From Algorithmic Fairness to Evidentiary Reliability: Judicial Analytics under Article 6 ECHR

Arjun Bhubaneshwar Banerjee
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:148-151, 2026.

Abstract

Data-driven analyses of judicial decision-making now range from traditional empirical legal studies based on inferential statistics to computational systems that deploy technologies like machine learning (ML) and natural language processing (NLP) to detect and predict patterns in judicial decisions. In debates relating to algorithmic fairness, these methods are usually evaluated through bias metrics, mitigation techniques, and predictive performance. This paper asks a normative legal question: under what conditions can such outputs function as legally reliable evidence in the assessment of judicial impartiality under Article 6(1) European Convention on Human Rights (ECHR? The paper departs from the argument that statistical validity and legal reliability are distinct standards. Statistical validity concerns model performance, robustness, and predictive success. Legal reliability, on the other hand, concerns whether an empirical claim can operate as a reason within adjudication. Situated at the level of the European Court of Human Rights (ECtHR), the paper reconstructs reliability not as a domestic admissibility rule but as a condition of epistemic justification. It shows that empirical or computational claims about judicial decision-making can inform the Article 6 inquiry only if they are grounded in ascertainable facts, intelligible to legal reasoning, open to adversarial scrutiny, and are methodologically credible. The paper develops this claim through a four-part framework of ascertainability, transparency and source accountability, methodological credibility, and explainability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-banerjee26a, title = {From Algorithmic Fairness to Evidentiary Reliability: Judicial Analytics under Article 6 ECHR}, author = {Banerjee, Arjun Bhubaneshwar}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {148--151}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/banerjee26a/banerjee26a.pdf}, url = {https://proceedings.mlr.press/v350/banerjee26a.html}, abstract = {Data-driven analyses of judicial decision-making now range from traditional empirical legal studies based on inferential statistics to computational systems that deploy technologies like machine learning (ML) and natural language processing (NLP) to detect and predict patterns in judicial decisions. In debates relating to algorithmic fairness, these methods are usually evaluated through bias metrics, mitigation techniques, and predictive performance. This paper asks a normative legal question: under what conditions can such outputs function as legally reliable evidence in the assessment of judicial impartiality under Article 6(1) European Convention on Human Rights (ECHR? The paper departs from the argument that statistical validity and legal reliability are distinct standards. Statistical validity concerns model performance, robustness, and predictive success. Legal reliability, on the other hand, concerns whether an empirical claim can operate as a reason within adjudication. Situated at the level of the European Court of Human Rights (ECtHR), the paper reconstructs reliability not as a domestic admissibility rule but as a condition of epistemic justification. It shows that empirical or computational claims about judicial decision-making can inform the Article 6 inquiry only if they are grounded in ascertainable facts, intelligible to legal reasoning, open to adversarial scrutiny, and are methodologically credible. The paper develops this claim through a four-part framework of ascertainability, transparency and source accountability, methodological credibility, and explainability.} }
Endnote
%0 Conference Paper %T From Algorithmic Fairness to Evidentiary Reliability: Judicial Analytics under Article 6 ECHR %A Arjun Bhubaneshwar Banerjee %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-banerjee26a %I PMLR %P 148--151 %U https://proceedings.mlr.press/v350/banerjee26a.html %V 350 %X Data-driven analyses of judicial decision-making now range from traditional empirical legal studies based on inferential statistics to computational systems that deploy technologies like machine learning (ML) and natural language processing (NLP) to detect and predict patterns in judicial decisions. In debates relating to algorithmic fairness, these methods are usually evaluated through bias metrics, mitigation techniques, and predictive performance. This paper asks a normative legal question: under what conditions can such outputs function as legally reliable evidence in the assessment of judicial impartiality under Article 6(1) European Convention on Human Rights (ECHR? The paper departs from the argument that statistical validity and legal reliability are distinct standards. Statistical validity concerns model performance, robustness, and predictive success. Legal reliability, on the other hand, concerns whether an empirical claim can operate as a reason within adjudication. Situated at the level of the European Court of Human Rights (ECtHR), the paper reconstructs reliability not as a domestic admissibility rule but as a condition of epistemic justification. It shows that empirical or computational claims about judicial decision-making can inform the Article 6 inquiry only if they are grounded in ascertainable facts, intelligible to legal reasoning, open to adversarial scrutiny, and are methodologically credible. The paper develops this claim through a four-part framework of ascertainability, transparency and source accountability, methodological credibility, and explainability.
APA
Banerjee, A.B.. (2026). From Algorithmic Fairness to Evidentiary Reliability: Judicial Analytics under Article 6 ECHR. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:148-151 Available from https://proceedings.mlr.press/v350/banerjee26a.html.

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