Biased Language Detection in Court Decisions

Pinto, Alexandra Guedes; Cardoso, Henrique Lopes; Duarte, Isabel Margarida; Warrot, Catarina Vaz; Sousa‐Silva, Rui

doi:10.1007/978-3-030-62365-4_38

Cited by 6 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research has found that court decisions tend to be often gender-biased, despite the expectations that the law treats everyone equally. Recent research (Pinto et al 2020) has thus focused on building a linguistic model that will be used to develop a writing assistant that will be used by legal practitioners. The tool will flag the drafted text for possible instances of gendered language and subsequently draw the attention of the writer to those instances.…”

Section: Writing Through Technologymentioning

confidence: 99%

See 1 more Smart Citation

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

Self Cite

View full text Add to dashboard Cite

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

show abstract

Section: Writing Through Technologymentioning

confidence: 99%

“…Hovy et al 2017;Axelrod et al 2019). An example of an ethical application of NLP is the deployment of tools to identify biased language in court decisions, thereby giving the courts an opportunity to rephrase those decisions (Pinto et al 2020). Another is in the detection of fake news.…”

Section: Problems To Solve Before Using Nlp Toolsmentioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…From a forensic linguistics perspective, we can envision employing techniques such as rhetorical profiling (Visser et al 2021), which aims at characterizing-or profiling from a sociolinguistic point of view-speakers or authors in terms of arguing style, including their preference for certain types of argument schemes. In a similar vein, by exploring argumentation trails, one can develop refined models for detecting rhetorical strategies employed in fora such as news diffusion channels (Al-Khatib et al 2016), or prejudicial bias (Spliethöver and Wachsmuth 2020) in legal texts (Pinto et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Argumentation models and their use in corpus annotation: Practice, prospects, and challenges

et al. 2023

Self Cite

View full text Add to dashboard Cite

The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora.

show abstract

“…Pinto et al [49] proposed a project to develop a linguistic model and a tool to perform such a task over a (manually annotated) corpus of legal sentences published by the Portuguese Ministry of Justice on gender-based violence cases.…”

Section: Related Workmentioning

confidence: 99%

Gender Bias Detection in Court Decisions: A Brazilian Case Study

Benatti,

Severi,

Avila

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

Data derived from the realm of the social sciences is often produced in digital text form, which motivates its use as a source for natural language processing methods. Researchers and practitioners have developed and relied on artificial intelligence techniques to collect, process, and analyze documents in the legal field, especially for tasks such as text summarization and classification. While increasing procedural efficiency is often the primary motivation behind natural language processing in the field, several works have proposed solutions for human rights-related issues, such as assessment of public policy and institutional social settings. One such issue is the presence of gender biases in court decisions, which has been largely studied in social sciences fields; biased institutional responses to gender-based violence are a violation of international human rights dispositions since they prevent gender minorities from accessing rights and hamper their dignity. Natural language processing-based approaches can help detect these biases on a larger scale. Still, the development and use of such tools require researchers and practitioners to be mindful of legal and ethical aspects concerning data sharing and use, reproducibility, domain expertise, and value-charged choices. In this work, we (a) present an experimental framework developed to automatically detect gender biases in court decisions issued in Brazilian Portuguese and (b) describe and elaborate on features we identify to be critical in such a technology, given its proposed use as a support tool for research and assessment of court activity.

show abstract

Biased Language Detection in Court Decisions

Cited by 6 publications

References 18 publications

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Argumentation models and their use in corpus annotation: Practice, prospects, and challenges

Gender Bias Detection in Court Decisions: A Brazilian Case Study

Contact Info

Product

Resources

About