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JoseMariaDelajaraFirstEssay 8 - 11 Oct 2019 - Main.JoseMariaDelajara
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| | | The reaction against legal analytics relies on a wrong view of data. Most people think that it serves a crystal ball, enabling them to predict the outcome of every single dispute. That is not the case. For one, legal analytics is data-hungry, so it needs enough volume, variety, velocity and veracity (known as the four V’s) Even so, a decision pattern does not necessarily mean the judge will behave the same way every time. It could be just an influence of an unexpected personal event. Also, judicial analytics depends on the details of the data of the specific court. For example, if the record shows that a court has favoured plaintiffs, it will likely attract meritless cases. At a superficial level, this will hinder the prediction power of the data of that court (i.e. the numbers will revert to the mean by the generated as a reaction to legal analytics). | |
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< | Judicial analytics does not predict the future. Instead, its output is a probability based on past decision patterns. Hence, lawyers must not forget they are still human, and that they suffer from probably-neglect bias. Most importantly, judges need to be reminded that legal analytics provides a spectral opportunity of identifying unknown patterns, even to them, and learning from past mistakes. It is a means towards making legal decision less intuitive. | >
> | Judicial analytics does not predict the future. Instead, its output is a probability based on past decision patterns. Hence, lawyers must not forget they are still human, and that they suffer from probably-neglect bias. Most importantly, judges need to be reminded that legal analytics provides a key opportunity for identifying unknown patterns, even to them, and learning from past mistakes. It is a means towards making legal decision less intuitive. | | | The way forward: data commons |
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