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Professors Santarosa and Starr Use Mcubed Projects to Explore Technology and the Law

By Kristy Demas
March 1, 2019

Michigan Law Professors Veronica Santarosa and Sonja Starr recently were awarded Mcubed funding for research projects focused on technology and the law. Mcubed is part of U-M's Third Century Initiative and offers either $15,000 or $60,000 for projects conducted by cubes—interdisciplinary faculty groups brought together by a shared research interest.

The Mcubed website describes the program as one that "stimulates innovative research and scholarship by distributing real-time seed funding to multi-unit, faculty-led teams." The program's goals include empowering cubes "to keep pace with the rapid information flow of today's research environment, explore high-risk approaches that might not attract immediate support from more traditional funding mechanisms, tackle pressing social problems too complex for a single disciplinary approach, and to expose the next generation of researchers/scholars to the benefits of multidisciplinary collaboration."

"Adopting Technology: Institutional Drivers and Barriers" is the project spearheaded by Santarosa and her cube partners, Ross Business School professors Gwen Yu and Christopher Williams, which will explore the legal industry's slow adoption of technologies that could streamline a lawyer's work.

"The speed and success in technology adoption can depend on a law firm's business model, incentive structure, client demands, regulatory environment, and/or educational opportunities,” said Santarosa."Given the promises waiting to be unlocked in these innovations, unfamiliarity with technology shouldn’t scare away lawyers. We lawyers are natural intellectual brokers and are well positioned to tap into this new wave."

Law firms can make better use of their attorneys' talents by using technology to draft routine contracts, eliminating the burden of tedious document preparation. Using software that reviews contracts or extracts data from legal texts as well as more advanced tools like machine learning-powered predictive algorithms allows for more accurate and efficient contracts. "It is better to avoid a problem in the first place—to be proactive rather than reactive," said Santarosa.

Algorithms can sift through volumes of contracts to expose areas where unconscious bias might come into play and reveal hidden risks had the documents been written and reviewed by humans alone. Ultimately, Santarosa's cube hopes to show that the adoption of those new technologies and innovations would allow lawyers to leverage their legal expertise and focus on high-value work.

Starr's Mcubed project—"Fairness and Legality in Algorithmic Decision Making"—also looks at technology and the law. Along with her collaborators, Laura Balzano, assistant professor of electrical engineering and computer science, and Yuekai Sun, assistant professor of statistics, Starr will study the perception of automated decision making and how it could play a role in the legal arena, specifically criminal law cases. They will explore a number of interrelated issues "involving the fairness of predictive algorithms in criminal justice and other socially important contexts."

According to Starr, her cube partners will look at the problem through a scientific lens in an attempt to "design a dynamic machine learning model that can simultaneously maximize different, potentially competing fairness concerns, or perhaps (if that proves impossible) to prove that it can't be done."

Starr's role is different. "I have been a critic of the use of algorithms to predict criminal defendants' future crime risk because of the inappropriate demographic and socioeconomic variables they rely on and the disparate impacts they are likely to have," she said. She hopes the cube funding will help to expand her existing work into a possible book project. Starr also said that by collaborating with partners from engineering and statistics, her insight into the scientific side of the research will broaden as the project develops.

"I'm certainly open to learning about ways that algorithms might be rendered fairer,” she said "For the others, having me in the cube will help them to understand the legal and policy concerns that surround the development of these instruments—including, perhaps, pointing out when a solution that seems attractive from a technical perspective may be illegal."

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