Machine Understandable Contracts with Deep Learning
Danielle Denny2020, 2020 International Conference on Computational Science and Computational Intelligence (CSCI)
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
This paper is a review of the major international activities to create computer-understandable and executable legal specifications. They cover rules for legal contracts, regulations and laws/statutes. This includes legal markup languages (e.g. legal XML); contract management software; computable (i.e. executable) contracts; blockchain Smart contracts; and what we refer to as 'digital twin' 1 legal (i.e. computer-executable plus human-readable) specifications. Computer-executable legal specifications are fundamental for the digital automation of legal contracts and regulations, digital commerce and integrated infrastructures, such as Internet of Things interconnection of devices and Industry 4.0 for manufacturing automation (Alcácer and Cruz-Machado 2019). The paper reviews the different approaches to computer-understandable legal specifications, their potential impact on digital commerce (e.g. reducing errors, supporting automation), and important underlying AI technologies: machine learning, natural language processing (NLP) and sentiment analysis etc. We also present a 'digital twin' legal specifications system where each set of rules comprises a human readable specification (i.e. natural language) and a corresponding computerexecutable specification (i.e. code).
Related papers
The Impact of Artificial Intelligence on Contract Law: A Legal Analysis
2024
Artificial Intelligence (AI) is transforming various sectors, including law, with profound implications for contract law. This paper explores how AI technologies such as machine learning algorithms and smart contracts challenge traditional legal principles in the formation, interpretation, performance, and enforcement of contracts. It discusses the complexities AI introduces regarding legal responsibility and proposes a framework to integrate AI into contract law while ensuring fairness and accountability. Key considerations include AI's capacity to negotiate autonomously, interpret contract terms impartially, and enforce agreements through innovative technologies like blockchain. As AI continues to evolve, balancing legal principles with technological advancements remains crucial for the future of contract law.
Conversion of Legal Agreements into Smart Legal Contracts using NLP
Cornell University - arXiv, 2022
A Smart Legal Contract (SLC) is a specialized digital agreement that consists of natural language and computable components. The Accord Project is an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with a great deal of effort to create a useable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several NLP models to convert law contracts to the Accord Project's SLC format. We then further describe an interface enabling users to build their SLC with the proposed pipeline.
AI Contract: Potentiality of AI Contract to Revolutionize the Legal Field
International Journal of Research Publication and Reviews
AI (Artificial Intelligence) is a highly practiced and effective technology used in various fields all over the world. It has made our lives effortless and at the same time highly accessible to dynamic technology. AI can be described as a highly intelligent and influential technology that is far more powerful, technologically advanced and evolved than the reach of a human being which can process masses of data, accomplish and verify flaws in intricate files instantaneously and many more. Contract is a legal relationship which binds two or more parties where both parties agree to certain obligations and benefits. The traditional contract construction methods grasp considerably a long duration and required enormous human efforts along with high proficiency in the same. Here derives the real exigency of making use of the advanced technology where AI becomes a finest option today. With the help of AI in contracts, it analyses and highlights any fault in the legal document or data, fills out the loopholes and can even help in drafting a contract efficiently and swiftly. The doctrinal research outlines the concept of AI Contracts, its merits, demerits, future impacts in legal system as well as contract. The doctrinal research work aims stimulate new inventions and technological advancements in various forms of contracts which can revolutionize not only contracts but also legal realm.
The Future of Contract Review: Innovating Legal Practice Through NLP Integration
Legal Convention 2024, 2024
The legal profession is undergoing a paradigm shift driven by the integration of artificial intelligence (AI), with Natural Language Processing (NLP) emerging as one of the most transformative technologies in contract review and due diligence. In an era where the volume and complexity of contracts in business transactions continue to rise exponentially, traditional manual review processes are rapidly becoming inefficient, expensive, and prone to human error. As a result, law firms and legal departments are seeking innovative solutions that not only streamline workflows but also enhance accuracy and scalability. This research explores how NLP technology can dramatically reduce the time and resources required for contract review and due diligence while improving accuracy and consistency. We analyze the current state of NLP in legal applications, examining innovations such as machine learning algorithms for contract classification, entity extraction for key clause identification, and semantic analysis for understanding contract language (Surden, 2019a). However, the integration of NLP into legal practice is not without challenges. We address critical concerns including data privacy, maintenance of attorney-client privilege, and the need for human oversight in legal decision-making (Chagal-Feferkorn, 2019). The paper also discusses the implications for legal education and practice, as successful implementation of NLP systems necessitates new skills and approaches from legal professionals (McGinnis & Pearce, 2014)
From Legal Contracts to Formal Specifications: A Progress Report
NLP4RE, 2021
Smart contracts are software systems that partially automate, monitor and control the execution of legal contracts. The requirements of such systems consist of a formal specification of the legal contract whose execution is to be monitored and controlled. Legal contracts are always available as text expressed in natural language. We have been working on the translation of such text documents into formal specifications. Our translation process consists of four steps that (a) Semantic annotation of text identifying obligations, powers, contracting parties and assets, (b) Identification of relationships among the concepts identified in (a), (c) Generation of a domain model for terms used in the contract, as well as identification of parameters and local variables for the contract, (d) Generation of formal expressions that formalize the constituents of obligations and powers. This paper reports on the status of the project and the results that have been achieved.
