Using decision science to gain insight into ethical issues: an example involving thresholds in workers' compensation.

Systematically analyzing and comparing the ethical dimensions of policy-decision alternatives is remarkably difficult. The ethical implications of a set of decision alternatives, as well as the ranking of that set, are subject to any number of quantitative and qualitative variables, not the least of which is differing individual interpretation. In spite of this, decision science offers a consistent, transparent framework from which to analyze the ethical components and implications of policy decisions. Workers' Compensation insurance programs are state-governed systems of insurance in which workers, in exchange for giving up the right to sue their employer and their coemployees, receive some compensation if they are injured on the job, without regard as to who was at fault. Importantly, Workers' Compensation does not compensate workers for all losses. Thus, injured workers often sue those who provide goods and services to their employer's production system. Different states set different thresholds relating to who can be brought into such a lawsuit and under what conditions a sued means-of-production entity can, in turn, bring the injured party's employer into the suit as a third-party defendant. Forensic engineers are often involved in such lawsuits to evaluate whether or not a given component of a production system is or is not defective. Using Workers' Compensation as an example, this paper explores the methodology and the difficulty of quantifying the ethical implications of policy decisions by examining the concept of thresholding a policy variable. Thresholding will be defined and the ethical effect on the various parties of varying a policy threshold will be discussed.     

人工智能的可解释性与AI的法律责任问题研究

人工智能(AI)作为类人类智能,无论我们是否赋予其主体资格,在解决其法律责任问题时,都必须对其行为进行解释,为此,探讨人工智能的法律责任问题,应该基于人工智能行为的可解释性的全新路径来推进,而不是纠缠于当下学界关于人工智能主体地位与法律责任的各种主体论与责任理论。人工智能的可解释性,亦即解释人工智能如何在大数据的基础上进行算法决策。然而,在AI领域,虽然以深度学习为代表的人工智能技术已取得了令人瞩目的成就,但如何确保以非技术性的方式向最终用户和其他利益相关方解释算法决策以及任何驱动这些决策的数据,仍是一个无法得到解决的难题,人工智能"黑箱"释明难题决定了人工智能行为的不可解释性。法律责任的本质是答责,不具有可解释性的人工智能不能自我答责,因此其无法承担法律责任;法律责任的目的是预防,不具有可解释性的人工智能无法实现法律责任的预防目的。人工智能法学研究的下一个前沿问题,是人工智能的可解释性问题。     

被派遣劳动者社会保险权保障:必要性及法律规制

最新修订的《劳动合同法》充分回应了劳务派遣法律问题学术讨论达成的基本共识,但问题并未因此而得到一劳永逸的解决。被派遣劳动者的社会保险权利救济问题,不仅有利于解决劳务派遣被滥用的现实困境,也是劳动法与社会保障法衔接的关键,学界对此研究甚少。现有关于保护被派遣劳动者社会保险权利的法律规定,存在雇主责任制度失效、异地派遣滥用规制不足、社会保险费征缴主体责任缺失、工伤保险待遇支付责任分配不公等问题。只有跳出单一视角,通过民事规则的完善、行政救济的补足和刑事制裁的法律创新的公私法双重路径,才能实质有效地保护被派遣劳动者的社会保险权利,实现生存权保障和社会安全秩序的法益目标。     

Beyond the Goods/Resources Dichotomy: Animal Labor and Trade Law

Abstract

Since its inception, the World Trade Organization (WTO) has, in a rather self-evident manner, treated animals as objects of trade: Animals must be either goods or natural resources subject to the terms and conditions of the General Agreement on Tariffs and Trade (GATT). However, broader public and legal efforts to recategorize animals from goods to “sentient beings,” which are emerging across the world, are casting serious doubt on these assumptions. Using animals’ subjectivity as a starting point, a new and bourgeoning strand of anthropological, ethical, and political studies argues that animals should properly be recognized as working subjects. Be it guide dogs, truffle hogs, logging elephants, or dairy cows—working animals, they argue, are owed wholly new legal and ethical duties. This article builds on these arguments to examine the consequences of “animal labor” for trade law: Are animals wrongly classified as commodities or resources? Is there a need and room to recognize animals as service providers under the General Agreement on Trade in Services (GATS)? What are the legal consequences of this proposed change? This article sets out to answer these questions and argues that recognizing animals as workers in trade law is conceptually coherent and can play a crucial role in empowering states to protect animals effectively at the international level.     

