Three Laws

Alschner on AI and Legal Analytics @w_alschner @uocommonlaw

Wolfgang Alschner, University of Ottawa Common Law Section is publishing AI and Legal Analytics in Artificial Intelligence and the Law in Canada (Florian Martin-Bariteau and Teresa Scassa, eds., Toronto: LexisNexis Canada, 2021).

Lawyers across the world are beginning to use statistics, machine learning, and data science to review contracts, investigate case law, or predict judicial outcomes. This ability to mine law as data is known as legal analytics. Legal analytics promises to render legal analysis scalable as lawyers can quickly peruse hundreds, thousands, or even millions of legal texts that would take months to read. Legal information thereby not only becomes more accessible, but legal services can be provided more efficiently and effectively helping to close the access-to-justice gap. Data and algorithms power such legal analytics. But whereas algorithms are often open source, access to legal documents such as statutes or cases in bulk is surprisingly restricted as data is often concentrated among a few large legal service providers. Creating a healthy ecosystem for legal analytics to thrive thus requires open legal data, while protecting sensitive private information, as well as innovation and competition among providers.

The full text is not available from SSRN.

McKay on Predicting Risk in Criminal Procedure @carolyn_mak

Carolyn McKay, University of Sydney Law School, is publishing Predicting Risk in Criminal Procedure: Actuarial Tools, Algorithms, AI and Judicial Decision-Making, in Predicting Risk in Criminal Procedure: Actuarial Tools, Algorithms, AI and Judicial Decision-Making' (C. McKay, ed. Current Issues in Criminal Justice, 2019 (Forthcoming)).

Risk assessments are conducted at a number of decision points in criminal procedure including in bail, sentencing and parole as well as in determining extended supervision and continuing detention orders of high risk offenders. Such risk assessments have traditionally been the function of the human discretion and intuition of judicial officers, based on clinical assessments, framed by legislation and common law principles, and encapsulating the concept of individualised justice. Yet the progressive technologisation of criminal procedure is witnessing the incursion of statistical, data-driven evaluations of risk. Human judicial evaluative functions are increasingly complemented by a range of actuarial, algorithmic, machine learning and Artificial Intelligence (AI) tools that purport to provide accurate predictive capabilities, and objective, consistent risk assessments. But ethical concerns have been raised globally regarding algorithms as proprietary products with in-built statistical bias as well as the diminution of judicial human evaluation in favour of the machine. This article focuses on risk assessment and what happens when decision-making is delegated to a predictive tool. Specifically, this article scrutinises the inscrutable proprietary nature of such risk tools and how that may render the calculation of the risk score opaque and unknowable to both the offender and the court.

Download the essay from SSRN at the link.