Thomas Arnold

As a way to address both ominous and ordinary threats of artificial intelligence, researchers have started proposing ways to stop an AI system before it has a chance to escape outside control and cause harm. A so-called “big red button” would enable human operators to interrupt or divert a system while preventing the system from learning that such an intervention is a threat. Though an emergency button for AI seems to make intuitive sense, that approach ultimately concentrates on the point when a system has already “gone rogue” and seeks to obstruct interference. A better approach would be to make ongoing self-evaluation and testing an integral part of a system’s operation, diagnose how the system is in error and to prevent chaos and risk before they start. In this paper, we describe the demands that recent big red button proposals have not addressed, and we offer a preliminary model of an approach that could better meet them. We argue for an ethical core that consists of a scenario-generation mechanism and a simulation environment that are used to test a system’s decisions in simulated worlds, rather than the real world. This EC would be kept opaque to the system itself: through careful design of memory and the character of the scenario, the system’s algorithms would be prevented from learning about its operation and its function, and ultimately its presence. By monitoring and checking for deviant behavior, we conclude, a continual testing approach will be far more effective, responsive, and vigilant toward a system’s learning and action in the world than an emergency button which one might not get to push in time.

This paper argues against the moral Turing test as a framework for evaluating the moral performance of autonomous systems. Though the term has been carefully introduced, considered, and cautioned about in previous discussions :251–261, 2000; Allen and Wallach 2009), it has lingered on as a touchstone for developing computational approaches to moral reasoning :98–109, 2015). While these efforts have not led to the detailed development of an MTT, they nonetheless retain the idea to discuss what kinds of action and reasoning should be demanded of autonomous systems. We explore the flawed basis of an MTT in imitation, even one based on scenarios of morally accountable actions. MTT-based evaluations are vulnerable to deception, inadequate reasoning, and inferior moral performance vis a vis a system’s capabilities. We propose verification—which demands the design of transparent, accountable processes of reasoning that reliably prefigure the performance of autonomous systems—serves as a superior framework for both designer and system alike. As autonomous social robots in particular take on an increasing range of critical roles within society, we conclude that verification offers an essential, albeit challenging, moral measure of their design and performance.

This paper addresses ethical challenges posed by a robot acting as both a general type of system and a discrete, particular machine. Using the philosophical distinction between “type” and “token,” we locate type-token ambiguity within a larger field of indefinite robotic identity, which can include networked systems or multiple bodies under a single control system. The paper explores three specific areas where the type-token tension might affect human–robot interaction, including how a robot demonstrates the highly personalized recounting of information, how a robot makes moral appeals and justifies its decisions, and how the possible need for replacement of a particular robot shapes its ongoing role. We also consider how a robot might regard itself as a replaceable token of a general robotic type and take extraordinary actions on that basis. For human–robot interaction robotic type-token identity is not an ontological problem that has a single solution, but a range of possible interactions that responsible design must take into account, given how people stand to gain and lose from the shifting identities social robots will present.

Recent scholarship has shed more light on the relationship between Husserl and Lotze. And Husserl indeed claims of Lotze that “his inspired interpretation of the Platonic doctrine of Forms […] put up a bright first light and determined all further studies” ( 2002a, 297). In this paper I will try to answer the question what exactly Husserl saw in this “bright light”—the answer being much more complicated than “Platonism.” As I will show, Lotze misreads Plato, but in interesting ways, and Husserl in turn misreads Lotze. In other words, this paper is about a misreading of a misreading—yet one fundamental for the development of phenomenology in that Husserl’s engagement with Lotze enabled him (a) to expand and differentiate his own anti-reductionism in regard to ideal entities, leading him to two forms of what he calls “Platonism,” one of which even runs contrary to Lotze’s own account, and (b) to conceive of a method to gain truths about the material apriori. After a brief introduction, I will discuss (1) Lotze’s interpretation of Plato, (2) what Husserl took from it, and finally (3) Plato’s Platonism, to substantiate my claims 1.

