Rule-based systems have been used in the legal domain since the 1970s. Save for rare exceptions, machine learning has only recently been used. But why this delay? We investigate the appropriate use of machine learning to support and make legal predictions. To do so, we need to examine the appropriate use of data in global legal domains—including in common law, civil law, and hybrid jurisdictions. The use of various forms of Artificial Intelligence, including rule-based reasoning, case-based reasoning and machine learning in law requires an understanding of jurisprudential theories. We will see that the use of machine learning is particularly appropriate for non-professionals: in particular self-represented litigants or those relying upon legal aid services. The primary use of machine learning to support decision-making in legal domains has been in criminal detection, financial domains, and sentencing. The use in these areas has led to concerns that the inappropriate use of Artificial Intelligence leads to biased decision making. This requires us to examine concerns about governance and ethics. Ethical concerns can be minimized by providing enhanced explanation, choosing appropriate data to be used, appropriately cleaning that data, and having human reviews of any decisions.

中文翻译：

---

使用机器学习支持法律预测的好处和危险

自 20 世纪 70 年代以来，基于规则的系统一直被用于法律领域。除了极少数例外，机器学习直到最近才被使用。但为什么会延迟呢？我们研究机器学习的适当使用来支持和做出法律预测。为此，我们需要审查全球法律领域（包括普通法、民法和混合法域）中数据的适当使用。在法律中使用各种形式的人工智能，包括基于规则的推理、基于案例的推理和机器学习，需要了解法理学理论。我们将看到机器学习的使用特别适合非专业人士：特别是自我代理的诉讼当事人或依赖法律援助服务的当事人。机器学习支持法律领域决策的主要用途是刑事侦查、金融领域和量刑。这些领域的使用引发了人们的担忧，即人工智能的不当使用会导致决策出现偏差。这要求我们审视对治理和道德的担忧。通过提供增强的解释、选择要使用的适当数据、适当地清理数据以及对任何决策进行人工审查，可以最大限度地减少道德问题。