LEVERAGING ARTIFICIAL INTELLIGENCE FOR INTELLECTUAL PROPERTY COMPLIANCE AND GLOBAL REGULATORY ADHERENCE
Journal ijetrm , 2024
The rapid globalization of markets and technological advancements have underscored the importance of robust intellectual property (IP) compliance and regulatory adherence. Leveraging artificial intelligence (AI) offers innovative solutions to navigate complex international IP laws and protect valuable assets. AI-powered compliance monitoring can dynamically track, interpret, and update regulatory requirements, ensuring that companies maintain adherence to evolving international IP frameworks. Machine learning (ML) models facilitate the identification and management of IP infringements across multiple jurisdictions, accounting for local legal differences and enforcement standards. This approach provides a proactive mechanism to minimize infringement risks and enhance global IP strategy. Additionally, the integration of blockchain technology and AI-driven smart contracts introduces a transformative layer of transparency and efficiency to IP rights management. These smart contracts enable real-time enforcement of licensing agreements and create immutable records that simplify transactions and fortify trust among parties. The synergy between AI, blockchain, and IP management holds significant promise in reinforcing compliance, streamlining administrative tasks, and facilitating faster dispute resolution. Companies adopting these technologies can better safeguard their intellectual assets and adapt quickly to regulatory shifts, enhancing their competitiveness and fostering sustainable growth in an increasingly interconnected economy. This paper explores how AI can be a strategic tool to bolster IP protection and regulatory adherence, offering actionable insights into its deployment for seamless, cross-border IP compliance.
Artificial Intelligence Applied to Contracts: Panacea or Poison
IJFMR Volume 5, Issue 3, 2023
The use of artificial intelligence (AI) in contract writing has led to the emergence of new and innovative contractual practices, including in drafting, execution and dispute resolution. This article presents the different aspects of the use of AI in the design and management of the different contracts of working life, focusing on the key vigilance points for contracting AI and the key clauses that need to be included in contracts to ensure legal compliance and stakeholder protection. A proactive, enlightened and adaptive approach is essential to ensure that the benefits of AI are maximized while minimizing risks and preserving the fundamental principles enshrined in international treaties and the Moroccan constitution. In addition, it also raises questions about the future of the legal profession and the need for lawyers to develop new skills to work with AI.
The Gap between Deep Learning and Law: Predicting Employment Notice
2020
This study aims to determine whether Natural Language Processing with deep learning models can shed new light on the Canadian calculation system for employment notice. In particular, we investigate whether deep learning can enhance the predictability of notice period, that is, whether it is possible to predict notice period with high accuracy. A major challenge with the classification of reasonable notice is the inconsistency of the case law. As argued by the Ontario Court of Appeal, the process of determining reasonable notice is "more art than science". In a previous study, we assessed the predictability of reasonable notice periods by applying statistical machine learning to a hand-annotated dataset of 850 cases. Building on this past study, this paper utilizes state-of-the-art deep learning models on a free-text summary of cases. We further experiment with a variety of domain adaptations of state-of-the-art pretrained BERT-esque models. Our results appear to show that ...
