ARIN7017_COMP7311 Legal issues in artificial intelligence and data science [2023]
Course Description: This course explores the intersection of law and artificial intelligence (AI), focusing on the legal implications, challenges, and opportunities presented by the rapid advancements in AI technologies. Students will examine the legal frameworks governing AI, ethical considerations, liability issues, intellectual property concerns, and the role of AI in shaping various legal fields such as criminal justice, intellectual property, privacy, and employment law.
Course Objectives:
- Grasp the fundamental concepts of artificial intelligence and its applications in different sectors.
- Analyze the legal and ethical implications of AI technologies.
- Evaluate existing legal frameworks and regulations governing AI.
- Examine the challenges and opportunities AI presents in various legal domains.
- Discuss the role of AI in shaping the future of law practice and legal professions.
- Develop critical thinking and problem-solving skills in addressing legal issues related to AI.
- Explore case studies and real-world examples of AI applications in law and legal decision-making.
- Discuss emerging trends and debates in the field of law and artificial intelligence.
Course Outline:
Class 1: Introduction to Law and Artificial Intelligence
- Overview of AI technologies
- Historical development and current state of AI
- Introduction to legal issues surrounding AI
Class 2: Legal and Ethical Considerations
- Ethical frameworks for AI development and deployment
- Privacy concerns and data protection laws
- Bias, fairness, and transparency in AI algorithms
Class 3: Regulatory Frameworks for AI
- International and national regulations governing AI
- Regulatory challenges and gaps
- Comparative analysis of AI regulations across jurisdictions
Class 4: Liability and Accountability
- Liability for AI-generated outcomes
- Tort law and product liability in the context of AI
- Challenges in assigning accountability in AI systems
Class 5: AI in Criminal Justice
- AI in predictive policing and risk assessment
- Legal and ethical implications of AI in criminal justice
- Challenges of bias and fairness in AI-driven decision-making
Class 6: Intellectual Property and AI
- Intellectual property rights in AI-generated works
- Patentability of AI inventions
- Copyright issues in AI-generated content
Class 7: AI and Employment Law
- Impact of AI on the future of work
- Legal considerations in AI-based hiring and employment practices
- Worker rights and responsibilities in the age of automation
Class 8: AI and Privacy Law
- Privacy challenges posed by AI technologies
- Regulatory frameworks for AI-driven data processing
- Privacy-preserving techniques and best practices
Class 9: AI and Contract Law Student Presentations
- Contracts involving AI technologies
- Legal issues in AI-driven contract automation
- Challenges in enforcing AI-generated contracts
Class 10: Future Trends and Debates Student Presentations
- Emerging trends in law and artificial intelligence
- Debates on the role of AI in legal decision-making
- Ethical and policy considerations for the future of AI in law
Assessment:
- Class participation: 20%
- Assignments and case studies: 20%
- Project/Presentation: 30%
- Final paper: 30%
|
Activity |
Weight |
When |
|
Class Participation |
20% |
Every class |
|
Assignments and case studies |
20% |
Ongoing |
|
Project/Presentation |
30% |
Starting class 8 |
|
Final paper |
30% |
Class 10 |
Note: This syllabus is subject to modification based on the instructor's discretion and evolving developments in the field of law and artificial intelligence.
FT_EXIT_SURVEY FT_EXIT_SURVEY 2023/24 FinTech Exit Survey [2023]
EXIT_SURVEY EXIT_SURVEY 2023/24 CS Exit Survey [2023]
COMP4801_FITE4801_NY Final year project information [Next Year] [2023]
COMP7305 Cluster and cloud computing [Section SA, 2023]
This course will focus on the underlying technologies that enable cloud computing as well as giving examples on various cloud deployment and delivery models. In addition, this course put more emphasis on the practical aspect of cloud computing; in particular, focusing on cloud-native application development. Students will be introduced the fundamental concepts of writing containerized applications and deploy them on the cloud platform using Kubernetes. We will adopt the Azure cloud as a learning platform for deploying cloud applications, in particular, Spark applications.
COMP3410 Internship [Section SA, 2023]
This Moodle course page is for the courses COMP3410 and COMP3412 to collect training reports. Students who are taking
- a 1-year / 6-month internship between June 2023 to August 2024
- a summer internship between May 2024 to August 2024 (including those newly joined ISCS5 students),
should submit their training reports through this Moodle page.