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Biased Language Detection in Court Decisions

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

Abstract

The Portuguese Commission for Citizenship and Gender Equality advocates that equality between men and women is a fundamental principle of the Portuguese Constitution. While court decisions should reflect this principle, a preliminary analysis in cases of gender violence reveals that this is not always the case. Based on the extensive literature on subjectivity, modality and bias in Linguistics and in tandem with AI and Natural Language Processing (NLP) techniques, the research proposed in this paper aims to study the linguistic formulations that convey bias in court decisions. The goal is to develop a linguistic model and, subsequently, a tool to automatically detect gender bias in this text genre. A corpus of a set of legal sentences on gender violence has been extracted from the public access database of the Portuguese Ministry of Justice (IGFEJ), which can be subject to a manual annotation process according to a typology of biased categories and structures. By exploiting the corpus in a supervised machine learning approach while following the most recent advances in NLP, we aim to deliver the aforementioned tool for biased language detection.

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Notes

  1. 1.

    https://www.cig.gov.pt/a-cig/missao/.

  2. 2.

    Diário da República, Resolution No. 61/2018, of 21 May.

  3. 3.

    https://www.cig.gov.pt/documentacao-de-referencia/doc/portugal-mais-igual/.

  4. 4.

    http://www.dgsi.pt/.

  5. 5.

    https://genderbiasnlp.talp.cat/gebnlp2020/shared-task/.

  6. 6.

    https://pan.webis.de/semeval19/semeval19-web/.

  7. 7.

    http://doraemon.iis.sinica.edu.tw/emotionlines/challenge.html.

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Acknowledgements.

This research is partially supported by CLUP(FCT/UID/ LIN/00022/2019), by LIACC (FCT/UID/CEC/0027/2020) and by project DAR-GMINTS (POCI/01/ 0145/FEDER/031460), funded by Fundação para a Ciência e a Tecnologia (FCT).

Author information

Authors and Affiliations

  1. Faculdade de Letras da Universidade do Porto (FLUP) via Panorâmica s/n, 4150-564, Porto, Portugal

    Alexandra Guedes Pinto, Isabel Margarida Duarte, Catarina Vaz Warrot & Rui Sousa-Silva

  2. Centro de Linguística da Universidade do Porto (CLUP), Porto, Portugal

    Alexandra Guedes Pinto, Isabel Margarida Duarte, Catarina Vaz Warrot & Rui Sousa-Silva

  3. Faculdade de Engenharia da Universidade do Porto (FEUP), Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Henrique Lopes Cardoso

  4. Laboratório de Inteligência Artificial e Ciências de Computadores (LIACC), Porto, Portugal

    Henrique Lopes Cardoso

Authors
  1. Alexandra Guedes Pinto
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  2. Henrique Lopes Cardoso
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  3. Isabel Margarida Duarte
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  4. Catarina Vaz Warrot
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  5. Rui Sousa-Silva
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Corresponding author

Correspondence to Henrique Lopes Cardoso .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Pinto, A.G., Cardoso, H.L., Duarte, I.M., Warrot, C.V., Sousa-Silva, R. (2020). Biased Language Detection in Court Decisions. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_38

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_38

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