# The Ethics of Automating Legal Actors

arXiv: https://arxiv.org/abs/2312.00584 · HTML: https://mlsanity.com/paper/2312.00584/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 111 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [LEACE: Perfect linear concept erasure in closed form](https://mlsanity.com/paper/2306.03819/) (2023)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019)
- [Investigating Bias and Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2007.10075/) (2020)
- [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://mlsanity.com/paper/2103.13084/) (2021)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Kernelized Concept Erasure](https://mlsanity.com/paper/2201.12191/) (2022)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [LEVEN: A Large-Scale Chinese Legal Event Detection Dataset](https://mlsanity.com/paper/2203.08556/) (2022)
- [Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy](https://mlsanity.com/paper/2205.12771/) (2022)
- [CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain](https://mlsanity.com/paper/1911.08962/) (2019)
- [Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?](https://mlsanity.com/paper/2005.13213/) (2020)
- [On the Ethical Limits of Natural Language Processing on Legal Text](https://mlsanity.com/paper/2105.02751/) (2021)
- [On the Role of Negative Precedent in Legal Outcome Prediction](https://mlsanity.com/paper/2208.08225/) (2022)
- [Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models](https://mlsanity.com/paper/2104.07505/) (2021)
- [Differential Privacy in Natural Language Processing: The Story So Far](https://mlsanity.com/paper/2208.08140/) (2022)
- [What About the Precedent: An Information-Theoretic Analysis of Common Law](https://mlsanity.com/paper/2104.12133/) (2021)
- [Privacy-Preserving Models for Legal Natural Language Processing](https://mlsanity.com/paper/2211.02956/) (2022)

## Abstract (arXiv)

The introduction of large public legal datasets has brought about a renaissance in legal NLP. Many of these datasets are comprised of legal judgements - the product of judges deciding cases. This fact, together with the way machine learning works, means that several legal NLP models are models of judges. While some have argued for the automation of judges, in this position piece, we argue that automating the role of the judge raises difficult ethical challenges, in particular for common law legal systems. Our argument follows from the social role of the judge in actively shaping the law, rather than merely applying it. Since current NLP models come nowhere close to having the facilities necessary for this task, they should not be used to automate judges. Furthermore, even in the case the models could achieve human-level capabilities, there would still be remaining ethical concerns inherent in the automation of the legal process.