Machine ethics via logic programming

Machine ethics is an interdisciplinary field of inquiry that emerges from the need of imbuing autonomous agents with the capacity of moral decision-making. While some approaches provide implementations in Logic Programming (LP) systems, they have not exploited LP-based reasoning features that appear essential for moral reasoning. This PhD thesis aims at investigating further the appropriateness of LP, notably a combination of LP-based reasoning features, including techniques available in LP systems, to machine ethics. Moral facets, as studied in moral philosophy and psychology, that are amenable to computational modeling are identified, and mapped to appropriate LP concepts for representing and reasoning about them. The main contributions of the thesis are twofold. First, novel approaches are proposed for employing tabling in contextual abduction and updating – individually and combined – plus a LP approach of counterfactual reasoning; the latter being implemented on top of the aforementioned combined abduction and updating technique with tabling. They are all important to model various issues of the aforementioned moral facets. Second, a variety of LP-based reasoning features are applied to model the identified moral facets, through moral examples taken off-the-shelf from the morality literature. These applications include: (1) Modeling moral permissibility according to the Doctrines of Double Effect (DDE) and Triple Effect (DTE), demonstrating deontological and utilitarian judgments via integrity constraints (in abduction) and preferences over abductive scenarios; (2) Modeling moral reasoning under uncertainty of actions, via abduction and probabilistic LP; (3) Modeling moral updating (that allows other – possibly overriding – moral rules to be adopted by an agent, on top of those it currently follows) via the integration of tabling in contextual abduction and updating; and (4) Modeling moral permissibility and its justification via counterfactuals, where counterfactuals are used for formulating DDE.Fundação para a Ciência e a Tecnologia (FCT)-grant SFRH/BD/72795/2010 ; CENTRIA and DI/FCT/UNL for the supplementary fundin

On Automating the Doctrine of Double Effect

The doctrine of double effect ($\mathcal{DDE}$) is a long-studied ethical principle that governs when actions that have both positive and negative effects are to be allowed. The goal in this paper is to automate $\mathcal{DDE}$. We briefly present $\mathcal{DDE}$, and use a first-order modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine. We present formalizations of increasingly stronger versions of the principle, including what is known as the doctrine of triple effect. We then use our framework to simulate successfully scenarios that have been used to test for the presence of the principle in human subjects. Our framework can be used in two different modes: One can use it to build $\mathcal{DDE}$-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is $\mathcal{DDE}$-compliant, by applying a $\mathcal{DDE}$ layer on an existing system or model. For the latter mode, the underlying AI system can be built using any architecture (planners, deep neural networks, bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes a few parameters in its model, such verification is possible. The role of the $\mathcal{DDE}$ layer here is akin to a (dynamic or static) software verifier that examines existing software modules. Finally, we end by presenting initial work on how one can apply our $\mathcal{DDE}$ layer to the STRIPS-style planning model, and to a modified POMDP model.This is preliminary work to illustrate the feasibility of the second mode, and we hope that our initial sketches can be useful for other researchers in incorporating DDE in their own frameworks.Comment: 26th International Joint Conference on Artificial Intelligence 2017; Special Track on AI & Autonom

Employing AI to Better Understand Our Morals

We present a summary of research that we have conducted employing AI to better understand human morality. This summary adumbrates theoretical fundamentals and considers how to regulate development of powerful new AI technologies. The latter research aim is benevolent AI, with fair distribution of benefits associated with the development of these and related technologies, avoiding disparities of power and wealth due to unregulated competition. Our approach avoids statistical models employed in other approaches to solve moral dilemmas, because these are “blind” to natural constraints on moral agents, and risk perpetuating mistakes. Instead, our approach employs, for instance, psychologically realistic counterfactual reasoning in group dynamics. The present paper reviews studies involving factors fundamental to human moral motivation, including egoism vs. altruism, commitment vs. defaulting, guilt vs. non-guilt, apology plus forgiveness, counterfactual collaboration, among other factors fundamental in the motivation of moral action. These being basic elements in most moral systems, our studies deliver generalizable conclusions that inform efforts to achieve greater sustainability and global benefit, regardless of cultural specificities in constituents

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

Moral Programming: Crafting a flexible heuristic moral meta-model for meaningful AI control in pluralistic societies

