Workshop 9: Friday, September 23, 12:00 to 2:00 p.m. Central Time (Chicago time)
Please join us for our next CS+Law Research Workshop online on Friday, September 23 from Noon to 2:00 p.m. CT (Chicago time).
Workshop 9 organizer: Northwestern University (Jason Hartline and Dan Linna)
Link to join Zoom on September 23: Will be circulated to Google Group
20-minute presentation - Aileen Nielsen
20-minute presentation - Liren Shan
30 minute open Q&A about both presentations
30 minutes networking and small-group discussions
Building a Better Lawyer
Machine Guidance Can Make Legal Work Faster and Fairer
Presenter: Aileen Nielsen
Aileen Nielsen is a Fellow in Law & Tech at ETH Zurich's Center for Law and Economics. Her research emphasizes empirical and experimental approaches to understanding the interplay of law and technology, with a particular emphasis on topics in algorithmic governance and privacy. She also develops materials for industry audiences, such as a trade book on algorithmic fairness: Practical Fairness: Achieving Fair and Secure Data Models. Currently a Ph.D. student at ETH Zurich, Aileen received a J.D. at Yale Law School, a M.S. in Applied Physics from Columbia University, and a B.A. in Anthropology from Princeton University.
Co-authors: Laura Skylaki, Milda Norkute, and Alexander Stremitzer
The possibility to make lawyers work better with technology is an important and ever moving target in the development of legal technologies. Thanks to new digital technologies, lawyers can do legal research and writing far more effectively today than just a few decades ago. But, to date, most assistive technology has been limited to legal search capabilities, with attorney users of these technologies executing relatively confined instructions in a narrow task. Now, a new breed of legal tools offers guidance rather than information retrieval. This rapid expansion in the range of tasks for which a machine can offer competent guidance for legal work has created new opportunities for human-machine cooperation to improve the administration of law but also offers new risks that machine guidance may bias the practice of law in undesirable ways.
Different forms of technology can have drastically different impacts on human behavior, but, to date, there has been little empirical or experimental work to measure how legal AI might influence professional performance. Such information is crucial to ensure that machine guidance does not undermine professional conduct and accountability. It is therefore essential to test machine technologies rigorously and holistically so as to ensure good legal process and outcomes while also taking advantage of the promise of human-machine cooperation.
We present a randomized controlled study that tackles the question of how machine guidance influences the quality of legal work. We look both to the quality of the procedure by which work is carried out and the quality of the outputs themselves. Our results show that a legal AI tool can make lawyers faster and fairer without otherwise influencing aggregate measures of work quality. On the other hand, we identify some distributional effects of the machine guidance that raise concerns about the impact of machine guidance on human work quality. We thus provide experimental evidence that legal tools can improve objectively assessed performance indicators (efficiency, fairness) but also raise concerns about how the quality of legal work should be defined and regulated. In addition to these results, we also furnish an example methodology as to how organizations could begin to assess legal AI tools to ensure appropriate and responsible deployment of these tools.
Algorithmic Learning Foundations for Common Law
Preprint on arxiv
Presenter: Liren Shan
I am currently a fourth-year Ph.D. student in the Theory Group at Northwestern University, advised by Prof. Konstantin Makarychev. Before that, I was an undergrad at Fudan University, where I was advised by Prof. Zhongzhi Zhang. I have a broad interest in various aspects of theoretical computer science and mathematics. My research interests include approximation algorithms, graph theory, and algorithmic game theory. The aim of my research is to design algorithms for data analysis and decision-making in real-world problems. I currently focus on clustering and graph partitioning problems.
Co-authors: Jason D. Hartline, Daniel W. Linna Jr., Alex Tang
This paper looks at a common law legal system as a learning algorithm, models specific features of legal proceedings, and asks whether this system learns efficiently. A particular feature of our model is explicitly viewing various aspects of court proceedings as learning algorithms. This viewpoint enables directly pointing out that when the costs of going to court are not commensurate with the benefits of going to court, there is a failure of learning and inaccurate outcomes will persist in cases that settle. Specifically, cases are brought to court at an insufficient rate. On the other hand, when individuals can be compelled or incentivized to bring their cases to court, the system can learn and inaccuracy vanishes over time.