CS+Law
Monthly Workshop

When: Third Friday of each month at Noon Central Time (sometimes fourth Friday; next workshops: September 23; October 28; November 18; December 16; January 20; February 17; March 17; April 21; May 19)

What: First 90 minutes: Two presentations of CS+Law works in progress or new papers with open Q&A. Last 30 minutes: Networking.

Where: Zoom

Who: CS+Law faculty, postdocs, PhD students, and other students (1) enrolled in or who have completed a graduate degree in CS or Law and (2) engage in CS+Law research intended for publication.

A Steering Committee of CS+Law faculty from Berkeley, Boston U., U. Chicago, Cornell, MIT, Northwestern, Penn, and UCLA organizes the CS+Law Monthly Workshop. A different university serves as the chair for each monthly program and sets the agenda.

Why: The Steering Committee’s goals include building community, facilitating the exchange of ideas, and getting students involved. To accomplish this, we ask that participants commit to attending regularly.

Computer Science + Law is a rapidly growing area. It is increasingly common that a researcher in one of these fields must interact with the other discipline. For example, there is significant research in each field regarding the law and regulation of computation, the use of computation in legal systems and governments, and the representation of law and legal reasoning. There has been a significant increase in interdisciplinary research collaborations between researchers from CS and Law. Our goal is to create a forum for the exchange of ideas in a collegial environment that promotes building community, collaboration, and research that helps to further develop CS+Law as a field.

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


Agenda:

20-minute presentation - Aileen Nielsen

10-minute Q&A

20-minute presentation - Liren Shan

10-minute Q&A

30 minute open Q&A about both presentations

30 minutes networking and small-group discussions


Presentation 1:

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


Abstract:

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.


Presentation 2:

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


Abstract:

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.

Join us to get meeting information

Join our group to get the agenda and Zoom information for each meeting and engage in the CS+Law discussion.

Interested in presenting?

Submit a proposed topic to present. We strongly encourage the presentation of works in progress, although we will consider the presentation of more polished and published projects.

Steering Committee

Ran Canetti (Boston U.)

Aloni Cohen (U. Chicago)

April Dawson (NC Central)

Dazza Greenwood (MIT)

James Grimmelmann (Cornell Tech)

Jason Hartline (Northwestern)


Dan Linna (Northwestern)

Pamela Samuelson (Berkeley)

John Villasenor (UCLA)

Rebecca Wexler (Berkeley)

Christopher Yoo (Penn)

Background - CS+Law Monthly Workshop

Northwestern Professors Jason Hartline and Dan Linna convened an initial meeting of 21 CS+Law faculty at various universities on August 17, 2021 to propose a series of monthly CS+Law research conferences. Hartline and Linna sought volunteers to sit on a steering committee. Hartline, Linna, and their Northwestern colleagues provide the platform and administrative support for the series.