Would it sound odd if you heard the words “Eskwelabs is on the case”? Though this statement seems to come straight from a crime novel, the statement is entirely factual. Our Data Science Fellowship, a 12-week intensive part-time cohort-based program, is not just about tech skills but applying them to solve problems that are important to society.
Bianca Bencio, Matthew Chan, Fidel Racines, Jay Silverio, and Romeo Ben Manangu joined forces on one such data for good project, where they harnessed the power of data science to democratize access to legal research and lawyers, working with Digest.ph as a client.
Have you ever heard of Digest.ph? Though you may be tempted to think it’s a cooking or foodie website, it is not. Digest is a legal technology platform that aims to significantly improve access to quality legal services and research. Through the Data Science Fellowship capstone, our learners contributed to Digest’s platform by building something that would have taken years to complete without data science.
Bianca, Matthew, Fidel, Jay, and Ben worked with their Eskwelabs mentor to build a legal case classifier which is powered by artificial intelligence (AI). Their project, called “DIGEST-ION,” was able to classify, according to crime type, the thousands of cases that the Supreme Court of the Philippines have handled since its inception.
How were they able to accomplish this using data science? Here’s a simplified rundown of how they did it:
Much like it was harder to find assignment answers in the library before the Internet was invented, lawyers used to manually look for cases in archives. This task was incredibly time-consuming. Although the last few decades have seen computers enabling mass digitization of physical documents, digital databases have remained largely uncategorized and sorted only chronologically.
But why is it important for lawyers to look at past cases? The Philippines has a mixed legal system. Practically speaking, this means that both statutes (written law) and cases (Supreme Court decisions) form part of the law of the land. Lawyers must look through voluminous amounts of past cases in search of what might be relevant to their client’s particular situation. As Digest founder Raymond Rodis puts it, “Digest aims to help lawyers find relevant laws to cite in court. To do that, we needed a way of classifying thousands of Supreme Court cases into relevant categories.”
From 1901 to 2017, the Supreme Court of the Philippines resolved around 60,000 total cases. From this, the project group focused on 18,000 criminal cases. To classify these cases by hand would require lawyers to read over each case and decide what category it can be assigned to. At a speed of 10 cases a day, it would still take 5 lawyers collectively more than 1 year to complete the criminal law section. However, using data science, lawyers were only involved in advising the development of labelling. Once the Data Science Fellows finished the challenging task of labelling, software powered by machine learning (a field related to artificial intelligence) did most of the heavy lifting and classified the selected Supreme Court cases into specific criminal offense categories. This machine learning-driven case classification software is the classifier engine.
To build the classifier engine, the Fellows did the following:
Empowering lawyers, their clients, and other stakeholders of the legal system.
The classifier engine built by the DIGEST-ION team has the potential to save lawyers time in legal research. Since lawyers typically charge by the hour, saving time also means saving money. Automation of the search for cases can help democratize reasonably-priced legal services for more Filipinos.
In Rodis’ own words, “The project was able to show us a proof of concept on how an algorithm can accurately classify cases instead of lawyers spending thousands of hours categorizing [the cases] manually.”
In addition to establishing proof of concept, the Fellows gleaned important insight as to what particular techniques, models, and tools work most effectively for this challenge. Another key insight was the indispensability of working in close collaboration with domain experts, in this case, legal professionals from the Digest team. Their expertise during the labelling phase was a necessary complement to the Fellows’ data science practice. Furthermore, engaging in direct and consistent dialogue with the individuals who are most likely to use the tool allowed the Fellows to develop their project with a user-centered end goal in mind.
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