RISK PROBABILITY BASED ON THE SEMINAL FREY AND OSBORNE STUDY
PROFESSIONAL GROUP CHARACTERISTICS AND SIMILAR OCCUPATIONS
Legal, Social and Religious Associate Professionals
Legal, social and religious associate professionals provide technical and practical services and support functions in legal processes and investigations, social and community assistance programmes and religious activities (ILO, 2018).
- Legal and Related Associate Professionals
- Social Work Associate Professionals
- Religious Associate Professionals
- Conveyancing clerk
- Court clerk
- Judge’s clerk
- Justice of the peace
- Law clerk
- Legal assistant
- Title searcher
In the discovery phase of a lawsuit, lawyers and paralegals can be required to sift through thousands, even tens of thousands of documents depending on the case. Now, sophisticated databases can use big data techniques including syntactic analysis and keyword recognition to accomplish the same tasks in much less time.
In fact, it’s likely that a Watson-style machine learning system could be legally “trained” to review precedent and case history and even draft legal briefs — which has traditionally been the job of lower level law firm associates. But it is not only the lowly junior associates whose jobs are at risk: a statistical model created by researchers at Michigan State University and South Texas College of Law was able to predict the outcome of almost 71 percent of U.S. Supreme Court cases (see case example below).
The artificial-intelligence platforms dramatically affect how legal work gets done. These platforms will mine documents for evidence that will be useful in litigation, to review and create contracts, raise red flags within companies to identify potential fraud and other misconduct or do legal research and perform due diligence before corporate acquisitions.
Using data to predict Supreme Court’s decisions:
Michigan State University law professor Daniel Martin Katz and his colleagues have created an algorithm that has accurately predicted 70 percent of the Supreme Court’s overall decisions, and 71 percent of the votes of individual justices — more robust results than any other predictive study done to date. Applying various techniques from machine learning, the algorithm takes into account dozens of variables before it makes a prediction.