Practical Challenges in Explicit Ethical Machine Reasoning

We examine implemented systems for ethical machine reasoning with a view to identifying the practical challenges (as opposed to philosophical challenges) posed by the area. We identify a need for complex ethical machine reasoning not only to be multi-objective, proactive, and scrutable but that it must draw on heterogeneous evidential reasoning. We also argue that, in many cases, it needs to operate in real time and be verifiable. We propose a general architecture involving a declarative ethical arbiter which draws upon multiple evidential reasoners each responsible for a particular ethical feature of the system's environment. We claim that this architecture enables some separation of concerns among the practical challenges that ethical machine reasoning poses

Harnessing Higher-Order (Meta-)Logic to Represent and Reason with Complex Ethical Theories

The computer-mechanization of an ambitious explicit ethical theory, Gewirth's Principle of Generic Consistency, is used to showcase an approach for representing and reasoning with ethical theories exhibiting complex logical features like alethic and deontic modalities, indexicals, higher-order quantification, among others. Harnessing the high expressive power of Church's type theory as a meta-logic to semantically embed a combination of quantified non-classical logics, our work pushes existing boundaries in knowledge representation and reasoning. We demonstrate that intuitive encodings of complex ethical theories and their automation on the computer are no longer antipodes.Comment: 14 page

Philosophical Signposts for Artificial Moral Agent Frameworks

This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency. At the very least, the said philosophical concepts may be treated as signposts for further research on how to truly account for the nature of Artificial Moral Agents

Responsible Autonomy

As intelligent systems are increasingly making decisions that directly affect society, perhaps the most important upcoming research direction in AI is to rethink the ethical implications of their actions. Means are needed to integrate moral, societal and legal values with technological developments in AI, both during the design process as well as part of the deliberation algorithms employed by these systems. In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems. Given that ethics are dependent on the socio-cultural context and are often only implicit in deliberation processes, methodologies are needed to elicit the values held by designers and stakeholders, and to make these explicit leading to better understanding and trust on artificial autonomous systems.Comment: IJCAI2017 (International Joint Conference on Artificial Intelligence

The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of the autonomous system. We propose an ethical recommendation component, which we call Jiminy, that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas involving the opinions of the stakeholders. First, Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, Jiminy uses context-sensitive rules to decide which of the stakeholders take preference. At the abstract level, these three methods are characterized by the addition of arguments, the addition of attacks among arguments, and the removal of attacks among arguments. We show how Jiminy can be used not only for ethical reasoning and collaborative decision making, but also for providing explanations about ethical behavior

Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support

A framework and methodology---termed LogiKEy---for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy's unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain theories in expressive classic higher-order logic (HOL). This meta-logical approach enables the provision of powerful tool support in LogiKEy: off-the-shelf theorem provers and model finders for HOL are assisting the LogiKEy designer of ethical intelligent agents to flexibly experiment with underlying logics and their combinations, with ethico-legal domain theories, and with concrete examples---all at the same time. Continuous improvements of these off-the-shelf provers, without further ado, leverage the reasoning performance in LogiKEy. Case studies, in which the LogiKEy framework and methodology has been applied and tested, give evidence that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure

Taking Turing by Surprise? Designing Digital Computers for morally-loaded contexts

There is much to learn from what Turing hastily dismissed as Lady Lovelace s objection. Digital computers can indeed surprise us. Just like a piece of art, algorithms can be designed in such a way as to lead us to question our understanding of the world, or our place within it. Some humans do lose the capacity to be surprised in that way. It might be fear, or it might be the comfort of ideological certainties. As lazy normative animals, we do need to be able to rely on authorities to simplify our reasoning: that is ok. Yet the growing sophistication of systems designed to free us from the constraints of normative engagement may take us past a point of no-return. What if, through lack of normative exercise, our moral muscles became so atrophied as to leave us unable to question our social practices? This paper makes two distinct normative claims: 1. Decision-support systems should be designed with a view to regularly jolting us out of our moral torpor. 2. Without the depth of habit to somatically anchor model certainty, a computer s experience of something new is very different from that which in humans gives rise to non-trivial surprises. This asymmetry has key repercussions when it comes to the shape of ethical agency in artificial moral agents. The worry is not just that they would be likely to leap morally ahead of us, unencumbered by habits. The main reason to doubt that the moral trajectories of humans v. autonomous systems might remain compatible stems from the asymmetry in the mechanisms underlying moral change. Whereas in humans surprises will continue to play an important role in waking us to the need for moral change, cognitive processes will rule when it comes to machines. This asymmetry will translate into increasingly different moral outlooks, to the point of likely unintelligibility. The latter prospect is enough to doubt the desirability of autonomous moral agents

Building Ethically Bounded AI

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar

Artificial morality: Making of the artificial moral agents

Abstract: Artificial Morality is a new, emerging interdisciplinary field that centres around the idea of creating artificial moral agents, or AMAs, by implementing moral competence in artificial systems. AMAs are ought to be autonomous agents capable of socially correct judgements and ethically functional behaviour. This request for moral machines comes from the changes in everyday practice, where artificial systems are being frequently used in a variety of situations from home help and elderly care purposes to banking and court algorithms. It is therefore important to create reliable and responsible machines based on the same ethical principles that society demands from people. New challenges in creating such agents appear. There are philosophical questions about a machine’s potential to be an agent, or mora l agent, in the first place. Then comes the problem of social acceptance of such machines, regardless of their theoretic agency status. As a result of efforts to resolve this problem, there are insinuations of needed additional psychological (emotional and cogn itive) competence in cold moral machines. What makes this endeavour of developing AMAs even harder is the complexity of the technical, engineering aspect of their creation. Implementation approaches such as top- down, bottom-up and hybrid approach aim to find the best way of developing fully moral agents, but they encounter their own problems throughout this effort