Legal Systems and Artificial Intelligence

2019年在达沃斯举行的世界经济论坛会议预示着日本“社会5.0”的曙光。它的目标是：创建一个以人为本的社会，通过一个高度整合网络空间和物理空间的系统，在经济发展和社会问题解决之间取得平衡。通过使用人工智能(AI)、机器人技术和数据，“社会5.0”建议“……在需要的时候，只向需要的人提供那些需要的产品和服务，从而优化整个社会和组织系统。”日本政府承认，实现这一愿景不会没有困难，但打算正面面对这些困难，目标是成为世界上第一个面临挑战性问题的国家，以呈现一个模范的未来社会。英国政府同样致力于投资人工智能，并同样将人工智能视为打造更有利可图的经济和繁荣社会的核心。然而，这一愿景正开始在LegalTech开发者的言辞中具体化，他们着眼于数据密集型--从而目标--丰富的法律环境。在投资和声称比人类律师和法官更强大的决策能力的鼓舞下，LegalTech现在被委托开启一个建立在人工智能和大数据基础上的“智能”法律的新时代。虽然关于这些技术的能力有许多大胆的说法，但相对较少的人关注更根本的问题，即我们如何评估使用这些技术复制法律程序的核心方面的可行性，并确保公众在开发和实施中拥有有意义的发言权。这一创新和及时的研究项目打算从多个方面探讨这些问题。在理论层面上，我们使用与日本合作伙伴合作开发的地平线扫描方法和创新的系统进化法律模型来考虑这一步骤可能产生的后果。法律推理的许多方面都具有算法特征，这些特征可以使自己实现自动化。然而，进化的观点也指出了法律推理与ML不一致的特征：包括法律知识的自反性和法律规则在遇到其他社会子系统产生的混乱和非结构化数据时的不完备性。我们将通过开发一个层级模型(或本体)来测试我们的理论，该模型(或本体)源自我们的法律专业知识和公开可用的数据集，用于根据英国法律对雇佣关系进行分类。这将让我们探索在多大程度上可以使用计算密集度较低的方法(如马尔可夫模型和蒙特卡洛树)来模拟法律推理。在这些理论创新的基础上，我们将把注意力从使用历史数据对法律领域进行建模，转向探索是否可以使用各种优化数据集的技术来可靠地预测法律案件的结果。为此，我们将使用来自英格兰和威尔士高等法院的24,179起案件的数据集。这将使我们能够利用自然语言处理(NLP)技术，除了确定纠纷的主要法律和事实要点、补救办法、费用和审判持续时间外，还可以利用命名实体识别(以识别相关方)和情绪分析(以分析意见并确定当事人的处置)。通过针对这个数据集跟踪各种预测启发式方法和ML技术，我们希望对预测纠纷结果的可行性和对哪些因素与法律决策相关的洞察有一个更细粒度的理解。这将使我们能够与现有研究的结果进行比较分析，并阐明在哪些法律背景和问题上，人工智能可以和不能用来产生准确和可重复的结果。

A World Economic Forum meeting at Davos 2019 heralded the dawn of 'Society 5.0' in Japan. Its goal: creating a 'human-centred society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.' Using Artificial Intelligence (AI), robotics and data, 'Society 5.0' proposes to '...enable the provision of only those products and services that are needed to the people that need them at the time they are needed, thereby optimizing the entire social and organizational system.' The Japanese government accepts that realising this vision 'will not be without its difficulties,' but intends 'to face them head-on with the aim of being the first in the world as a country facing challenging issues to present a model future society.' The UK government is similarly committed to investing in AI and likewise views the AI as central to engineering a more profitable economy and prosperous society.This vision is, however, starting to crystallise in the rhetoric of LegalTech developers who have the data-intensive-and thus target-rich-environment of law in their sights. Buoyed by investment and claims of superior decision-making capabilities over human lawyers and judges, LegalTech is now being deputised to usher in a new era of 'smart' law built on AI and Big Data. While there are a number of bold claims made about the capabilities of these technologies, comparatively little attention has been directed to more fundamental questions about how we might assess the feasibility of using them to replicate core aspects of legal process, and ensuring the public has a meaningful say in the development and implementation. This innovative and timely research project intends to approach these questions from a number of vectors. At a theoretical level, we consider the likely consequences of this step using a Horizon Scanning methodology developed in collaboration with our Japanese partners and an innovative systemic-evolutionary model of law. Many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are inconsistent with ML: including the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social sub-systems. We will test our theory by developing a hierarchical model (or ontology), derived from our legal expertise and public available datasets, for classifying employment relationships under UK law. This will let us probe the extent to which legal reasoning can be modelled using less computational-intensive methods such as Markov Models and Monte Carlo Trees.Building upon these theoretical innovations, we will then turn our attention from modelling a legal domain using historical data to exploring whether the outcome of legal cases can be reliably predicted using various technique for optimising datasets. For this we will use a data set comprised of 24,179 cases from the High Court of England and Wales. This will allow us to harness Natural Language Processing (NLP) techniques such as named entity recognition (to identify relevant parties) and sentiment analysis (to analyse opinions and determine the disposition of a party) in addition to identifying the main legal and factual points of the dispute, remedies, costs, and trial durations. By trailing various predictive heuristics and ML techniques against this dataset we hope to develop a more granular understanding as to the feasibility of predicting dispute outcomes and insight to what factors are relevant for legal decision-making. This will allow us to then undertake a comparative analysis with the results of existing studies and shed light on the legal contexts and questions where AI can and cannot be used to produce accurate and repeatable results.