A preliminary study on artificial intelligence oracles and smart contracts: A legal approach to the interaction of two novel technological breakthroughs
Artificial Intelligence and Smart Contracts are two cutting-edge technological achievements of the so-called 4th Industrial Revolution era. Both have already had a significant impact on various aspects of modern life, including transactions, and each one has already been under scientific investigation. Instead, their interaction has not become the subject of a debate, although it can further (positively) affect the transactions. This interconnection takes place through specific mechanisms, called Oracles, which can be, among others, highly sophisticated Artificial Intelligence systems (autonomous systems). The present article aims to present the role of the Artificial Intelligence Oracles throughout the 'smart contractual procedure', as well as to shed light on the potential (new) legal issues this interconnection may raise. The main result of this article is to indicate the appropriate legal directions in case of Artificial Intelligence Oracles' failures, based on the most prevalent current approaches to AI's (the user's) contractual and/or non-contractual liability. The major research's conclusion is that the Artificial Intelligence Oracle's failures may result in one of the following situations: (a) breach of a (smart) contract, (b) unjust enrichment, (c) conclusion of a (voidable) smart contract that should not have been concluded, or (d) non-conclusion of a smart contract that should have been concluded. The responsibility of each person participating in the 'smart contractual procedure', i.e. the contractual parties, the blockchain platform and the Artificial Intelligence user/owner (or even the Artificial Intelligence system itself), as well as the AI provider or designer, is examined in each of the afore-mentioned situations separately. Given that legislative initiatives have already begun, the present article aspires to contribute to the consistent address of the newly raised legal issues.
State of the Art in Artificial Intelligence applied to the Legal Domain
arXiv (Cornell University), 2022
While Artificial Intelligence applied to the legal domain is a topic with origins in the last century, recent advances in Artificial Intelligence are posed to revolutionize it. This work presents an overview and contextualizes the main advances on the field of Natural Language Processing and how these advances have been used to further the state of the art in legal text analysis.
Related papers
Towards the Approach to Building Smart Contracts Based on Business Rules Using Natural Language Processing
Grail of Science
This paper considers the smart contracts development process based on business rules using natural language processing as the research object. The research subject includes software components for creating smart contracts based on business rules using natural language processing. The research aims to simplify the software component development for decentralized systems by using smart contracts generation from business rules written in natural language. This study considers smart contract development approaches and technologies, intelligent text processing methods, as well as software development techniques using the Python programming language for the experimental implementation of the proposed solution. This study outlines the relevance of this research, provides a state-of-the-art analysis, proposes the improved procedure of smart contracts’ development and deployment, and suggests an algorithm for smart contract generation based on business rules.
Ergo - a programming language for Smart Legal Contracts
ArXiv, 2021
We present a smart legal contract platform to support a wide range of smart legal contract use cases. We see this as a step towards improving existing approaches to representing the complexity of legal agreements and executing aspects of these agreements. The smart contract is a coded computer program that will automatically execute when something triggers it. In contrast, the smart legal contract is a legal agreement in digital and executable code that connects terms and can interact with other software systems. Clack et al. (2016) provides an encompassing definition of smart contract and smart legal contract by considering the operational aspect and legal focus of both while basing the definition in the topics of automation and enforceability: “A smart contract is an automatable and enforceable agreement. Automatable by computer, although some parts may require human input and control. Enforceable either by legal enforcement of rights and obligations or via tamper-proof execution ...
JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021
Cornell University - arXiv, 2021
COLIEE is an annual competition in automatic computerized legal text processing. Automatic legal document processing is an ambitious goal, and the structure and semantics of the law are often far more complex than everyday language. In this article, we survey and report our methods and experimental results in using deep learning in legal document processing. The results show the difficulties as well as potentials in this family of approaches. CCS CONCEPTS • Computing methodologies → Neural networks; • Applied computing → Law.
Deep Learning for Detecting and Explaining Unfairness in Consumer Contracts
2019
Consumer contracts often contain unfair clauses, in apparent violation of the relevant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural networks that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only provide reasons and explanations to the user, but also enhance the automated detection of unfair clauses.
Emerging Legal Informatics towards Legal Innovation: Current status and future challenges and opportunities Sugam Sharma (Computer Science and Legal Informatics) 1 , Samia Gamoura (AI and Machine Learning) 2 , Deva Prasad (Law) 3 , Arti Aneja (Law) 4
Legal Information Management, Cambridge University Press, 2021
In the last couple of years, the Legal Informatics area has slowly begun to develop as Artificial Intelligence and its allied techniques and technologies expand their footprints in the field of Law. The legal, computational, and data science communities are collaborating to build the computational and data-driven innovative legal models to improve and advance all aspects of the existing legal system with the effective use of modern computer technologies such as Machine Learning, Deep Learning, and Natural Language Processing. In this research paper, we have explored the important factors with the potential to transform the existing jurisprudence into a smart and intelligent legal system with a good degree of automation. Shortly, the existence of such a justice system can be envisioned, which is faster, fair, and economically feasible even to the highly marginalized and underprivileged societies globally, “the poor”.
