Job:

• Retrieves all court records with relevant fee and payment data from authorized sources.
• Accurately categorizes fees by type and county.
• Includes a complete payment history and notes on current balances.
• Clearly flags any uncertainty or ambiguous data.
• Handles common name errors, aliases, and old formats.
• Produces transparent, annotated reports that follow current legal workflows.
• Offers links to source materials and explains calculations for legal review.

Signs of a Bad Job:

• Misses relevant data or retrieves incorrect records.
• Fails to distinguish between individuals with similar names.
• Incorrectly totals fees or omits historical payments.
• Does not flag missing or ambiguous data.
• Provides incomplete or confusing output that requires significant manual review.

Workflow Phases

Phase 1: Intake

• Legal staff enters identifiers (name, DOB, aliases, case numbers).
• Agent verifies access to all approved databases.
• Initiates search on OSCN and ODCR as needed.

Phase 2: Data Extraction

• Collects:
  • Fees assessed (e.g., supervision, restitution).
  • Payment history.
  • Hearing dates.
  • Warrants or summons status.
• Notes gaps (e.g., pre-2005 cases) and flags for follow-up.

Phase 3: Analysis and Categorization

• Cross-checks data across sources.
• Flags inconsistencies or likely edge cases (misspellings, ambiguous acronyms).
• Applies confidence scores and prepares notes for human review.

Phase 4: Reporting

• Generates summary and detailed reports.
  • Fee breakdown by county and case.
  • Hyperlinks to original source documents and payments.
  • Structured output (tables, bullet points) aligned with LASO workflow.
• Provides documentation of agent’s process and reasoning.
• Flags issues that need legal follow-up.

Status and Next Steps

The Fines & Fees Data Researcher is entering the prototyping stage. Following initial design research with LASO legal staff, the project team has identified core technical requirements and mapped the existing manual workflow used to gather fine and fee data. These findings have informed a phased development plan focused on replicating and enhancing key steps through automation while embedding human review checkpoints throughout the workflow.

The next phase of development involves building data connectors to related databases and the organizations that run them. Ensuring responsible and secure access to sensitive information is a top priority.

To ensure quality and build trust in the tool’s output, the prototype will be deployed as part of a human-AI team model. Legal staff at LASO will continue to supervise each case, using the tool’s summaries and documentation to verify results and make legal decisions. The agent will provide not only raw data but also detailed annotations, confidence scores, and links back to original records. These features are designed to support expert oversight while reducing the research and analysis burden on staff. Pilot testing will allow the team to evaluate the tool’s reliability, improve its performance on ambiguous or edge cases, and iterate on interface design based on direct attorney and paralegal feedback.

Over the coming months, the team will evaluate the prototype against real case examples, compare results with manually collected data, and refine the rubric for successful performance. A broader goal is to develop a generalizable model for how AI agents can assist in data-intensive legal aid workflows—starting with fines and fees research, but potentially extending to other domains like benefits screening, criminal record expungement, or consumer debt analysis.