Justice Goh Yihan: Speech delivered at the SMU Necessary Legal Skills for Junior Lawyers in the Age of AI event

15.     Now, what do you need to learn about? 

16.     First, you will need to know how to use AI tools within the legal field, much like how LexisNexis and Westlaw are already integrated into legal education. It may be difficult now to settle on one tool to learn given the prevalence of new tools every other day.⁽¹⁴⁾ But, at this stage, it may not be the mastery of a particular AI tool that matters, but the general familiarity of the use and relevance of such tools. Specifically, you will need to master techniques on how to write effective prompts that guide an AI to produce the desired output.⁽¹⁵⁾

17.      But even as students learn how to use such tools, they also need to know about the limitations of AI and how these may affect the very tools they use. For example, in the Guidelines for Use of Generative AI in Courts and Tribunals issued by the Courts of New Zealand to their judges, it is stressed that judges need to understand not only the capabilities of Generative AI chatbots, but also of their limitations. The Guidelines stress, for instance, that such chatbots are not search engines and certainly do not provide answers from an authoritative database. It is also important to be mindful of the data used to train the chatbot, and if such data is representative of the local context. Perhaps most importantly, students need to be taught about biases that may be inherent in any output from a Generative AI chatbot. The Singapore courts have recently released a Guide on the Use of Generative Artificial Intelligence Tools by Court Users that makes similar points. Ultimately, students need to know how to discern inaccurate output even as they rely on AI tools to enhance their work. In a sense, this is similar to how we always check the reliability of the primary source. 

18.     Apart from AI tools, students will need to know about the substantive law generated by AI, such as whether AI should be ascribed legal rights or be exposed to legal liability. I will just give you two examples.

19.     One legal controversy that has arisen from the ability of machine learning systems to learn concerns whose state of mind is relevant if a machine learning system arrives at a model that causes damage to an individual. The answer under the common law could turn on the nature of the AI system. Where the machine learning system is deterministic, the answer could lie in the programmers’ state of mind. The Singapore Court of Appeal in Quoine v B2C2⁽¹⁶⁾  addressed the question of the relevant state of mind in cases concerning deterministic AI systems. For deterministic AI systems in general, the Court of Appeal held that, “working backwards from the output that emanated from the programs”, the relevant state of knowledge is that of the programmers.⁽¹⁷⁾

20.     Yet, because the court in Quoine confined its reasoning to deterministic AI systems, the logic outlined above may not apply if the AI system in question is non-deterministic. Some possibilities as to how the law could deal with this question of state of mind include attributing the state of mind of either the programmer or user to the machine learning system, regarding the system as an agent for the relevant operator,⁽¹⁸⁾ or perhaps even creating a novel type of intention for non-deterministic machine learning systems.

21.     Another capability that some AI systems possess, especially machine learning systems, is the ability to, loosely speaking, “create”. The uniqueness of generative AI systems is that their models, instead of spotting similarities between the training data, predict missing data in the media that they have been provided with. The ability of generative AI systems to “create” works gives rise to a number of intellectual property questions. 

22.     One concerns how society should deal with AI “inventions” and “works”. One option is to conclude that, as the invention or work has no human inventor or author, it should fall into the public domain.⁽¹⁹⁾ For example, the UK Supreme Court in Thaler v Comptroller-General of Patents, Designs and Trade Marks⁽²⁰⁾  ruled that because an inventor within the meaning of the UK Patents Act 1977 must be a natural person, it follows that Dr Thaler’s AI, DABUS, which is not a person at all, cannot be an inventor under the Act. Against this, it has been argued that, as a matter of policy, without intellectual property rights over AI-created works, there would be “no tangible incentive for developers of AI machines to continue creating, using, and improving their capabilities”.⁽²¹⁾ In order to prevent this undesirable outcome, there have emerged various law reform suggestions regarding how AI-created works can be protected. These include comparing AI systems to employees creating works in the course of their employment, attributing authorship to AI systems, or creating sui generis rights for AI-created works.⁽²²⁾

23.     Whatever the answers may be to these legal controversies generated by AI, you need to be aware of them and start thinking how the law you learn can be meaningfully applied to these very real-life problems.

Unlearn

24.     We have so far talked about what you need to reinforce and learn in the age of AI. But what do you need to unlearn? I suggest that the most exciting aspect of AI in education is not only about content, but how it enhances the very learning process. So, you may need to unlearn how you have been absorbing information and to be open to learning in an entirely different way, whether in law school or beyond.

25.     In a briefing post, the UK Parliament noted that AI tools have the potential to provide different ways of learning and to help educators with lesson planning, marking, and other tasks.⁽²³⁾ There is no reason why AI cannot achieve the same in legal education.

26.     It is not hard to imagine a future where students hold moot sessions with very real AI avatars in place of reluctant classmates who awkwardly play the role of witness or opposing counsel, often with comedic but at times ineffective effect. Given how humans learn better by repeated immersion as opposed to rote learning, junior lawyers who have the opportunity of training with otherwise scarce human training partners should benefit immensely. They can also do so without concerns about causing real-world consequences until they are assessed to be ready. But in using these tools, students should also be aware of their limitations, especially being desensitised to what would otherwise be human behaviour. Indeed, research has shown that chess computers were not a perfect substitute, as players training with them were not exposed to and thus did not learn to exploit idiosyncratic human mistakes.⁽²⁴⁾

27.     Apart from being a teaching tool, AI can also revolutionise the assessment method. For example, AI could lead to collection algorithms to provide detailed and personalised feedback from students. AI systems can show student abilities, repeat lessons, and design personalised learning plans for each student.⁽²⁵⁾ This is not too far-fetched. Indeed, many of us have Amazon Alexa or Google Assistant which detects user patterns and gives user-tailored recommendations on what to buy and what to listen to. AI would thus be the powerful if persistently present virtual teaching assistant that provides truly personalised learning for individual students.⁽²⁶⁾ And what’s more, building on the collective wisdom of other learners.

28.     Ultimately, AI in the classroom has the potential of revolutionising legal training. As students, you should remain open to these new methods of learning in the age AI and unlearn learning biases you may unconsciously have. 

Conclusion

29.     To sum up, I do not think that the essence of being a lawyer will change with AI. If anything, that essence, comprising skills like critical thinking and creative resolution of problems, have become lost in the cut and thrust of legal practice. AI holds the promise of focusing us on the essence of being a lawyer once again. But to realise that promise, one needs to reinforce those core legal skills as well as humanity and ethics, learn new things brought about by AI, and unlearn learning biases that may ill-suited in the age of AI.

30.     And a course such as Legal Research and Writing is an important component of all that I have said. Indeed, I have always told people how, of all the courses I took in law school, the equivalent of Legal Research and Writing was singularly the most important course. This is not because it taught me a lot of legal principles, which can and will change over time, but it thought me how to think like a lawyer. It taught me how to structure my analysis. For instance, my tutor told us that a good research memorandum should be capable of being understood by simply looking at the table of contents of the various sections. It is a lesson that I still apply today when writing judgments.

31.     You all represent the next generation of our legal profession. You will be among the first generations of lawyers who will be exposed to AI tools from the outset of your legal education. It is daunting. But it is also exciting, so long as you do not lose sight of the necessary legal skills, humanity, and ethical values that form the essence of being a lawyer. 

32.     Thank you.