Legal & Regulatory Considerations in AI Development
artificial-intelligence-ai.
Course Modules:
Module 1: The Legal Risks of AI
Legal challenges in AI development and deployment
Case studies: biased algorithms, facial recognition bans, data misuse
Introduction to AI liability and accountability
Module 2: Global Regulatory Frameworks
Overview of major AI-related regulations:
EU AI Act
OECD AI Principles
UNESCO Recommendations on AI
U.S. AI Bill of Rights
Government and international agency roles
Module 3: Data Protection & Privacy Laws
GDPR and data subject rights (EU)
CCPA (California), HIPAA (U.S.), and other global frameworks
Consent, data minimization, and lawful data usage
Module 4: Transparency, Explainability & Documentation
Legal requirements for AI explainability
Model documentation: datasheets, model cards, algorithmic impact assessments
Black box models vs. auditable systems
Module 5: Bias, Discrimination & Civil Rights
Anti-discrimination laws and algorithmic fairness
Protected attributes (gender, race, disability)
Legal responsibility for biased or harmful AI outputs
Module 6: Capstone Project – Regulatory Compliance Checklist
Choose an AI use case (e.g., healthcare chatbot, automated loan approval)
Identify applicable laws and potential risks
Submit a compliance checklist and risk mitigation plan
Tools & Resources Provided:
AI compliance checklist templates
Model documentation frameworks (datasheets, model cards)
Legal brief samples and regulation comparison charts
Optional: AI audit readiness templates
Target Audience:
AI developers and engineering leads
Legal teams and compliance officers
Product managers and startup founders
Students and professionals interested in AI governance
Global Learning Benefits:
Avoid legal penalties and ethical violations
Align AI development with national and international laws
Build user trust through transparency and compliance
Prepare for audits, assessments, and ethical reviews
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