An investigative analysis of Gender Bias in JudicialData using Supervised and Unsupervised MachineLearning Techniques
Judicial Sentences, Machine Learning, Supervised, Unsupervised, Data analytics, Data Mining, Gender bias.
Brazilian Courts have been working in virtualisation of judicial processes since this century's rise, leading to a revolution in
relations, services and labour force. A huge volume of data has been produced and computational techniques have been an intimate ally to keeping business processes under control and delivering services as juridical clients expect. However,although there is a misunderstanding that automation solutions are always ’intelligent’, which in most cases, it is not true, there has never been any discussion about the use of intelligent solutions for this end as well as any issues related with automatic predicting and decision making using historical data in context. One of the problems that has already come to light is the bias in judicial datasets around the world. Thus, this work will focus on evaluating,applying and understanding resources based on fine and parameter tuning, with the end of better using machine learning techniques when working on judicial systems, and, therefore,raising the discussion related to secondary issues. We have used a real dataset of judicial sentences (Além da Pena), applying supervised and unsupervised learning models and our results point to the accuratedetection of gender bias.