# _Ex Machina_: Analytical platforms, Law and the Challenges of Computational Legal Science
## 4 Law, Computation and the Machines
### Professional Platforms  
Today, customers show to be interested in receiving information on, for example, the odds of winning a case and they ask increasingly focused questions to their lawyer about this. The vast majority of lawyers still rely solely on their own experience, knowledge of case precedents, and intuition to predict what the courts will actually do [123]. However, they are inherently limited in their capacity to retain and process the information necessary to make well-informed judgments. Moreover, each lawyer has a limited range of personal experience. Computers, on the other hand, are far better at storing, processing, and summarising great amount of information. Thus, by leveraging the power of computers, lawyers can more accurately forecast how events will play out in litigation. Thus, lawyers who embrace data-driven decision-making will gain a clear advantage over their counterparts who still cling to their outdated instruments.  
In the last few years, all the sciences moved towards Big Data era, completely changing their perspective, while in legal sciences this great change was hard to happen. _\"Law is horribly inefficient\"_, says [PERSON], a professor at Stanford Law School, director of the Stanford Program in Law, Science and Technology. _\"And in some ways, it is inefficient by design\"_. After all, lawyers get paid by the hour, so inefficiency is rewarded, says [PERSON]. Indeed, some are rewarded richly: Top lawyers charge north of US $1000 per hour [124]. Big Data changes the rules of the game. It invites lawyers to make a fundamental change in their approach to the law itself by looking to statistical patterns, predictors, and correlations, in addition to the legal rules that purportedly control outcomes--case law, statutory law, procedural rules, and administrative regulations. Traditional lawyering required knowledge of the pertinent legal rules and the ability to apply them to a given set of facts, whether in litigation or in transactional work. The question was whether a feature of the customer's current situation would trigger a rule and its mandatory result. Analogies, comparisons, and normative judgmentsare figured into this assessment and Big Data can drive the lawyer's decision/thinking. For example, historical litigation data in the aggregate might reveal a judge's tendency to grant or deny certain types of pretrial motions, so a lawyer can act accordingly.  
In recent years, fortunately, legal science dropped from the past and moved towards data, analytics and other data-driven tech instruments. Hundreds of fresh new legal tech companies are arising [125], mostly to support lawyers in their job or customers in tackling legal issues--i.e., the business-oriented _Premonition_[126] that predicts lawyers/lawsuit's success chance - but also in research area even if the number of experiences is still limited. Indeed, leading the way in the emerging renaissance of legal research is a web-startup named _Lex Machina_[127] (which is Latin for \"law machine\"). Lex Machina [128] was founded in 2006 as an interdisciplinary project between the Stanford Law School and Stanford University's computer science department to bring transparency to intellectual-property litigation. Today, Lex Machina's web-based analytics service is used to uncover trends and patterns in historical patent litigation to more accurately forecast costs and more effectively evaluate various case strategies. The service scrapes PACER [129] for new cases involving intellectual property on a nightly basis [127; 130]. Lex Machina uses a combination of human reviewers and a proprietary algorithm that automatically parses and classifies the outcome for each case that the service extracts from PACER. After extracting, processing, and scrubbing the data, Lex Machina assembles and presents aggregated case data for a particular judge, party, attorney, or law firm with analytics that allow users to quickly discern trends and patterns in the data that may affect the cost or outcome of their case.