Dor Bitan, Ran Canetti, Shafi Goldwasser, Rebecca Wexler
The use of hidden investigative software to collect evidence of crimes presents courts with a recurring dilemma: On the one hand, there is often clear public interest in keeping the software hidden to preserve its effectiveness in fighting crimes. On the other hand, criminal defendants have rights to inspect and challenge the full evidence against them, including law enforcement's investigative methods. In fact, in the U.S. adversarial legal system, the defendant's rights to scrutinize the government's tools are crucial to the truth-seeking process and to keeping law enforcement conduct lawful and constitutional. Presently, courts balance these conflicting interests on a case-by-case basis through evidentiary privilege law, often voicing their frustration with the challenging dilemma they face. We demonstrate how judicious use of a sophisticated cryptographic tool called Zero Knowledge Proofs (ZKPs) could help to mitigate this dilemma: Based on actual court cases where evidence was collected using a modified version of a peer-to-peer software, we demonstrate how law enforcement could, in these cases, augment their investigative software with a ZKP-based mechanism that would allow them to later provide full responses to challenges made by a defense expert -- and allow a defense expert to independently verify law enforcement claims -- while keeping the software hidden. We demonstrate the technical feasibility of our mechanism via a proof-of-concept implementation. We also propose legal analysis that justifies its use, discusses its merits, and considers the legal implications that the very existence of such a mechanism might have, even in cases where it has not been used. Our proof-of-concept may also extend to other verification dilemmas in the legal landscape.
The software industry's history is also its future. Its history has been defined by both abundance and scarcity, and its future will be, too. In the 1970s and 80s, perceived software scarcity led U.S. legislators to formally grant intellectual property protections to software creators. Later, a different kind of scarcity-a lack of access to source code-led the founders of the free and open source software movement to flip intellectual property protections on their head in an effort to better promote abundance. That movement proved wildly successful, with today's software industry based on vast amounts of freely available open source software resources that both organizations and individuals collaboratively build. Abundance and scarcity will also define software's future, but in different ways. The abundance that the open source software movement spawned is in the midst of a significant commercial phase. That sometimes means that commercial competitors bring to the table a scarcity mindset that conflicts with the norms that made that movement so successful. Intellectual property concerns at times derail what may otherwise be even greater software abundance. And because so much software is moving into the Cloud, trade secrecy may become the software industry's most important form of intellectual property to the extent the industry abandons open models of innovation. The software industry's growing dependence on artificial intelligence (AI) is likely to contribute to these trends. The software industry is increasingly becoming synonymous with the AI industry, as more and more software companies either rely on AI in running their services or provide AI products to the public. As with all software, these AI technologies are increasingly provided from the Cloud, where trade secrecy is not only possible, but often preferable. But trade secrecy may be even more likely in the AI context because much of the magic in implementing AI systems lies in the know-how to piece them together from available open source software resources, decades-old AI techniques, and data. Hence, to the extent that software and AI technologists spurn open innovation in favor of a scarcity mindset, trade secrecy is likely to become its dominant form of legal protection. The advent of web3 technologies may eventually change some of these trends. But for now, increasing secrecy seems the most likely outcome. I conclude by arguing that this shift to secrecy is likely preferable to other forms of intellectual property.
The revolutionary potential of automatic code generation tools based on Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to help software professionals write more accurate, reusable code, they could make programming accessible for a whole new class of non-technical users. However, non-technical users have been slow to embrace these tools. This may be because their concrete syntax is often patterned after the operations of textual or graphical interfaces. The interfaces are common, but users would need more extensive, precise and detailed knowledge of them than they can be assumed to have, to use them as concrete syntax. Conversational interfaces (chatbots) offer a much more accessible way for non-technical users to generate code. In this paper, we discuss the basic challenge of integrating conversational agents within Model-Driven Engineering (MDE) frameworks, then turn to look at a specific application: the auto-generation of smart contract code in multiple languages by non-technical users, based on conversational syntax. We demonstrate how this can be done, and evaluate our approach by conducting user experience survey to assess the usability and functionality of the chatbot framework.