Husserl beruft sich immer wieder programmatisch auf Platon als den Gründervater der europäischen Philosophie, arbeitet jedoch die Bezüge der Phänomenologie zum Platonismus nie auf - obwohl er die "historische Rückbesinnung" auf die Urstiftung seines Denkens als wesentlichen Bestandteil der "Selbstbesinnung auf ein Selbstverständnis dessen hin, worauf man eigentlich hinaus will, als der man ist, als historisches Wesen" charakterisiert. Die vorliegende Arbeit will diese Reflexion leisten. Ihr Gegenstand ist mithin die Transformation Platonischer Gedanken in Husserls Phänomenologie. Dabei werden sechs Problemgebiete thematisiert: A) Philosophie als Wissenschaft, B) Philosophie als Ethik, C) Philosophie als Polemik, D) Philosophie und das Ende der Lebenswelt, E) Philosophie und das Eigenrecht des Eidetischen, F) Philosophie und das Absolute. Den Abschnitten folgen jeweils kritische Erwägungen zu den Spannungen, die vor dem Platonischen Hintergrund innerhalb der Phänomenologie sichtbar werden. Die Arbeit zeigt, dass die Phänomenologie tatsächlich als eine eigenwillige Endstiftung des Platonismus betrachtet werden kann, wodurch sich eine neue Perspektive auf ihre programmatische und systematische Grundkonfiguration ergibt.

Habit and habitualisation play an important role in Husserl's phenomenology, yet one aspect of habituality has been somewhat overlooked, namely the dimension of authenticity/inauthenticity. While authenticity in Heidegger has received a lot of attention, inauthenticity in Husserl is less well researched, although, as I will show, it is of equal importance to his overall theorising. The central aim of this paper is to explore the authenticity/inauthenticity-distinction in the various domains of habitualisation and to establish its fundamental importance for Husserl. In the first, introductory part of this paper I offer a few remarks on how to understand and categorise Husserl's transcendental take on habits; I also introduce the important distinction between authentic and inauthentic habits and authenticity and inauthenticity in general. In the second part I present the problem of mechanisation as a form of inauthentic habitualisation as it appears in Husserl's phenomenologies of ethics and, more importantly, science. The third part concludes the paper with a reflection on how phenomenology is programmatically aimed against any form of inauthenticity, and on how it might or might not achieve this goal.

Explainability has emerged as a critical AI research objective, but the breadth of proposed methods and application domains suggest that criteria for explanation vary greatly. In particular, what counts as a good explanation, and what kinds of explanation are computationally feasible, has become trickier in light of oqaque “black box” systems such as deep neural networks. Explanation in such cases has drifted from what many philosophers stipulated as having to involve deductive and causal principles to mere “interpretation,” which approximates what happened in the target system to varying degrees. However, such post hoc constructed rationalizations are highly problematic for social robots that operate interactively in spaces shared with humans. For in such social contexts, explanations of behavior, and, in particular, justifications for violations of expected behavior, should make reference to socially accepted principles and norms. In this article, we show how a social robot’s actions can face explanatory demands for how it came to act on its decision, what goals, tasks, or purposes its design had those actions pursue and what norms or social constraints the system recognizes in the course of its action. As a result, we argue that explanations for social robots will need to be accurate representations of the system’s operation along causal, purposive, and justificatory lines. These explanations will need to generate appropriate references to principles and norms—explanations based on mere “interpretability” will ultimately fail to connect the robot’s behaviors to its appropriate determinants. We then lay out the foundations for a cognitive robotic architecture for HRI, together with particular component algorithms, for generating explanations and engaging in justificatory dialogues with human interactants. Such explanations track the robot’s actual decision-making and behavior, which themselves are determined by normative principles the robot can describe and use for justifications.

Soft robots promise an exciting design trajectory in the field of robotics and human–robot interaction (HRI), promising more adaptive, resilient movement within environments as well as a safer, more sensitive interface for the objects or agents the robot encounters. In particular, tactile HRI is a critical dimension for designers to consider, especially given the onrush of assistive and companion robots into our society. In this article, we propose to surface an important set of ethical challenges for the field of soft robotics to meet. Tactile HRI strongly suggests that soft-bodied robots balance tactile engagement against emotional manipulation, model intimacy on the bonding with a tool not with a person, and deflect users from personally and socially destructive behavior the soft bodies and surfaces could normally entice.