Capstone Project in AI and Law

Purpose

Provide learners with a hands-on opportunity to apply knowledge and skills from the course to a real-world legal challenge. Emphasis is on designing AI-enabled solutions while integrating ethical, technical, and regulatory considerations.

Course Objectives

The objectives of this course are to;

  1. Design an AI solution tailored to legal practice challenges.
  2. Integrate ethical, regulatory, and professional considerations into AI workflows.
  3. Demonstrate practical mastery of AI applications in legal contexts.
  4. Communicate technical solutions and insights effectively to legal and non-technical stakeholders.
Learning Outcomes

By the end of this course, a learner will be able to;

  1. Develop innovative AI-based solutions addressing real legal practice challenges.
  2. Critically evaluate AI tools for effectiveness, fairness, and compliance.
  3. Integrate ethical, regulatory, and professional considerations into AI solutions.
  4. Communicate AI applications clearly to diverse stakeholders, including lawyers, clients, and firm leadership.
  5. Reflect on lessons learned and propose next steps for scaling or improving AI solutions in practice.
Outline of Content
  1. Project Selection & Proposal
    • Identify a legal problem suitable for AI intervention.
    • Draft a project proposal outlining:
      • Problem statement
      • AI approach and methodology
      • Data sources (ensure anonymization and compliance)
      • Ethical and regulatory considerations
      • Expected outcomes and evaluation metrics
  2. Solution Design & Development
    • Develop the AI solution:
      • Predictive models (e.g., case outcomes, backlog forecasting)
      • Intelligent agents (e.g., legal chatbots, document triage)
      • Governance frameworks (AI policies, vendor evaluation protocols)
    • Apply principles learned in Modules 4-6: ethics, governance, compliance, and professional responsibility.
    • Document technical workflow, including algorithms, data preprocessing, and evaluation methods.
  3. Testing and Validation
    • Evaluate AI performance: accuracy, fairness, and reliability.
    • Conduct scenario testing to simulate real-world legal application.
    • Ensure outputs meet confidentiality and professional standards.
  4. Presentation & Stakeholder Communication
    • Prepare a comprehensive report and presentation:
      • Problem context and rationale for AI intervention
      • Solution architecture and methodology
      • Compliance, ethical, and governance measures
      • Results, limitations, and recommendations
    • Deliver a stakeholder-focused demo to illustrate solution value and usability.
Reference List
  1. Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press. DOI: https://doi.org/10.1017/9781316761380
  2. Susskind, R. (2019). Tomorrow’s Lawyers: An Introduction to Your Future (3rd Ed.). Oxford University Press. ISBN: 9780192864727 Book summary: https://youtu.be/I3nSSZPYIw0
  3. The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics (2022). Cambridge University Press. DOI: https://doi.org/10.1017/9781009072168
  4. Mak, V., Tjong Tjin Tai, E., & Berlee, A. (2020). Research Handbook in Data Science and Law. Edward Elgar Publishing.
  5. Warsaw (2025). AI in the Work of an Attorney-at-Law: Recommendations on how Attorneys-at-Law should use AI-Based Tools. 1st ed. Krajowa Izba Radców Prawnych, ul. Powązkowska 15, 01–797 Warszawa.
    https://kirp.pl/wp-content/uploads/2025/05/rekomendacje-ENG-NET.pdf
  6.  

Practical Tools and Online Platforms (for Labs/Capstone)

  1. Westlaw AI / Lexis+ AI – legal research automation.
  2. Casetext CoCounsel (GPT-4 powered) – memo drafting, contract review.
  3. Harvey AI – legal practice assistant (already in use by major firms).
  4. IronClad / Juro / Robin AI / Spellbook – contract lifecycle management.
  5. Luminance AI – document review.
  6. Reality Defender / Hive / Microsoft Video Authenticator – deepfake detection (for Module on evidence).
Policy and Regulatory Frameworks
  1. European Union Artificial Intelligence Act (2021–2025 drafts). Key regulation on AI risk categories and legal compliance.
  2. African Union (2022). Continental AI Strategy. Useful for contextualizing AI in African jurisdictions.
  3. Uganda’s Data Protection and Privacy Act (2019). Critical for confidentiality and evidence issues.
  4. General Data Protection Regulation (GDPR, EU 2018). The gold standard for privacy compliance, relevant to cross-border practice.
Request for more Information

For our Courses and Admission

Please enable JavaScript in your browser to complete this form.
Full Name
=

Template is not defined. Select an existing template or create a new one.
Template is not defined. Select an existing template or create a new one.
Template is not defined. Select an existing template or create a new one.