Artificial Intelligence (AI) permeates more and more application domains. Its progress regarding scale, speed, and scope magnifies potential societal benefits but also ethically and safety relevant risks. Hence, it becomes vital to seek a meaningful control of present-day AI systems (i.e. tools). For this purpose, one can aim at counterbalancing the increasing problem-solving ability of AI with boundary conditions core to human morality. However, a major problem is that morality exists in a context-sensitive steadily shifting explanatory sphere co-created by humans using natural language – which is inherently ambiguous at multiple levels and neither machine-understandable nor machine-readable. A related problem is what we call epistemic dizziness, a phenomenon linked to the inevitable circumstance that one could always be wrong. Yet, while universal doubt cannot be eliminated from morality, it need not be magnified if the potential/requirement for steady refinements is anticipated by design. Thereby, morality pertains to the set of norms and values enacted at the level of a society, other not nearer specified collectives of persons, or at the level of an individual. Norms are instrumental in attaining the fulfilment of values, the latter being an umbrella term for all that seems decisive for distinctions between right and wrong – a central object of study in ethics. In short, for a meaningful control of AI against the background of the changing contextsensitive and linguistically moulded nature of human morality, it is helpful to craft descriptive and thus sufficiently flexible AI-readable heuristic models of morality. In this way, the problem-solving ability of AI could be efficiently funnelled through these updatable models so as to ideally boost the benefits and mitigate the risks at the AI deployment stage with the conceivable side-effect of improving human moral conjectures. For this purpose, we introduced a novel transdisciplinary framework denoted augmented utilitarianism (AU) (Aliman and Kester, 2019b), which is formulated from a meta-ethical stance. AU attempts to support the human-centred task to harness human norms and values to explicitly and traceably steer AI before humans themselves get unwittingly and unintelligibly steered by the obscurity of AI’s deployment. Importantly, AU is descriptive, non-normative, and explanatory (Aliman, 2020), and is not to be confused with normative utilitarianism. (While normative ethics pertains to ‘what one ought to do’, descriptive ethics relates to empirical studies on human ethical decision-making.) This chapter offers the reader a compact overview of how AU coalesces elements from AI, moral psychology, cognitive and affective science, mathematics, systems engineering, cybernetics, and epistemology to craft a generic scaffold able to heuristically encode given moral frameworks in a machine-readable form. We thematise novel insights and also caveats linked to advanced AI risks yielding incentives for future work

Epistemic Mentalizing and Causal Cognition Across Agents and Objects

This dissertation examines mentalizing abilities, causal reasoning, and the interactions thereof. Minds are so much more than false beliefs, yet much of the existing research on mentalizing has placed a disproportionately large emphasis on this one aspect of mental life. The first aim of this dissertation is to examine whether representing others’ knowledge states relies on more fundamentally basic cognitive processes than representations of their mere beliefs. Using a mixture of behavioral and brain measures across five experiments, I find evidence that we can represent others\u27 knowledge quicker and using fewer neural resources than when representing others’ beliefs. To be considered a representation of knowledge rather than belief, both mentalizer and mentalizee must accept the propositional content being represented as factive (Kiparsky & Kiparsky, 2014; Williamson, 2002). As such, my results suggest that representing the mental states of others may be cognitively easier when the content of which does not need to be decoupled from one’s own existing view of reality. Our perception of other minds can influence how we attribute causality for certain events. The second aim of this dissertation is to explore how perceptions of agency and prescriptive social norms influence our intuitions of how agents and objects cause events in the world. Using physics simulations and 3D anthropomorphic stimuli, the results of three experiments show that agency, itself, does not make agents more causal to an outcome than objects. Instead, causal judgments about agents and objects differ as a function of the counterfactuals they respectively afford. Furthermore, I show that what distinguishes the counterfactuals we use to make causal attributions to agents and objects are the prescriptions we hold for how they should behave. Why do we say a fire occurred because of a lightning strike, rather than the necessary presence of oxygen? The answer involves our normative expectations of the probability of lightning strikes and the relative ubiquity of oxygen (Icard et al., 2017). The third aim of this dissertation explores the gradation of causal judgments across multiple contributing events that each vary in their statistical probability. I contribute to ongoing theoretical debates about how humans select causes in experimental philosophy and cognitive science by introducing a publicly available dataset containing 47,970 causal attribution ratings collected from 1,599 adult participants and structured around four novel configurations of causal relationships. By quantitatively manipulating the influence of normality, I systematically explore the continuous space of a causal event’s probability from unlikely to certain. It is my hope that this benchmark dataset may serve as a growing testbed for diverging theoretical models proposing to characterize how humans answer the question: Why

The role of robotics and AI in technologically mediated human evolution: a constructive proposal

This paper proposes that existing computational modeling research programs may be combined into platforms for the information of public policy. The main idea is that computational models at select levels of organization may be integrated in natural terms describing biological cognition, thereby normalizing a platform for predictive simulations able to account for both human and environmental costs associated with different action plans and institutional arrangements over short and long time spans while minimizing computational requirements. Building from established research programs, the proposal aims to take advantage of current momentum in the direction of the integration of the cognitive with social and natural sciences, reduce start-up costs and increase speed of development. These are all important upshots given rising unease over the potential for AI and related technologies to shape the world going forward