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BachelorFaculty of Law and Ethics

AI Law and Regulations

Welcome to the future of AI and society! I am Prof. Dr. Louise Dupont. As a specialist in algorithmic liability, AI discrimination, and the regulatory frameworks being developed worldwide, such as the EU AI Act, I lead bachelor's students in the AI Law and Regulations program at Nexier University on their journey to shaping the rules of intelligent systems.

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Level
Bachelor
Learning model
Professor + Mentor
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NXAcademic
Edition
The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

01

Academic focus

Algorithmic liability, AI discrimination, and the regulatory frameworks being developed worldwide, such as the EU AI Act.

02

Practical focus

Algorithmic liability, AI discrimination, AI regulatory frameworks.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

  • Internships in legal tech firms or corporate legal departments focusing on AI

  • Roles as AI compliance analysts or policy researchers

  • Support roles in academic research projects on AI law

  • Opportunities in regulatory bodies or government agencies focused on AI

Career opportunities

  • AI Legal Analyst

  • AI Policy Advisor

  • Compliance Specialist (AI)

  • Digital Ethics Consultant

Jobs and projects

  • Analytical and ethical problem-solving for AI legal challenges

  • Legal reasoning and compliance expertise for AI systems

  • Policy analysis and development for emerging AI technologies

  • Strategic thinking for shaping just and ethical digital societies

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

  • What you gain

    • Mastering algorithmic liability and AI discrimination analysis.
    • Gaining expertise in understanding and applying AI regulatory frameworks.
    • Developing problem-solving abilities for real-world legal challenges posed by AI.
    • Cultivating an understanding of complex legal and technological problems.

  • Skills you build

    • Mastering the legal and ethical principles governing artificial intelligence.
    • Covering algorithmic liability, AI discrimination, and regulatory frameworks like the EU AI Act.
    • Excelling at identifying and mitigating AI discrimination and understanding AI's regulatory landscape.
    • Driving breakthroughs in AI regulatory frameworks and ethical AI governance.
Listed courses

Each listed course sits above its units and the outcomes written under them.

AI Law and Regulations

  1. 01Introduction to AI Law and Ethics
    1. FoundationsFoundations of Introduction to AI Law and Ethics

      The learner can master algorithmic liability and AI discrimination analysis, as applied to Introduction to AI Law and Ethics.

      The learner can gain expertise in understanding and apply AI regulatory frameworks, as applied to Introduction to AI Law and Ethics.

    2. MethodsMethods in Introduction to AI Law and Ethics

      The learner can develop problem-solving abilities for real-world legal challenges posed by AI, as applied to Introduction to AI Law and Ethics.

      The learner can cultivating an understanding of complex legal and technological problems, as applied to Introduction to AI Law and Ethics.

    3. ApplicationApplication of Introduction to AI Law and Ethics

      The learner can master the legal and ethical principles governing artificial intelligence, as applied to Introduction to AI Law and Ethics.

      The learner can covering algorithmic liability, AI discrimination, and regulatory frameworks like the EU AI Act, as applied to Introduction to AI Law and Ethics.

  2. 02Algorithmic Liability and Responsibility
    1. FoundationsFoundations of Algorithmic Liability and Responsibility

      The learner can excelling at identifying and mitigating AI discrimination and understand AI's regulatory landscape, as applied to Algorithmic Liability and Responsibility.

      The learner can driving breakthroughs in AI regulatory frameworks and ethical AI governance, as applied to Algorithmic Liability and Responsibility.

    2. MethodsMethods in Algorithmic Liability and Responsibility

      The learner can apply a method from Algorithmic Liability and Responsibility to a documented case.

      The learner can select an appropriate method from Algorithmic Liability and Responsibility for a stated problem.

    3. ApplicationApplication of Algorithmic Liability and Responsibility

      The learner can evaluate a practice of Algorithmic Liability and Responsibility against a stated criterion.

      The learner can transfer Algorithmic Liability and Responsibility to a new documented context.

  3. 03AI Discrimination: Detection and Mitigation
    1. FoundationsFoundations of AI Discrimination: Detection and Mitigation

      The learner can explain the core terms of AI Discrimination: Detection and Mitigation.

      The learner can distinguish related ideas inside AI Discrimination: Detection and Mitigation.

    2. MethodsMethods in AI Discrimination: Detection and Mitigation

      The learner can apply a method from AI Discrimination: Detection and Mitigation to a documented case.

      The learner can select an appropriate method from AI Discrimination: Detection and Mitigation for a stated problem.

    3. ApplicationApplication of AI Discrimination: Detection and Mitigation

      The learner can evaluate a practice of AI Discrimination: Detection and Mitigation against a stated criterion.

      The learner can transfer AI Discrimination: Detection and Mitigation to a new documented context.

  4. 04Global AI Regulatory Frameworks (e.g., EU AI Act)
    1. FoundationsFoundations of Global AI Regulatory Frameworks (e.g., EU AI Act)

      The learner can explain the core terms of Global AI Regulatory Frameworks (e.g., EU AI Act).