实然与应然——法律伦理之可能

法律伦理及法律伦理学是否可能取决于从法律伦理行为事实能否推导出法律伦理行为应该。然而,单纯从行为事实是推不出行为应该的,只有通过法律目的判断以及法律伦理行为事实和法律目的之关系判断才能从法律伦理行为事实如何推导出法律伦理行为应该如何。法律伦理行为之应该经由人们的实践活动可以变为现实,其具体保障在于法律伦理良心、法律伦理名誉及底线法律伦理的法律化。     

Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI

In recent years a substantial literature has emerged concerning bias, discrimination, and fairness in artificial intelligence (AI) and machine learning. Connecting this work to existing legal non-discrimination frameworks is essential to create tools and methods that are practically useful across divergent legal regimes. While much work has been undertaken from an American legal perspective, comparatively little has mapped the effects and requirements of EU law. This Article addresses this critical gap between legal, technical, and organisational notions of algorithmic fairness. Through analysis of EU non-discrimination law and jurisprudence of the European Court of Justice (ECJ) and national courts, we identify a critical incompatibility between European notions of discrimination and existing work on algorithmic and automated fairness. A clear gap exists between statistical measures of fairness as embedded in myriad fairness toolkits and governance mechanisms and the context-sensitive, often intuitive and ambiguous discrimination metrics and evidential requirements used by the ECJ; we refer to this approach as “contextual equality.”This Article makes three contributions. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Many of the concepts fundamental to bringing a claim, such as the composition of the disadvantaged and advantaged group, the severity and type of harm suffered, and requirements for the relevance and admissibility of evidence, require normative or political choices to be made by the judiciary on a case-by-case basis. We show that automating fairness or non-discrimination in Europe may be impossible because the law, by design, does not provide a static or homogenous framework suited to testing for discrimination in AI systems.Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Equivalent signalling mechanisms and agency do not exist in algorithmic systems. Compared to traditional forms of discrimination, automated discrimination is more abstract and unintuitive, subtle, intangible, and difficult to detect. The increasing use of algorithms disrupts traditional legal remedies and procedures for detection, investigation, prevention, and correction of discrimination which have predominantly relied upon intuition. Consistent assessment procedures that define a common standard for statistical evidence to detect and assess prima facie automated discrimination are urgently needed to support judges, regulators, system controllers and developers, and claimants.Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. A ‘gold standard’ for assessment of prima facie discrimination has been advanced by the European Court of Justice but not yet translated into standard assessment procedures for automated discrimination. We propose ‘conditional demographic disparity’ (CDD) as a standard baseline statistical measurement that aligns with the Court's ‘gold standard’. Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law.     

首席法务官（CLO） 一个崛起的法律职业群体

在公司治理结构中,＂首席法务官＂（CLO）的头衔正日益成为其中重要的一极。企业法律事务究竟怎样开展？法务官们到底是怎样的一个群体？他们在公司治理结构中到底充当什么样的角色？发挥什么作用？成功的跨国公司的企业法务工作又有什么经验教训？目前中国的企业法务官群体又是怎样的一种状态？在本刊即将推出《法人.首席法务官》增刊之际,全球企业法律顾问协会（ACC）总部总裁费雷德.克雷布斯先生接受了本刊专访,这位杰出的公司法务专家对这一系列迫在眉睫的问题作出了极有价值的回答     

证券投资基金管理人与基金管理公司的法律辨析与现行立法检讨

证券投资基金管理人在基金法律关系中具有核心地位与绝对权利,是基金法制的中心与重点。依法成立的基金管理公司是基金管理人的法定形式;但是,基金管理公司须通过核准方可担任基金管理人且基金业务并非基金管理公司的唯一业务,两者不能简单等同。因此,在对基金管理公司进行法律规制与研究时,应充分考虑其所参加的各类法律关系的特征与需求。但现行基金管理公司的立法与研究并未区分上述两者之间的差异,而将基金管理公司等同于基金管理人加以规范,存在严重的局限性并导致一定程度的不公平,应当予以准确定位与有效调整。     

Climate Change and a Just Transition to the Future of Work

Rapidly growing concerns about the adverse effects of climate change are prompting a re-thinking of how companies view their strategies and operations and spurring legal and regulatory responses around the world. The overarching objective of these efforts is to facilitate and accelerate the transition to a more sustainable economy. The green transition will have substantial distributional and structural implications for workers and the workplace across companies and economic sectors. Indeed, the future of work will be significantly shaped by climate change. However, relatively scant scholarly attention has been devoted to the forward-looking legal implications of climate change for work. Similarly, legal scholars writing on climate change have largely neglected the laws governing employment. This article seeks to help fill that gap. How can companies, workers, and society respond to the green transition in a manner that enables better jobs, a safe and stable workplace, and more resilient companies? To answer this question, this article draws on the theory of just transition, which is rooted in environmental justice and labor rights. We offer an interpretation and application of just transition that expands its scope to serve as a blueprint for ethical business conduct and legal reform to improve the world of work and the lives of workers.     