Xing Hu, Zhipeng Gao, Xin Xia, David Lo · 5 authors
Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For end-users who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach SMARTDOC to generate user notice for smart contract functions automatically. Our tool can help end-users better understand the smart contract and aware of the financial risks, improving the usersâ confidence on the reliability of the smart contracts. SMARTDOC exploits the Transformer to learn the representation of source code and generates natural language descriptions from the learned representation. We also integrate the Pointer mechanism to copy words from the input source code instead of generating words during the prediction process. We extract 7,878 ăfunction, noticeă pairs from 54,739 smart contracts written in Solidity. Due to the limited amount of collected smart contract functions (i.e., 7,878 functions), we exploit a transfer learning technique to utilize the learned knowledge to improve the performance of SMARTDOC. The learned knowledge obtained by the pre-training on a corpus of Java code, that has similar characteristics as Solidity code. The experimental results show that our approach can effectively generate user notice given the source code and significantly outperform the state-of-the-art approaches. To investigate human perspectives on our generated user notice, we also conduct a human evaluation and ask participants to score user notice generated by different approaches. Results show that SMARTDOC outperforms baselines from three aspects, naturalness, informativeness, and similarity.
Blockchain is in its way of revolutionizing different sectors with its decentralized peer-to-peer networking. Smart contracts are the piece of software that have written rules to be executed automatically to update the state of the block chain in a systematic way. One of the main use of Smart contract is in Supply Chain management. Supply Chain management deals with lot of legal contracts at a time. Contracts are agreements between two or more parties that define the duties and obligations for execution of any kind of activities. In this research, we are trying to automate the supply chain related contracts by identifying the important entities such as contract type, start date, end date etc., by using Natural Language Processing methods, then convert the contract to smart contract. This provides an efficient template for creation of smart contracts from natural language contracts and thereby offer best smart contract template for a given type of contract in Supply Chain.
Recently, Blockchain technology adoption has expanded to many application\nareas due to the evolution of smart contracts. However, developing smart\ncontracts is non-trivial and challenging due to the lack of tools and expertise\nin this field. A promising solution to overcome this issue is to use\nModel-Driven Engineering (MDE), however, using models still involves a learning\ncurve and might not be suitable for non-technical users. To tackle this\nchallenge, chatbot or conversational interfaces can be used to assess the\nnon-technical users to specify a smart contract in gradual and interactive\nmanner.\n In this paper, we propose iContractBot, a chatbot for modeling and developing\nsmart contracts. Moreover, we investigate how to integrate iContractBot with\niContractML, a domain-specific modeling language for developing smart\ncontracts, and instantiate intention models from the chatbot. The iContractBot\nframework provides a domain-specific language (DSL) based on the user intention\nand performs model-to-text transformation to generate the smart contract code.\nA smart contract use case is presented to demonstrate how iContractBot can be\nutilized for creating models and generating the deployment artifacts for smart\ncontracts based on a simple conversation.\n
Case law is the term that refers to reports of past court decisions. It is considered an essential source of law, vital for legal professionals. Existing case law services are currently centralized, with an entity having complete control over the data and often charging fees for its access and other adding value services. This paper attempts to leverage the potential of blockchain technology in order to develop a public and decentralized platform that allows the submission of court decisions in a decentralized database and employs a network of curators who offer their validation, classification, and evaluation. Specifically, we design, analyze and implement AnyCase, a proof-of-concept prototype system on the Ethereum platform. We focus on the establishment of a sybil-resistant voting protocol used for reaching agreement and the development of a tokenized economy that incentivizes participation. Our preliminary analysis indicates that, besides being decentralized, AnyCase has the potential to compete with existing centralized systems in several other aspects.
Abstract This chapter focuses on how machine learning (ML) and distributed ledger technologies (DLTs) change the environment of the law, the substance of legal goods, and on the extent to which these changes affect legal protection. ML applications, for example, can decide a person's credit worthiness or employability. Moreover, DLTs can, for instance, self-execute transactions and policies without and beyond the law. One of the main challenges here thus concerns the regulatory effects of these novel technologies and the potential incompatibility of legal protection with techno-regulation (defined as the regulatory effects of a technology, whether or not intended). This challenge will be discussed in terms of automated compliance (âlegal by designâ) and technological articulation of fundamental rights (âlegal protection by designâ).
Abstract Smart contracts are innovative contracts that differ from traditional ones in that they are self-executing, as they entail the possibility of representing contract terms in programming code that gets automatically executed on a blockchain or other distributed ledgers. Following the latest developments in blockchain technology, smart contracts have been the focus of growing attention and are currently among the major innovations that are taking place in financial services. This paper investigates the scope for their application in insurance both in the near and longer term, exploring the legal challenges that they pose. The analysis shows that in the near term smart contracts will be mainly exploited to automate underwriting, claims handling and payouts. It considers how the automation of these processes will operate at law and emphasises the impact that smart contracts can have especially on the reduction of transaction costs and on the very essence of the insurance contractâthe insurerâs promise to pay. Building on current technological developments, the paper then turns to role that smart contracts can play in insurance in the longer term, advancing the prospect of the automation of the entire insurance contract. In particular, it argues that the interaction between smart contracts and artificial intelligence and machine learning can challenge traditional frameworks of thought such as incomplete contracting and, in the farther-distant future, will culminate in contracts that will both self-interpret and self-enforce their termsâwhat can be called the true smart contracts. The analysis identifies and addresses the main legal issues that can arise in this context, exploring how to strike a balance between the goal of fostering innovation and the need to ensure policyholder and investor protection.