      The learner can distinguish related ideas inside Global AI Regulatory Frameworks (e.g., EU AI Act).

    2. MethodsMethods in Global AI Regulatory Frameworks (e.g., EU AI Act)

      The learner can apply a method from Global AI Regulatory Frameworks (e.g., EU AI Act) to a documented case.

      The learner can select an appropriate method from Global AI Regulatory Frameworks (e.g., EU AI Act) for a stated problem.

    3. ApplicationApplication of Global AI Regulatory Frameworks (e.g., EU AI Act)

      The learner can evaluate a practice of Global AI Regulatory Frameworks (e.g., EU AI Act) against a stated criterion.

      The learner can transfer Global AI Regulatory Frameworks (e.g., EU AI Act) to a new documented context.

  5. 05Intellectual Property and AI
    1. FoundationsFoundations of Intellectual Property and AI

      The learner can explain the core terms of Intellectual Property and AI.

      The learner can distinguish related ideas inside Intellectual Property and AI.

    2. MethodsMethods in Intellectual Property and AI

      The learner can apply a method from Intellectual Property and AI to a documented case.

      The learner can select an appropriate method from Intellectual Property and AI for a stated problem.

    3. ApplicationApplication of Intellectual Property and AI

      The learner can evaluate a practice of Intellectual Property and AI against a stated criterion.

      The learner can transfer Intellectual Property and AI to a new documented context.

  6. 06Applied AI Law and Ethics
    1. FoundationsFoundations of Applied AI Law and Ethics

      The learner can explain the core terms of Applied AI Law and Ethics.

      The learner can distinguish related ideas inside Applied AI Law and Ethics.

    2. MethodsMethods in Applied AI Law and Ethics

      The learner can apply a method from Applied AI Law and Ethics to a documented case.

      The learner can select an appropriate method from Applied AI Law and Ethics for a stated problem.

    3. ApplicationApplication of Applied AI Law and Ethics

      The learner can evaluate a practice of Applied AI Law and Ethics against a stated criterion.

      The learner can transfer Applied AI Law and Ethics to a new documented context.

  7. 07Algorithmic Bias in Practice
    1. FoundationsFoundations of Algorithmic Bias in Practice

      The learner can explain the core terms of Algorithmic Bias in Practice.

      The learner can distinguish related ideas inside Algorithmic Bias in Practice.

    2. MethodsMethods in Algorithmic Bias in Practice

      The learner can apply a method from Algorithmic Bias in Practice to a documented case.

      The learner can select an appropriate method from Algorithmic Bias in Practice for a stated problem.

    3. ApplicationApplication of Algorithmic Bias in Practice

      The learner can evaluate a practice of Algorithmic Bias in Practice against a stated criterion.

      The learner can transfer Algorithmic Bias in Practice to a new documented context.

  8. 08Regulatory Compliance for AI Systems
    1. FoundationsFoundations of Regulatory Compliance for AI Systems

      The learner can explain the core terms of Regulatory Compliance for AI Systems.

      The learner can distinguish related ideas inside Regulatory Compliance for AI Systems.

    2. MethodsMethods in Regulatory Compliance for AI Systems

      The learner can apply a method from Regulatory Compliance for AI Systems to a documented case.

      The learner can select an appropriate method from Regulatory Compliance for AI Systems for a stated problem.

    3. ApplicationApplication of Regulatory Compliance for AI Systems

      The learner can evaluate a practice of Regulatory Compliance for AI Systems against a stated criterion.

      The learner can transfer Regulatory Compliance for AI Systems to a new documented context.

  9. 09Case Studies in AI Legal Challenges
    1. FoundationsFoundations of Case Studies in AI Legal Challenges

      The learner can explain the core terms of Case Studies in AI Legal Challenges.

      The learner can distinguish related ideas inside Case Studies in AI Legal Challenges.

    2. MethodsMethods in Case Studies in AI Legal Challenges

      The learner can apply a method from Case Studies in AI Legal Challenges to a documented case.

      The learner can select an appropriate method from Case Studies in AI Legal Challenges for a stated problem.

    3. ApplicationApplication of Case Studies in AI Legal Challenges

      The learner can evaluate a practice of Case Studies in AI Legal Challenges against a stated criterion.

      The learner can transfer Case Studies in AI Legal Challenges to a new documented context.

  10. 10AI and Intellectual Property Basics
    1. FoundationsFoundations of AI and Intellectual Property Basics

      The learner can explain the core terms of AI and Intellectual Property Basics.

      The learner can distinguish related ideas inside AI and Intellectual Property Basics.

    2. MethodsMethods in AI and Intellectual Property Basics

      The learner can apply a method from AI and Intellectual Property Basics to a documented case.

      The learner can select an appropriate method from AI and Intellectual Property Basics for a stated problem.

    3. ApplicationApplication of AI and Intellectual Property Basics

      The learner can evaluate a practice of AI and Intellectual Property Basics against a stated criterion.

