# Challenges When Using Jurimetrics in Brazil--A Survey of Courts
## Brazil's Freedom of Information Act
### Jurimetrics in the Context of Data Access  
Implementing jurimetric studies presents technological challenges, as the data is usually in a free form text, and a variety of techniques need to be applied to extract the requested information. Typically, a jurimetric study involves establishing a hypothesis and then gathering and processing the data that could corroborate or refute the hypothesis. Some other studies are meant to provide statistical information. In this case, instead of a hypothesis, we start with the definition of the data that we want to consolidate, but in the end, it boils down to gathering and processing data. In order to gather the data, information can be manually collected and typed in, or a script can be developed to gather the data. Needless to say, data gathering will be more effective when it can be automated by a script [22].  
To automate data gathering, one must create queries to get this data from its sources. Depending on how the data is organized and what interfaces are available to the source, this task may be simple or nearly impossible. Many sources impose blocks to automated querying, requiring human intervention. The most common implementation of this blocks are CAPTCHAS, where before delivering the results, a challenge that a machine would normally not be able to answer must be responded to [23]. These blocks are usually implemented for two reasons: to prevent data theft and to prevent the server from being overloaded from excessive queries. In the case of the courts, data theft is not an issue. However, there are some cases where allowing someone to download the entire court database might become an issue. For example, it is illegal for a potential employer to search for the labor lawsuits of a candidate. The Labor Court handles that by only allowing users to query data from the process number. Also, the second reason is relevant, as it may not be the best use of public money to finance servers that can handle automated queries.  
To allow automated queries, some companies adopt an API (Application Program Interface), which is a set of routines, protocols, and tools that allows software applications to talk to each other. In order to avoid excessive use and therefore extra costs, an API requires the user to register before using and imposes limits on hourly, daily, and/or monthly number of queries per user [24].  
When an API is not available, automated queries will likely resort to web-scraping, a technique that involves emulate a human browsing a web-site to gather the data. This process requires much more programming effort and consumes more client and server process and data-transfer, as most of the data is to make the information visually appealing to humans. To make matters worse, whenever a website layout changes, even if the same information is displayed, the web-scraping program likely stops working and needs to be updated. When a CAPTCHA is present, it is sometimes possible to implement some routine to automatically \"break\" the CAPTCHA. In most cases, the process becomes partially automatic, and the program halts for a human to answer the challenge to then resume web-scraping [25].  
Another important barrier to automate data gathering is the lack of standards. To make computer processing easier, the data should be organized and available in the same format in order to avoid expensive ETL (Extract, Transform, Load) processes. The FIA [3] makes no provision in this sense, and neither does any other regulation that followed it.