Jena McGill (University of Ottawa – Common Law Section) & Amy Salyzyn (University of Ottawa – Faculty of Law; University of Ottawa – Common Law Section) have posted The Supreme Court of Canada and Mainstreamed Judicial Analytics (Alschner, MacDonnell & Mathen, Eds. (De)Coding the Court: Legal Data Insights into Canada’s Supreme Court (Routledge), Forthcoming) on SSRN. Here is the abstract:
The Canadian legal community faces important questions about how to respond to the fast-growing field of judicial analytics. Although analyzing judicial decision-making is not new, judicial analytics tools allow for faster and more powerful analyses of large amounts of information. In the near-to-medium future, these tools will likely improve in terms of technological capacity, quality of outputs, and accessibility.
In this Chapter, we explore how mainstreamed judicial analytics might impact the Supreme Court of Canada. Specifically, we consider how analytics could influence: (1) the appointment process for Supreme Court judges; (2) the adjudication of cases at the Supreme Court; and (3) the ability of the public – and the Court itself – to appraise trends and tendencies in judicial decision-making at the Supreme Court. After canvassing the opportunities and limitations of utilizing judicial analytics in these three contexts, we conclude that, subject to some important limits, analytics may contribute to improved knowledge and transparency about the Supreme Court’s work and may provide new avenues for increased accountability. At the same time, we highlight key risks of relying on judicial analytics tools to understand the work of the Court and its judges. In order to minimize such risks, high quality tools must provide appropriately contextualized outputs and stakeholders, including the Court and its judges, should ensure they understand analytics tools and their outputs.
Finally, while analytics tools are relatively new, envisioning their potential uses at the Supreme Court reinscribes decidedly old questions about the work of judging. What makes a “good” Supreme Court judge? What kinds of observed patterns in Court practices should lead to reforms and why? What are the limits of the empirical study of patterns in the Court’s work? These questions underscore the importance of conducting analytics with a clear-eyed purpose and careful attention to the relevant normative questions. As analyses of court data become easier to produce, issues of data overload, data misuse and “data in the air” (i.e. collecting data without a purpose or baseline for evaluation) become significant risks. Such problems can frustrate the potential for analytics to add more transparency and accountability to the Court’s work.