The ethics of emerging forms of artificial intelligence has become a prolific subject in both academic and public spheres. A great deal of these concerns flow from the need to ensure that these technologies do not cause harm—physical, emotional or otherwise—to the human agents with which they will interact. In the literature, this challenge has been met with the creation of artificial moral agents: embodied or virtual forms of artificial intelligence whose decision procedures are constrained by explicit normative principles, requiring (...) the implementation of what is commonly called artificial morality into these agents. To date, the types of reasoning structures and principles which inform artificial morality have been of two kinds: first, an ethically maximal vision of artificial morality which relies on the strict implementation of traditional moral theories such as Kantian deontology or Utilitarianism, and second, a more minimalist vision which applies stochastic AI techniques to large data sets of human moral preferences so as to illicit or intuit general principles and preferences for the design of artificial morality. Taken individually, each approach is unable to fully answer the challenge of producing inoffensive behavior in artificial moral agents, most especially since both forms are unable to strike a balance between the ideal set of constraints which morality imposes on one hand, and the types of constraints public acceptability imposes, on the other. We provide an alternative approach to the design of artificial morality, the Ethical Valence Theory, whose purpose is to accommodate this balance, and apply this approach to the case of autonomous vehicles. (shrink)

The introduction of self-driving vehicles gives rise to a large number of ethical issues that go beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This article provides a broad overview of realistic ethical issues related to self-driving vehicles. Some of the major topics covered are as follows: Strong opinions for and against driverless cars may give rise to severe social and political conflicts. A low tolerance for accidents caused by driverless vehicles may delay the introduction of driverless systems (...) that would substantially reduce the risks. Trade-offs will arise between safety and other requirement on the road traffic system. Over-reliance on the swift collision-avoiding reactions of self-driving vehicles can induce people to take dangerous actions, such as stepping out in front of a car, relying on its fast braking. Children travelling alone can violate safety instructions such as the use of seatbelts. Digital information about routes and destinations can be used to convey commercial and political messages to car users. If fast passage can be bought, then socio-economic segregation of road traffic may result. Terrorists and other criminals can hack into a vehicle and make it crash. They can also use self-driving vehicles for instance to carry bombs to their designed places of detonation or to wreak havoc on a country’s road system. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard societal (...) values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

The ethics of autonomous vehicles has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. Starting from the assumption that human harm is unavoidable, many authors have developed differing accounts of what morality requires in these situations. In this article, a strategy for AV decision-making is proposed, the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation: different road (...) users hold different moral claims on the vehicle’s behavior, and the vehicle must mitigate these claims as it makes decisions about its environment. Using the context of autonomous vehicles, the harm produced by an action and the uncertainties connected to it are quantified and accounted for through deliberation, resulting in an ethical implementation coherent with reality. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of ‘moral positions’ concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an autonomous vehicle’s ethical decision making. (shrink)

Agreement technologies refer to a novel paradigm for the construction of distributed intelligent systems, where autonomous software agents negotiate to reach agreements on behalf of their human users. Smart Cities are a key application domain for agreement technologies. While several proofs of concept and prototypes exist, such systems are still far from ready for being deployed in the real-world. In this paper we focus on a novel method for managing elements of smart road infrastructures of the future, namely the case (...) of auction-based road intersections. We show that, even though the key technological elements for such methods are already available, there are multiple non-technical issues that need to be tackled before they can be applied in practice. For this purpose, we analyse legal and ethical implications of auction-based road intersections in the context of international regulations and from the standpoint of the Spanish legislation. From this exercise, we extract a set of required modifications, of both technical and legal nature, which need to be addressed so as to pave the way for the potential real-world deployment of such systems in a future that may not be too far away. (shrink)

In this paper, we explore and describe what is needed to allow connected and automated vehicles to break traffic rules in order to minimise road safety risk and to operate with appropriate transparency. Reviewing current traffic rules with particular reference to two driving situations, we illustrate why current traffic rules are not suitable for CAVs and why making new traffic rules specifically for CAVs would be inappropriate. In defining an alternative approach to achieving safe CAV driving behaviours, we describe the (...) use of ethical goal functions as part of hybrid AI systems, suggesting that functions should be defined by governmental bodies with input from citizens and stakeholders. Ethical goal functions for CAVs would enable developers to optimise driving behaviours for safety under conditions of uncertainty whilst allowing for differentiation of products according to brand values. Such functions can differ between regions according to preferences for safety behaviours within that region and can be updated over time, responding to continual socio-technological feedback loops. We conclude that defining ethical goal functions is an urgent and necessary step from governmental bodies to enable the safe and transparent operation of CAVs and accelerate the reduction in road casualties they promise to achieve. (shrink)

In this paper we introduce a computational control framework that can keep AI-driven military autonomous devices operating within the boundaries set by applicable rules of International Humanitarian Law (IHL) related to targeting. We discuss the necessary legal tests and variables, and introduce the structure of a hypothetical IHL-compliant targeting system.