Bakgrund: Satoshi Nakamoto kallas gruppen eller individen bakom kryptovalutan Bitcoin. Syftet med valutan Àr att kunna genomföra transaktioner snabbt, anonymt och kunna hÄlla tredje part, centralbanker och banker utanför. Syfte: Syftet Àr att undersöka och analysera svenska bankers instÀllning till Bitcoin. Det författarna till denna studie vill undersöka Àr hur bankens instÀllning ser ut till valutan om den blir allt mer populÀr att företag och privatpersoner börjar genomföra transaktioner utan inblandning med banken. Metod: För att kunna svara pÄ syftet har studien genomförts med en abduktiv metod. Datainsamling har skett genom att intervjua relevanta personer pÄ banker genom semistrukturerade intervjuer. I analysen förklaras sedan den datainsamlingen utifrÄn studiens teoretiska referensram. I syfte att fÄ kvalificerade bedömningar om framtiden har studien anvÀnt sig av delfi-metoden. Slutsats: Författarna till studien kom fram till att Bitcoin inte utgör nÄgot hot mot bankerna eftersom de kan kopiera tekniken och bankens kÀrnverksamhet handlar inte bara om att genomföra transaktioner. Den underliggande tekniken till Bitcoin tillsammans med en mer legitimerad valuta (exempelvis e-krona) har en möjlighet att anvÀndas av banker i framtiden.
Adam Watson, Regis Rukundakuvaga, Khachatur Matevosyan
Automated Case Management Systems are still at an early stage of adoption in many developing countries. These are frequently standalone systems implemented with donor financing, and they often fail due to capacity constraints or as a consequence of short-term, project-based funding. But there are examples of developing countries overcoming these pitfalls and producing innovative solutions that surpass government practices in more developed countries. The Integrated Electronic Case Management System (IECMS), developed and implemented by the Ministry of Justice of Rwanda from 2015-2016, is one such innovation. This system has progressed rapidly in its level of adoption and integration between law enforcement, the prosecutorâs office, courts, and corrections. This paper will discuss the key system functionalities and the implementation methodology, including both the benefits and shortcomings of this approach, with the goal of applying lessons learned in future installations. Foremost among the successes of this project were the integrated Sector Wide Approach, the thorough business process re-engineering, and strong ownership by the Rwandan Justice Sector staff. Particularly instructive will be the analysis of the integrated approach, covering five institutions with a single system in less than two years. However, the particular success in this case may not be replicable for governments with a more decentralized approach.
The law speaks clearly on the standards of proof, but listeners often misunderstand its words. This article tries, with some common sense, to explain how the law expects its standards to be applied, and then to show how the law thereby avoids such complications as the conjunction paradox. First, in accordance with belief function theory, the factfinder should start at zero belief. Given imperfect evidence, the factfinder will end up retaining a fair amount of uncommitted belief. As evidence comes in, though, the factfinder will form a belief in the truth of the disputed fact but also form a disbelief, or a belief in the factâs falsity. At the close of evidence, the standard of proof requires only comparing belief and disbelief. For example, the civil standard, rather than asking whether a fact more likely than not happened according to traditional probability theory, asks whether the factfinder believes the fact more than the factfinder believes that the fact did not happen. The burdened party need not push proof above 50% by dispelling the phantoms of every possibility, while the opponent need not generate a competing version of truth but can instead rely on denial to demand that the burdened party generate a belief.Second, belief and disbelief being nonadditive partial truths, the mathematical result is that one cannot combine beliefs by traditional probability theory, as by using the product rule designed for conjunction of betting odds. Instead, one must use multivalent logic, including its rule that conjoined likelihood equals the likelihood of the least likely element. Linking the elements in a chain tells a story that is as likely as its weakest link. Consequently, if each element of a claim or defense passes the standard of proof, the conjunction of elements will pass the standard of proof. The conjunction paradox thus vaporizes for factfinding, just as the law has always maintained. The law has found the way to decide in accord with our best knowledge of the facts.