As the legal industry gradually integrates artificial intelligence AI into its practice, the underlying technology continues to advance at a fever pitch. Machine learning platforms arguably represent the pinnacle of AI development, and this technology currently augments and replicates intelligent human tasks in ways never before conceived. The business applications of machine learning are bearing fruit across a spectrum of industries and professions. In fact, the most advanced forms of machine learning have been relegated primarily to lower-level attorney tasks such as e-discovery, due-diligence, and legal research and, unfortunately, have yet to be embraced by the upper echelon legal decision-makers and strategists.
Finally, this article argues that incorporating machine learning will enable firms to permanently capture attorney expertise and develop deep reservoirs of reputational capital as a source of enduring competitive advantage. Advanced Search. A related, methodological rather than empirical, strand of criticism emphasizes the barrenness of an approach that discards the cognitive and deliberative import of decisions as scientifically irrelevant and can more or less trivially be reconciled with any known observable behaviour e.
Simon, , Latsis, Finally, there is a logical critique of optimization which is not as well developped in the technical literature. This paper aims at making it more precise. Unable to display preview.
Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. Infinite Regressions in the Optimizing Theory of Decision. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Brewer, Oxford, Google Scholar. Baumol, W.
Beach, L. Balzer and W. Spohn eds. Latsis, S. Latsis ed.
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