Legal perspective on possible fairness measures – A legal discussion using the example of hiring decisions

With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a ”fair” ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute ”fairness”. The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs to determine which kind of fairness is the right one to choose for which certain scenario. To understand the implications of such a determination we explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions, analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective (based on EU law).     

Legal evaluation of the attacks caused by artificial intelligence-based lethal weapon systems within the context of Rome statute

Artificial intelligence (AI) as of the level of development reached today has become a scientific reality that is subject to study in the fields of law, political science, and other social sciences besides computer and software engineering. AI systems which perform relatively simple tasks in the early stages of the development period are expected to become fully or largely autonomous in the near future. Thanks to this, AI which includes the concepts of machine learning, deep learning, and autonomy, has begun to play an important role in producing and using smart arms. However, questions about AI-Based Lethal Weapon Systems (AILWS) and attacks that can be carried out by such systems have not been fully answered under legal aspect. More particularly, it is a controversial issue who will be responsible for the actions that an AILWS has committed. In this article, we discussed whether AILWS can commit offense in the context of the Rome Statute, examined the applicable law regarding the responsibility of AILWS, and tried to assess whether these systems can be held responsible in the context of international law, crime of aggression, and individual responsibility. It is our finding that international legal rules including the Rome Statute can be applied regarding the responsibility for the act/crime of aggression caused by AILWS. However, no matter how advanced the cognitive capacity of an AI software, it will not be possible to resort to the personal responsibility of this kind of system since it has no legal personality at all. In such a case, responsibility will remain with the actors who design, produce, and use the system. Last but not least, since no AILWS software does have specific codes of conduct that can make legal and ethical reasonings for today, at the end of the study it was recommended that states and non-governmental organizations together with manifacturers should constitute the necessary ethical rules written in software programs to prevent these systems from unlawful acts and to develop mechanisms that would restrain AI from working outside human control.     

Bushwhacking the Ethical High Road: Conflict of Interest in the Practice of Law and Real Life

A long‐standing scholarly tradition regards professions, in general, and ethics rules, in particular, as “projects” of market control. It is no surprise, critics charge, that in the latest assault on the monopoly of the American legal profession–waged by multidisciplinary professional service firms–lawyers are hiding behind their ethics rules to protect their turf. In this article, I report on an extensive empirical study of conflict of interest in private legal practice and look comparatively at other fiduciaries, among them, accountants, psychotherapists, physicians, journalists, and academics. I investigate the role of ethics rules that seek to insure fiduciary loyalty in structuring the delivery of services. How does social and institutional change, roiling the fiduciary world, threaten disinterestedness and loyalty and how, if at all, do fiduciaries respond? How is the regulation of conflict of interest accomplished? Where are the conflicts rules most likely to be honored or ignored? What incentive structures encourage compliance? What are the costs and unexpected consequences of compliance? What is foregone? And is it all worth it? In what might come as a surprise to many, I find that the legal profession takes conflict of interest more seriously than many of the rest of us. As the title implies, legal practitioners largely travel alone, bushwhacking through the underbrush snarling the ethical high road. As critical scholarship predicted, lawyers do enjoy a monopoly at the end of the road. But this monopoly is achieved, not by restraint of trade or some other artifice or stratagem of market control, but by lack of competition. It seems that no one else is trudging alongside the lawyers. Lawyers are not necessarily more ethical than the others; they just behave more ethically–at least with respect to conflict of interest. The question is why. And what difference does it make?     

机动车责任保险制度在机动车事故中的作用

机动车责任保险在交通安全管理中起着抑制交通灾害,促进民事责任制度发展的重要的作用。我国虽然实行了机动车第三者责任强制保险制度,但仍然存在一定的缺陷。解决的对策是应改变责任限额全国统一数额的规定;同时扩大道路交通事故社会救助基金单一的垫付抢救费的职能,增设机动车事故人身伤害补偿金制度。     

File work,legal care,and professional habitus—an ethnographic reflection on different styles of advocacy

This article contrasts two English solicitors' styles of file work and aims to spell out some of the styles' respective practical and ethical implications. What I call ‘elaborate style’ relates to the file as the primary means of casework, while the ‘minimalist style’ relates to the file in terms of a compulsory exercise. The contrasts in style are further explored by attempting a set of explanations for them. The paper seeks explanation by relating the styles to the respective cases filed, to gender differences in the legal profession, and to the professional habitus of either solicitor. In conclusion, the inquiry suggests an explanation involving the legal discourse served. There are good reasons for either style under certain pre-trial and trial procedures. To stick habitually to one style while moving towards another procedure may cause unwanted consequences.     

