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;
- Design an AI solution tailored to legal practice challenges.
- Integrate ethical, regulatory, and professional considerations into AI workflows.
- Demonstrate practical mastery of AI applications in legal contexts.
- 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;
- Develop innovative AI-based solutions addressing real legal practice challenges.
- Critically evaluate AI tools for effectiveness, fairness, and compliance.
- Integrate ethical, regulatory, and professional considerations into AI solutions.
- Communicate AI applications clearly to diverse stakeholders, including lawyers, clients, and firm leadership.
- Reflect on lessons learned and propose next steps for scaling or improving AI solutions in practice.
Outline of Content
- 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
- 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.
- Develop the AI solution:
- 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.
- 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.
- Prepare a comprehensive report and presentation:
Reference List
- 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
- Susskind, R. (2019). Tomorrow’s Lawyers: An Introduction to Your Future (3rd Ed.). Oxford University Press. ISBN: 9780192864727 Book summary: https://youtu.be/I3nSSZPYIw0
- The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics (2022). Cambridge University Press. DOI: https://doi.org/10.1017/9781009072168
- Mak, V., Tjong Tjin Tai, E., & Berlee, A. (2020). Research Handbook in Data Science and Law. Edward Elgar Publishing.
- 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
Practical Tools and Online Platforms (for Labs/Capstone)
- Westlaw AI / Lexis+ AI – legal research automation.
- Casetext CoCounsel (GPT-4 powered) – memo drafting, contract review.
- Harvey AI – legal practice assistant (already in use by major firms).
- IronClad / Juro / Robin AI / Spellbook – contract lifecycle management.
- Luminance AI – document review.
- Reality Defender / Hive / Microsoft Video Authenticator – deepfake detection (for Module on evidence).
Policy and Regulatory Frameworks
- European Union Artificial Intelligence Act (2021–2025 drafts). Key regulation on AI risk categories and legal compliance.
- African Union (2022). Continental AI Strategy. Useful for contextualizing AI in African jurisdictions.
- Uganda’s Data Protection and Privacy Act (2019). Critical for confidentiality and evidence issues.
- General Data Protection Regulation (GDPR, EU 2018). The gold standard for privacy compliance, relevant to cross-border practice.
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