Until the things that interest you really become yours, says Brown CS alum Edwina Rissland, you’re not very deep into them. Over an eclectic career spanning more than five decades, she’s dug well below the surface as a computer scientist, mathematician, legal scholar, musician, artist, photographer, and teacher. A true student of the world, her boundless love of learning has allowed her to master and connect fields often considered distinct or even unrelated.
Now a Professor Emerita of Computer Science at University of Massachusetts Amherst, Edwina was once an undergraduate at Brown, where she excelled as a concentrator in applied math. In her junior year, she took Brown CS Professor Andy van Dam’s introductory CS course, which Andy began teaching just a few years earlier after joining the faculty at Brown.
“I became kind of hooked on CS,” she says, but “at that point I wasn’t contemplating jumping sideways into a CS career.” Upon graduating from Brown, she received a master’s degree in mathematics at Brandeis University, then switched to MIT for her PhD.
“At some point,” she says, “I wanted a break from being a student.” So she began working as an analyst at MIT’s Lincoln Laboratory, working largely on “grunt work for a project in statistical pattern recognition.” As she performed calculation after calculation, Edwina began to wish she had “a mathematician’s workbench” – a place where “you could just jump around between sources and double check things.”
Inspired by previous work in hypertext at Brown in Andy’s research group, she attempted in her Ph.D. thesis to create a system for mathematicians to navigate the vast and interconnected web of definitions, propositions, theorems and examples that form the mathematical landscape. This system, she says, was also designed to help students of math understand the context of what they were learning – how certain concepts fit in with the bigger picture, or which ideas are most important to the subject matter at hand.
Edwina’s career trajectory took a sharp turn when, in her early years as an assistant professor, a friend gifted her a short book of Supreme Court decisions. “I noticed that they were talking about these cases the same way I talked about some examples,” she says. Intrigued, she began sitting in on classes at Harvard Law School; a year later, she received a fellowship in Law and Computer Science at Harvard, where she used her classes at the Law School as fodder for a large database of legal examples employed in deciding cases and teaching law school students. She later held an appointment as Lecturer on Law at Harvard for over ten years, where she taught an upper level course on AI and legal reasoning.
In the early ‘80s, researchers including Edwina were just beginning to discover the potential connections between AI and law – AI could be used to construct legal arguments, analyze the strength of a case, and even generate thought-provoking hypotheticals. In her lab throughout the ‘80s and ‘90s, Edwina and her students began building AI systems that could identify similar cases from the past and deal with hypothetical legal arguments, mainly through symbolic processing.
That field, which came to be known as computational law or AI and law, has continued to develop rapidly as techniques in artificial intelligence and machine learning have improved. Now, as with much of the rest of AI, the focus has shifted away from purely symbolic approaches toward those using ML and data-driven methods.
With the rapid development of AI and machine learning techniques, Edwina says, the future of the field presents both great promise and potentially detrimental pitfalls. Computers could soon serve as helpful tools, giving legal professionals – and many others – access to large swaths of data that could partly inform legal decisions.
On the other hand, Edwina cautions, humans have a tendency to rely too heavily on machines. “People don’t like to understand how systems work, particularly,” she says. “I hope that in decision-making, people will use them as decision aids but not decision surrogates. This will require that our systems get much better at explanation.”
In addition to her research and teaching, Edwina twice served as Program Director for Robust Intelligence (AI) in the Information and Intelligent Systems (IIS) Division of NSF’s CISE directorate, and also served as one of the founders and early presidents of the International Association for AI and Law. She was elected as a Fellow of the Association for the Advancement of Artificial Intelligence in 1991. She holds a graduate degree in voice from the Longy School of Music (now part of Bard College) in Cambridge; and in recent years, she has exhibited and published multiple collections of photography, much of which depicts scenes from around Cape Cod and Martha’s Vineyard.
This April, Edwina was honored with the CodeX prize, which is given annually by the Stanford Center for Legal Informatics “to an individual or individuals for a noteworthy contribution to computational law,” according to the Stanford Law School. She received the prize together with Kevin Ashley; as her Ph.D. student, they worked together on a ground-breaking system (called HYPO) for generating hypotheticals and creating legal arguments.
Asked for advice, Edwina tells current students to explore their interests passionately and thoroughly. “I think Brown is amazing in that people can put together different courses of study that reflect the kinds of things they’re interested in,” she said. “Follow your instincts – you never know what you’re going to find.”
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