Should artificial intelligences ever be included as co-authors of democratic decisions? According to the conventional view in democratic theory, the answer depends on the relationship between the political unit and the entity that is either affected or subjected to its decisions. The relational conditions for inclusion as stipulated by the all-affected and all-subjected principles determine the spatial extension of democratic inclusion. Thus, AI qualifies for democratic inclusion if and only if AI is either affected or subjected to decisions by the (...) political unit in relevant ways. This paper argues that the conventional view is too simple; that it neglects democratic reasons to recognize only agents and/or moral patients as participants in decision-making. The claim defended is that AAP and ASP implicitly affirm requirements for agency and patiency. In ASP, the entity included must be an agent understood either in terms of legal status, capacity to comply with the law or ability to recognize legitimate authority. In AAP, the entity included must be a patient, understood either in terms of capacity for sentience or consciousness. Thus, the idea here is to explore the potential democratic inclusion of artificial intelligences by an updated account of the relevant conditions of agency and patiency that are implicit in democratic theory. Although conceivable that AI is or will be either affected or subjected in relevant ways to decisions made by political units, it is far less clear that AI will ever be agents or patients in the sense required for democratic inclusion. (shrink)

Modelling that exploits visual elements and information visualisation are important areas that have contributed immensely to understanding and the computerisation advancements in many domains and yet remain unexplored for the benefit of the law and legal practice. This paper investigates the challenge of modelling and expressing structures and processes in legislation and the law by using visual modelling and information visualisation (InfoVis) to assist accessibility of legal knowledge, practice and knowledge formalisation as a basis for legal AI. The paper uses (...) a subset of the well-defined Unified Modelling Language (UML) to visually express the structure and process of the legislation and the law to create visual flow diagrams called lawmaps, which form the basis of further formalisation. A lawmap development methodology is presented and evaluated by creating a set of lawmaps for the practice of conveyancing and the Landlords and Tenants Act 1954 of the United Kingdom. This paper is the first of a new breed of preliminary solutions capable of application across all aspects, from legislation to practice; and capable of accelerating development of legal AI. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.

In natural language understanding, a crucial goal is correctly interpreting open-textured phrases. In practice, disagreements over the meanings of open-textured phrases are often resolved through the generation and evaluation of interpretive arguments, arguments designed to support or attack a specific interpretation of an expression within a document. In this paper, we discuss some of our work towards the goal of automatically generating and evaluating interpretive arguments. We have curated a set of rules from the code of ethics of various professional (...) organizations and a set of associated scenarios that are ambiguous with respect to some open-textured phrase within the rule. We collected and evaluated arguments from both human annotators and state-of-the-art generative language models in order to determine the relative quality and persuasiveness of both sets of arguments. Finally, we performed a Turing test-inspired study in order to assess whether human annotators can tell the difference between human arguments and machine-generated arguments. The results show that machine-generated arguments, when prompted a certain way, can be consistently rated as more convincing than human-generated arguments, and to the untrained eye, the machine-generated arguments can convincingly sound human-like. (shrink)

Argumentation schemes bring artificial intelligence into day to day conversation. Interpreting the force of an utterance, be it an assertion, command, or question, remains a task for achieving this goal. But it is not an easy task. An interpretation of force depends on a speaker’s use of words for a hearer at the moment of utterance. Ascribing force relies on grammatical mood, though not in a straightforward or regular way. We face a dilemma: on one hand, deciding force requires an (...) understanding of the speaker’s words; on the other hand, word meaning may shift given the force in which the words are spoken. A precise theory of how mood and force relate helps us handle this dilemma, which, if met, expands the use of argumentation schemes in language processing. Yet, as our analysis shows, force is an inconstant variable, one that contributes to a scheme’s defeasibility. We propose using critical questions to help us decide the force of utterances. (shrink)

Argumentation schemes bring artificial intelligence into day to day conversation. Interpreting the force of an utterance, be it an assertion, command, or question, remains a task for achieving this goal. But it is not an easy task. An interpretation of force depends on a speaker’s use of words for a hearer at the moment of utterance. Ascribing force relies on grammatical mood, though not in a straightforward or regular way. We face a dilemma: on one hand, deciding force requires an (...) understanding of the speaker’s words; on the other hand, word meaning may shift given the force in which the words are spoken. A precise theory of how mood and force relate helps us handle this dilemma, which, if met, expands the use of argumentation schemes in language processing. Yet, as our analysis shows, force is an inconstant variable, one that contributes to a scheme’s defeasibility. We propose using critical questions to help us decide the force of utterances. (shrink)