Exploring bias analysis on judicial data using machine learning techniques

SILVA, Bruno Santos F. and DA COSTA ABREU, Marjory (2022). Exploring bias analysis on judicial data using machine learning techniques. In: 2022 12th International Conference on Pattern Recognition Systems (ICPRS). IEEE.

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Abstract

The use of data driven automation is not new, but it has gain a lot of attention recently with the wide-spread understanding that it is the solution to all problems in terms of ‘fair’ and ‘non-bias’ classification. This is not different in the law area, where ‘artificial intelligence’ became a ‘magic word’. However, using historic data is a very tricky job which can quite easily propagate discrimination in a very efficient way. Thus, this work is aimed to analyse data from legal proceedings looking for evidence related to the occurrence of bias in the judges' decision-making process, considering mainly the gender or social condition of the convicts. Supervised and unsupervised machine learning techniques, preceded by data analysis and processing procedures, were used to explain and find explicit data behaviour. Our results pointed to the fragility of the techniques to identify biases but suggest the need to improve data pre-processing and the search for more robust classification techniques.

Item Type:	Book Section
Additional Information:	ICPRS 12th International Conference on Pattern Recognition Systems 7 June 2022- 10 June 2022, Saint-Etienne, France. © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identification Number:	https://doi.org/10.1109/ICPRS54038.2022.9854068
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	27 Apr 2022 10:42
Last Modified:	12 Oct 2023 10:31
URI:	https://shura.shu.ac.uk/id/eprint/30153

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