General Course Information
1.1 Course details
|Course code:||LLAW6280 / JDOC6280|
|Course name:||Introduction to Artificial Intelligence and Law|
|Programme offered under:||LLM Programme / JD Programme|
|Prerequisites / Co-requisites:||No|
|Credit point value:||9 credit / 6 credits|
1.2 Course description
Big data and artificial intelligence are poised to become the fourth industrial revolution, fundamentally changing the way we live, work, and learn. This course introduces how data analytics and artificial intelligence are currently applied into legal studies, legal practice, and policy making.
To get a flavor of this course, consider the following questions that we will cover: 1. Recent machine learning algorithms outperform judges in making parole decisions in the United States, that is, algorithms are now better at predicting risks associated with the release of criminal suspects. How did the algorithms accomplish this 2. Data analysis enables scholars and policy makers to precisely calculate incarceration influence on criminals income after release, for example, X years of incarceration will decrease income by $Y. How 3. Data analytics help scholars to study when and why individuals obey contracts. How?
This course can be seen as an introduction to data-driven and empirical methods in legal studies. The focus is to use real world examples to give students a basic idea of the underlying logics of applying different methods. Students are expected to achieve critical appreciation for empirical methods and data analytics thinking in law, but are not required to implement empirical research by themselves. In other words, the content covered in this course will be introductory in nature. No computer programming or statistical analysis experience is required. Students who have programming or statistical analysis experience and want to study how to implement an empirical project in law should register LLAW6285 / JDOC6285 Computer Programming, Data Mining, and the Law An Applied Introduction.
Topics to be covered in the classes include:
The definition of several closely related concepts, including statistics, big data, data analytics, and artificial intelligence
Introduction to basic methods of data analytics, including statistical test, regression analysis, logistics model, machine learning, causal inference
Overview of data analytics and artificial intelligence in scientific research, education, economics, finance, marketing, and other sectors
The application of the above methods in a range of legal areas, including contract law, property, intellectual property, criminal law, corporate and financial regulation, judicial behavior, and law and development
1.3 Course teachers
|Course convenor||John Liufirstname.lastname@example.org||CCT 902||By email|
2.1 Course Learning Outcomes (CLOs) for this course
CLO 1 Have a general understanding of statistical analysis, machine learning and artificial intelligence.
CLO 2 Have an understanding of the current and emerging legal and policy issues created by applications of artificial intelligence.
CLO 3 Engage in academic and policy debates about the direction and approaches of future legal studies and law reform.
2.2 LLM and JD Programme Learning Outcomes (PLOs)
Please refer to the following link:
2.3 Programme Learning Outcomes to be achieved in this course
|PLO A||PLO B||PLO C||PLO D||PLO E||PLO F|
3.1 Assessment Summary
|Assessment task||Due date||Weighting||Feedback method*||Course learning outcomes|
|Class participation||N/A||5%||1, 2, 3|
|Reading report||TBA||20%||1, 2, 3|
|Research proposal||TBA||25%||1, 2, 3|
|Essay||TBA||50%||1||1, 2, 3|
|*Feedback method (to be determined by course teacher)|
|1||A general course report to be disseminated through Moodle|
|2||Individual feedback to be disseminated by email / through Moodle|
|3||Individual review meeting upon appointment|
|4||Group review meeting|
|5||In-class verbal feedback|
3.2 Assessment Detail
To be advised by course convenor(s).
3.3 Grading Criteria
Please refer to the following link: https://www.law.hku.hk/_files/law_programme_grade_descriptors.pdf
4.1 Learning Activity Plan
|Seminar:||3 hours / week for 12 teaching weeks|
|Private study time:||9.5 hours / week for 12 teaching weeks|
Remarks: the normative student study load per credit unit is 25 ± 5 hours (ie. 150 ± 30 hours for a 6-credit course), which includes all learning activities and experiences within and outside of classroom, and any assessment task and examinations and associated preparations.
4.2 Details of Learning Activities
To be advised by course convenor(s).
|Reading materials:||Reading materials are posted on Moodle|
|Core reading list:||TBA|
|Recommended reading list:||TBA|
Please refer to the following link: http://www.law.hku.hk/course/learning-resources/