AI, Professionals, and Professional Work: The Practice of Law with Automated Decision Support Technologies
A report on work being done jointly with Deirdre Mulligan. Technical systems employing algorithms are shaping and displacing human decision making in a variety of fields. As technology reconfigures work practices, researchers have documented potential loss of human agency and skill, confusion about responsibility, diminished accountability, and both over- and under-reliance on decision-support systems. The introduction of predictive algorithm systems into professional decision making compounds both general concerns with bureaucratic inscrutability and opaque technical systems as well as specific concerns about encroachments on expert knowledge and (mis-)alignment with professional liability frameworks and ethics. To date, however, we have little empirical data regarding how automated decision-support tools are being debated, deployed, used, and governed in professional practice.
The objective of our ongoing empirical study is to analyze the organizational structures, professional rules and norms, and technical system properties that shape professionals’ understanding and engagement with such systems in practice. As a case study, we examine decision-support systems marketed to legal professionals, focusing primarily on technologies marketed for “e-discovery” purposes. Commonly referred to as “technology-assisted review” (TAR) or “predictive coding,” these systems increasingly rely on machine-learning techniques to classify and predict which of the voluminous electronic documents subject to litigation should be withheld or produced to the opposing side. We are accomplishing our objective through in-depth, semi-structured interviews of experts in this space: the technology company representatives who develop and sell such systems to law firms and the legal professionals who decide whether and how to use them in practice. We argue that governance approaches should be seeking to put lawyers and decision-support systems in deeper conversation, not position them as relatively passive recipients of system wisdom who must rely on out-of-system legal mechanisms to understand or challenge them. This requires attention to both the information demands of legal professionals and the processes of interaction that elicit human expertise and allow humans to obtain information about machine decision making.
Daniel N. Kluttz is a postdoctoral scholar at UC Berkeley's School of Information. There he helps organize and lead the Algorithmic Fairness and Opacity Working Group (AFOG), an interdisciplinary group that brings together UC Berkeley faculty, postdocs, and Bay Area technology professionals to develop research and policy recommendations regarding fairness and transparency, governance, professional ethics, and social impacts of emerging technologies and practices, particularly as applied to artificial-intelligence-based systems, algorithmic decision making, and data science.
Drawing from intellectual traditions in organizational theory, law and society, economic sociology, social psychology, and technology studies, Kluttz’s research is oriented around two broad lines of inquiry: 1) the formal and informal governance of economic and technological innovations, and 2) the organizational and legal environments surrounding such innovations. His current projects include studies of the psychological, organizational, and cultural underpinnings of personal data exchange in the digital economy, the effects of automated decision-support technologies on professional work practices and the construction and implementation of data science ethics in the tech industry and higher education. He has employed both qualitative and quantitative methods in his work, including in-depth interviews, longitudinal and multi-level modeling techniques, surveys, geospatial analyses, and historical/archival methods.
Kluttz’s research has appeared in a variety of peer-reviewed publications, including the Law & Society Review, Socio-Economic Review, and Handbook of Contemporary Sociological Theory. He holds a Ph.D. in sociology from UC Berkeley, a JD from the UNC-Chapel Hill School of Law, and dual bachelor’s degrees in sociology and psychology from UNC-Chapel Hill. Prior to pursuing his Ph.D., he practiced law in Raleigh, NC.