What Future for Human Rights? Decision-making by Algorithm

Fikfak, Veronika

doi:10.2139/ssrn.3916865

Cited by 3 publications

(3 citation statements)

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“…what decisions it was learning from in order to establish the relevant predictors, how these decisions were selected and whether cases against [a certain State] were over-represented. (Fikfak, 2021)…”

Section: Benefits and Pitfallsmentioning

confidence: 99%

The Expanding Methodological Toolbox of the ECHR Scholar

Gerards¹,

Erken²,

Loven³

2023

Law and Method

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Scholars who set out to study the European Convention on Human Rights (ECHR or Convention) system will find an abundance in research methods to choose from. In the early years of the European Court of Human Rights (ECtHR or Court), the methodological toolbox of the ECHR scholar largely consisted of qualitative and classical-doctrinal methods to study the Court's case law, as well as historical, philosophical and theoretical studies to contextualize the ECHR system. Today, these 'traditional' methods not only have evolved to reflect the enormous increase of, and scholarly interest in, the Court's case law but have also been complemented by empirical qualitative and quantitative, statistical and machine learning research methods. This contribution traces these major developments in the methods applied to studying the Court. By providing a comprehensive discussion of the different approaches, including their application, value and potential weaknesses, this contribution helps scholars understand, use and learn from the rich methodological toolbox of the ECHR scholar.

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Section: Benefits and Pitfallsmentioning

confidence: 99%

The Expanding Methodological Toolbox of the ECHR Scholar

Gerards¹,

Erken²,

Loven³

2023

Law and Method

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“…The task of legal judgment prediction raises ethical, civil rights, and legal policy concerns, both general and specific to the European Court of Human Rights (e.g., (Fikfak, 2021) on system bias and court caseload). The main premise of this work is to make incremental technical progress towards enabling systems to work with case outcome information in a way that is aligned with how human experts analyze case facts through an interplay with complex legal sources.…”

Section: Ethics Statementmentioning

confidence: 99%

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

T.y.s.s,

Ichim,

Grabmair

2023

Findings of the Association for Computational Linguistics: EACL 2023

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In this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input of case facts and convention articles. This configuration facilitates the model learning some legal reasoning ability in mapping article text to specific case fact text. It also provides an opportunity to evaluate the model's ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot experiments and apply domain adaptation methods based on domain discrimination and Wasserstein distance. Our results demonstrate that the article-aware architecture outperforms straightforward fact classification. We also find that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training influencing the effect.

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“…This limitation is particularly pronounced because of the self-referential wording of the ECtHR (Fikfak, 2021). As the court uses similar phrases in cases against the same respondent state or alleging the same violation, the model may learn that these are particularly relevant, even though this does not represent the legal reality.…”

Section: Limitationsmentioning

confidence: 99%

VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights

Xu,

Staufer,

T.y.s.s

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Recognizing vulnerability is crucial for understanding and implementing targeted support to empower individuals in need. This is especially important at the European Court of Human Rights (ECtHR), where the court adapts convention standards to meet actual individual needs and thus to ensure effective human rights protection. However, the concept of vulnerability remains elusive at the ECtHR and no prior NLP research has dealt with it. To enable future work in this area, we present VECHR, a novel expert-annotated multi-label dataset comprised of vulnerability type classification and explanation rationale. We benchmark the performance of state-of-the-art models on VECHR from both the prediction and explainability perspective. Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts. We analyze the robustness of these models in dealing with out-of-domain (OOD) data and observe limited overall performance. Our dataset poses unique challenges offering a significant room for improvement regarding performance, explainability, and robustness.

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What Future for Human Rights? Decision-making by Algorithm

Cited by 3 publications

References 0 publications

The Expanding Methodological Toolbox of the ECHR Scholar

The Expanding Methodological Toolbox of the ECHR Scholar

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights

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