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Practical intensiveAI
AI Innovation and the Law: Who Owns, Controls and Regulates the Technology?
Examine how law allocates rights, power and responsibility across AI data, models, outputs, agents and deployment decisions.
Course overview
Who owns the technology, who controls its use and who answers for its effects?
Artificial intelligence now influences how organisations create, decide, communicate and act. The legal questions no longer stop at who owns generated content. They extend to the data used to build and improve models, the contractual terms governing access, the authority given to AI agents, the people affected by automated decisions, the evidence retained when a system fails and the regulators able to intervene.
This course examines who owns, controls and regulates AI across the technology lifecycle. It connects intellectual property, data protection, contract, consumer protection, competition, equality, cybersecurity and sector regulation to the decisions made by developers, deployers, vendors, users and public authorities. Nigerian law and institutions provide the starting point. The course also considers international developments where they apply directly or influence contracts, procurement, investment and market access.
The course is designed for people who build, fund, buy, advise on, regulate or are affected by AI. Practical examples show where power and responsibility sit, what evidence an organisation should retain and what must happen when a system produces a harmful or disputed result.
Who the course is for
- Founders, entrepreneurs and business leaders developing or adopting AI
- Developers, data scientists, product managers and technology professionals
- Researchers, educators, creators and professionals whose work involves AI-generated content
- Lawyers, compliance, risk, audit and procurement professionals
- Policymakers, regulators and civil-society professionals working on technology and society
Learning outcomes
What participants should be able to do
- 1Explain the difference between owning an AI system, controlling its use and regulating its effects.
- 2Distinguish rights and responsibilities relating to training data, source code, models, prompts, user inputs and generated outputs.
- 3Identify intellectual-property, confidentiality, licensing and contractual issues across the AI supply chain.
- 4Assess data-protection, privacy, equality and consumer-protection questions raised by AI development and deployment.
- 5Map responsibility among developers, model providers, deployers, vendors, professional users and organisational decision-makers.
- 6Recognise the additional legal and security issues created when AI agents can access information, use tools or act on a person’s behalf.
- 7Distinguish binding law from regulatory guidance, technical standards, contractual commitments and ethical principles.
- 8Evaluate whether transparency, human review, complaint, redress, monitoring and incident arrangements are adequate for a proposed use.
- 9Frame the evidence and questions needed to recommend that an AI use should proceed, proceed subject to conditions, pause or stop.
Current areas of focus
Data and model provenance, copyright, licensing and confidential information
General-purpose, open-source and third-party AI models
Agentic AI, identity, delegated authority, permissions and audit trails
Synthetic media, disclosure and content provenance
Automated decisions, bias, meaningful human review and redress
AI procurement, supply-chain contracts, liability and supplier change
Governance, assurance, monitoring, incidents and evidence
Nigeria’s evolving AI and data framework, with relevant comparative developments
The legal and policy source pack will be reviewed before each delivery. This allows the course to incorporate new Nigerian measures and material international developments without tying the curriculum to one year or one regulatory model.
Course design
How the course develops practical judgement
This practical intensive is built around a defined professional task. It will combine focused teaching, a worked example, a reusable template and an exercise that produces a practical output.
Learn
Work through the governing concepts and the practical distinctions that matter.
Practise
Apply the method to a realistic scenario using a guided example.
Produce
Complete a focused exercise and leave with a reusable working tool.
Curriculum status
The curriculum is being developed or reviewed. The Academy will publish the detailed module structure when it is ready for learners.