Understanding the Costs of Litigation: The Case of the Hourly-Fee Lawyer

What does it mean to talk about the “cost of civil justice”? What can be done to bring down that cost? This article addresses these two important questions. Drawing on data collected by the Civil Litigation Research Project, the authors first examine the components of cost and then present an extensive analysis of what is by far the dominant element of the cost equation-legal services. The analysis of the cost of legal services examines the amount of time lawyers devote to cases and the rates they charge for their time. The major factors influencing time include adversariness, stakes, litigant goals, and court (federal versus state); hourly rates appear to be determined primarily by the legal services market. The article closes with a discussion of the implications of the results of the analysis for reforms of the civil justice system that might seek to lower the cost of justice.     

The 2013 CLSR-LSPI seminar on electronic identity: The global challenge – Presented at the 8th International Conference on Legal,Security and Privacy issues in IT Law (LSPI) November 11–15, 2013, Tilleke & Gibbins International Ltd., Bangkok,Thailand

We are the middle of a global identity crisis. New notions of identity are made possible in the online world where people eagerly share their personal data and leave ‘digital footprints’. Multiple, partial identities emerge distributed across cyberspace divorced from the physical person. The representation of personal characteristics in data sets, together with developing technologies and systems for identity management, in turn change how we are identified. Trustworthy means of electronic identification is now a key issue for business, governments and individuals in the fight against online identity crime. Yet, along with the increasing economic value of digital identity, there are also risks of identity misuse by organisations that mine large data sets for commercial purposes and in some cases by governments. Data proliferation and the non-transparency of processing practices make it impossible for the individual to track and police their use. Potential risks encompass not only threats to our privacy, but also knowledge-engineering that can falsify digital profiles attributed to us with harmful consequences. This panel session will address some of the big challenges around identity in the digital age and what they mean for policy and law (its regulation and protection). Questions for discussion include: What does identity mean today? What types of legal solutions are fit for purpose to protect modern identity interests? What rights, obligations and responsibilities should be associated with our digital identities? Should identity management be regulated and who should be held liable and for what? What should be the role of private and public sectors in identity assurance schemes? What are the global drivers of identity policies? How can due process be ensured where automated technologies affect the rights and concerns of citizens? How can individuals be more empowered to control their identity data and give informed consent to its use? How are biometrics and location-tracking devices used in body surveillance changing the identity landscape?     

Legal aspects of data cleansing in medical AI

Data quality is of paramount importance for the smooth functioning of modern data-driven AI applications with machine learning as a core technology. This is also true for medical AI, where malfunctions due to "dirty data" can have particularly dramatic harmful implications. Consequently, data cleansing is an important part in improving the usability of (Big) Data for medical AI systems. However, it should not be overlooked that data cleansing can also have negative effects on data quality if not performed carefully. This paper takes an interdisciplinary look at some of the technical and legal challenges of data cleansing against the background of European medical device law, with the key message that technical and legal aspects must always be considered together in such a sensitive context.     

Turning the Tables on Legal History: Parker's Common Law,History, and Democracy in America

Parker's Common Law, History, and Democracy in America joins an ongoing effort to turn the tables on “law and …” by replacing the familiar question “What can history, sociology, and cultural studies tell us about law?” with a new line of inquiry asking “What can law teach us about the reach and limits of disciplinary thinking?” In his study of the reception of common law into nineteenth‐century American jurisprudence, Parker unearths a notion of time based on stability and repetition that challenges the dominant modernist and historicist approach to the writing of law and history. Parker, however, shies away from drawing the full implications of this move and it remains unclear whether, in the final analysis, he escapes the spell of legal historicism.     

A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning

ABSTRACT

The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human–machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency.     

Employer remedies for work stoppages that violate no-strike provisions

An employer faced with a work stoppage that violates a contractual no-strike provision has several alternatives: the employer can seek an injunction against the strike, discipline employees involved in the strike, or attempt to recover damages for violations of the collective bargaining agreement. Each of these remedies, however, presents practical problems in terms of employer-employee relations as well as legal questions regarding the extent of relief available. In the following article, the author examines the remedies available to an employer when employees engage in activity that violates a no-strike provision. He also examines the impact of recent cases on an employer's ability to enforce a no-strike pledge.