AI is transforming education by enhancing learning through tools like chatbots, personalized platforms, and multimedia generators, moving beyond traditional classrooms. However, while these technologies offer convenience, they also present ethical risks such as copyright infringement, which this section addresses through real-world examples, checklists, and practical guidelines for responsible AI use.
Discussions and Activities – Legal Accountability and Case Studies
AI-Related Lawsuits and Legal Precedents
Case Study Learning
Conduct a case study analysis on “Amazon’s Recruitment AI Tool,” discussing key ethical concerns like Gender Bias, Opaque Decision-Making, and Impact on Diversity to assess individual responsibility in AI ethics.
Investigate legal cases involving self-driving vehicles and medical AI diagnosis errors, such as:
Tesla Autopilot-Related Fatality (March 2018)
IBM Watson for Oncology Misdiagnosis
Legal Framework Integration
Analyze how each case aligns with specific laws, including:
Tesla Autopilot Case: Product Liability Laws, Consumer Protection Laws, Insurance, and Civil Liability.
IBM Watson Misdiagnosis: Medical Malpractice Laws, Data Privacy Laws (e.g., GDPR, HIPAA), and Liability for Unsafe Products.
Discuss how these legal rulings have influenced AI system design and operation.
Tesla Autopilot Case Discussion: “Should laws mandate regular AI performance reporting for public trust?”
IBM Watson Case Discussion: “Why is human oversight necessary for AI-driven clinical decisions?”
Legal Structuring of Accountability
Risk Distribution Structure: Examine how legal responsibility is distributed among AI developers, providers, and users, covering product liability and user negligence.
Guidelines Based on Legal Precedents: Incorporate key considerations from lawsuits into AI education, including algorithm transparency and user notification obligations.
