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Keeping Track of AI Setbacks in Legal Tech: A Guide for Improvement

Keeping Track of AI Setbacks in Legal Tech: A Guide for Improvement

When developing solutions within Legal whilst making use of the latest AI tools then facing challenges is just part of the journey. These setbacks, however annoying they may seem, are full of learning opportunities. Just like evaluating pros and cons for different software libraries, noting down instances where AI fell short, and understanding the reasons behind these failures, sets a solid foundation for future success. Let's go over why it's beneficial to document AI missteps and a step-by-step guide on how to do it effectively.

The Value of Documenting AI Failures

Legal Tech AI projects are ambitious, aiming to revolutionise how legal professionals work all whilst using a brand new technology. However, the path is fraught with trials and error. Documenting these missteps is crucial for several reasons:

  1. Identifying Gaps: Understanding where AI falls short in legal applications helps pinpoint areas needing improvement or innovation.
  2. Progress Tracking: Keeping a record of failures and their contexts allows for a clear view of technological advancements over time.
  3. Tailored Learning: Notes on what didn't work and potential reasons why can guide focused learning and development in Legal Tech AI.

Revisiting your documented AI challenges when new features or capabilities are announced can shine a light on whether past issues can now be handled. This not only encourages continuous learning and adaptation but also ensures you're leveraging the latest advancements effectively.

How to Effectively Track AI Setbacks

Step 1: Establish a Documentation System

Choose a method for keeping records of AI challenges, whether it's a using OneNote, Notion, notes or a simple spreadsheet. The key is accessibility and ease of use, I won't judge you for continuing to live inside of Excel, much.

Step 2: Record Every Challenge

When an AI solution doesn't meet expectations, document:

  • Date
  • Project name
  • Objective
  • Description of the shortfall
  • Your thoughts on the cause
  • AI Tool

Examples:

Example 1: Inadequate Context Understanding in Contract Analysis

  • Date: 3rd March 2024
  • Project Name: AI-Powered Contract Review
  • Objective: Implement an AI to automate the review and analysis of complex legal contracts.
  • Shortfall: The AI struggled to understand the context when contracts referenced multiple documents or contained intricate legal language.
  • Thoughts on Cause: The AI’s context window was too limited to integrate the extensive background information needed for a comprehensive analysis.
  • AI Tool: Vendor A Tool
  • Date: 29th February 2024
  • Project Name: Legal Document Data Extraction
  • Objective: Use AI to extract specific data points from a variety of legal documents.
  • Shortfall: The AI failed to identify and extract all relevant information consistently across different document types.
  • Thoughts on Cause: The AI model lacked the necessary training on a diverse enough range of document formats and legal terminologies.
  • AI Tool: Vendor B Tool

Step 3: Analyse and Reflect

For each logged challenge, take the time to:

  • Consider different reasons for the failure
  • Think of alternative approaches
  • Reflect on lessons learned
  • Plan for potential retesting with future AI advancements

Step 4: Stay Informed on AI Developments

Regularly keep yourself updates on the latest changes in AI, especially as it pertains to Legal Tech. This will help you identify when a previously documented challenge may be worth re-evaluating.

Step 5: Schedule Regular Reviews

Set a routine, perhaps every month (given the pace of changes recently), to go through your documented AI challenges. See if:

  • New AI features or improvements could address these challenges.
  • Your understanding of the issue has evolved, offering new solutions.
  • Patterns in the failures suggest broader areas for improvement or learning.

Step 6: Try Again

When updates in AI technology suggest a past challenge may now be overcome, plan a follow-up test. Approach it with an open mind and the lessons learned from your previous experience.

Step 7: Update Your Records

After retesting, ensure your documentation reflects the new attempts, outcomes, and insights, keeping your learning journey up-to-date.

and so...

Documenting AI challenges in Legal Tech is more than a practice in diligence—it's a strategy for continuous improvement and innovation. By systematically tracking and revisiting these setbacks, you're not just learning from the past; you're preparing for a future where those very challenges become your next "over-night success".

and thanks...

Thanks to Kyle Bahr for their recommendation of including the AI Tool used in the list.

Thanks to Shreya Vajpei for their quick implementation of a public Notion to crowdsource these setbacks.