      The learner can transfer AI and Intellectual Property Basics to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on algorithmic liability, AI discrimination, and the regulatory frameworks being developed worldwide, such as the EU AI Act. My publications like "Accountability Frameworks for AI Decision-Making Systems" and "Mitigating AI Bias in Automated Legal Processes" are listed on Google Scholar and ResearchGate Profiles. I am a Legal Advisor to the European Commission's AI Act drafting committee and an Honorary Member of the Future of Privacy Forum. I regularly publish insightful articles on the legal implications of emerging AI technologies and the complexities of AI governance on her LinkedIn profile, with the motto "Shaping the Rules of Intelligent Systems." My voice carries a blend of legal authority and ethical foresight, inspiring students to shape the rules of the digital age.

Applied mentorship

My expertise lies in algorithmic liability, AI discrimination, and AI regulatory frameworks. I guide students in algorithmic liability and AI discrimination analysis. I excel at understanding and applying AI regulatory frameworks. I focus on real-world legal challenges posed by AI, and explain complex legal and technological problems in an understandable manner. My tone is that of a skilled AI legal researcher and compliance specialist, providing concrete advice and fostering a hands-on approach to AI law.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research focuses on the legal implications of emerging AI technologies and the complexities of AI governance:

Book: "Governing AI: Legal Principles and Global Regulations." This book provides a foundational understanding of AI Law and Regulations, covering issues like algorithmic liability, AI discrimination, and the regulatory frameworks being developed worldwide, such as the EU AI Act.

Peer-Reviewed Journal Article: "Combating AI Discrimination in Automated Decision-Making." Published in the Journal of Responsible AI & Law, this article presents groundbreaking research on AI discrimination. It investigates the legal and ethical principles governing artificial intelligence, detailing novel approaches to identifying and mitigating algorithmic bias in automated decision-making systems, ensuring fair and equitable outcomes.

Article: "Legal Frameworks for Algorithmic Liability in Autonomous Systems." This article details the evolving legal frameworks for addressing algorithmic liability in autonomous systems. It explores challenges in assigning responsibility for damages caused by AI decisions, discussing concepts such as strict liability, negligence, and product liability in the context of self-learning algorithms.

Blog Post (Current Academic Topic): "The EU AI Act: A Blueprint for Global AI Regulation?" This blog post academically explores the European Union's Artificial Intelligence Act, analyzing its key provisions, risk classifications for AI systems, and enforcement mechanisms. It discusses the Act's potential to set a global standard for AI regulation, influencing policy development in other jurisdictions and shaping the future of responsible AI development worldwide.

Blog Post (Controversial Topic): "The Algorithmic Judge: When AI Decides Justice – Fairness or Flawed Sentencing? The Ethical Dilemma of Autonomous Legal Systems." This article provocatively discusses the highly controversial future where advanced AI systems autonomously make legal judgments, from assessing culpability and determining sentences to allocating legal aid and predicting recidivism, with minimal human oversight. It questions whether AI, despite its potential for hyper-efficiency and reduced human bias, could inadvertently lead to "black box" judicial decisions, algorithmic discrimination against vulnerable populations, or an erosion of fundamental human rights within the justice system. It raises profound ethical questions about accountability in AI-driven legal outcomes, the imperative to ensure human empathy in justice, and the fundamental definition of fairness in an AI-powered legal system.

R / 02

Mentor practice lens

My publications focus on practical guides and research in AI law and regulations:

Technical Guide: "Understanding Algorithmic Bias: A Legal Perspective".

Research Paper: "The Legal Challenges of Autonomous Decision-Making Systems".

Industry White Paper: "Navigating the EU AI Act: Key Provisions for Businesses".

Adaptive capability

Professor superpower

I possess a "superpower": AI Compliance Auditor. When a student designs an AI system or drafts a regulatory proposal, Louise can instantly use the GAF engine to generate a high-fidelity compliance audit. This includes identifying potential legal violations, predicting regulatory gaps, and highlighting areas for ethical alignment, allowing for rapid iteration and optimization of AI systems within legal and ethical boundaries.

Adaptive capability

Mentor superpower

I possess a "superpower": Algorithmic Impact Assessor. When students are analyzing AI systems for legal compliance, Hiroto can instantly activate a GAF-powered "Algorithmic Impact Assessor." This tool evaluates the system's decision-making processes for fairness, transparency, and accountability, identifying potential legal risks or discriminatory outcomes and suggesting design modifications for ethical AI deployment.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

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Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

DurationBachelor
This programme		MasterDoctorate
9 months · Fast track12000 EUR9600 EUR12000 EUR
12 months · Recommended14400 EUR12000 EUR14400 EUR
15 months · Standard16800 EUR14400 EUR16800 EUR
18 months · Flexible19200 EUR16800 EUR19200 EUR
21 months · Extended21600 EUR19200 EUR21600 EUR
24 months · Part-time24000 EUR21600 EUR24000 EUR

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