The Applicability of Artificial Intelligence in Contractual Relationships
Acta Universitatis Sapientiae Legal Studies, 2020
The appearance and the impacts of AI and digitalisation in the different types of legal work and in different legal areas and in relation to certain legal institutions, are examined and analysed nowadays by many researches, in many ways. In this study, we examine the impact digitalisation and AI have on the law of obligations, particularly on the law of contract and which challenges shall the national legislators face in the near future. In the first part of the study, we deal with the formation of contracts by electronic means. After the short review of the related Hungarian regulation in force, recent results of the EU legislation will be introduced, which was generated by both the expansion of digital content and digital services. In the second part of the study, attention will be paid to a relatively new phenomenon, the so-called smart contract. In the course of our examination, we attempt to designate the framework of the notion of smart contract and to draft all those question...
Cognitively Rich Framework to Automate Extraction and Representation of Legal Knowledge
2018
With the explosive growth in cloud based services, businesses are increasingly maintaining large datasets containing information about their consumers to provide a seamless user experience. To ensure privacy and security of these datasets, regulatory bodies have speci ed rules and compliance policies that must be adhered to by organizations. These regulatory policies are currently available as text documents that are not machine processable and so require extensive manual e ort to monitor them continuously to ensure data compliance. We have developed a cognitive framework to automatically parse and extract knowledge from legal documents and represent it using an Ontology. The framework captures knowledge in form of key terms, rules, topic summaries, relationships between various legal terms, semantically similar terminologies, deontic expressions, and cross-referenced legal facts and rules. We built the framework using Deep Learning technologies like Tensorflow, for word embeddings ...
Artificial Intelligence and legal ecosystem: the new challenge
The professional activities in the last years have changed, moving through the use of digital resources, hardware and so ware, email, host-ing environments, business processes and so on. Nowadays, any professional has to confront with technological resources, digital environment , devices, Internet and networks. In the professional legal context there are many entities involved: professionals, Bar Associations, Bar Council, public bodies, clients, security funds, judicial system, banks, and more. This represents a true legal ecosystem, that is an environment within which is generated daily a very big data traffic. There are legal issues related to privacy and data protection that can be addressed by Software Management System. A Privacy Management System (PMS) is a software system working on the PbD principles and structured on the AI and ML principles. In the following paragraph it will be presented the solution. At the same time in a legal ecosystem an expert system software helps professionals; this software learns after each research reducing more and more the margin of error. In a few time a lawyer will have a powerful professional automate assistant by which earn time and money, but with a high level of competence mainly on the contents.
Automatic End-to-End Decomposition and Semantic Annotation of Laws Using High-Performance- Computing and Open Data as a Potential Driver for Digital Transformation
Zenodo (CERN European Organization for Nuclear Research), 2022
This paper is dealing with automatic end-toend law analysis conducted by decomposition and semantic annotation, by using the high-performancecomputing and government open data. Legal data and law texts are a category of open data and thus they possess the potential to unlock digital innovation and transformation capacity in governments and businesses, regarding the development of new, better, and more cost-effective services for citizens. For that reason, they can be recognized as a potential digital transformation driver. This research presents a baseline for automation of decomposition and annotation with process and service elements developed for utilization on high-performancecomputing infrastructure based on government laws open data and gives insights on how the results of it can initiate digital transformation.
AI-Powered Smart Contracts: The Dawn of Web 4. O
Smart contracts backed by artificial intelligence hold the key to a better tomorrow. As smart contracts begin to incorporate artificial intelligence, many previously steady marketplaces might instantly turn unpredictable. A smart contract is a program that operates autonomously on a blockchain. The program's decentralization ensures its safety, openness, and credibility. However, since they are static in essence and cannot adapt to new circumstances due to their immutable nature, these programs are limited in the tasks they perform. As a remedy for this shortcoming, we propose a few novel use cases where we solve some of the huge problems in a wide array of domains by incorporating Artificial Intelligence in these programs called Smart Contract. With consideration of hardware and new frameworks, this manuscript also discusses the potential of utilizing AI in such contracts. We describe the usage of AI to monitor the activity of these smart contracts in real- time and to provide ...
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.