Ethicists, policy-makers, and the general public have questioned whether artificial entities such as robots warrant rights or other forms of moral consideration. There is little synthesis of the research on this topic so far. We identify 294 relevant research or discussion items in our literature review of this topic. There is widespread agreement among scholars that some artificial entities could warrant moral consideration in the future, if not also the present. The reasoning varies, such as concern for the effects on (...) artificial entities and concern for the effects on human society. Beyond the conventional consequentialist, deontological, and virtue ethicist ethical frameworks, some scholars encourage “information ethics” and “social-relational” approaches, though there are opportunities for more in-depth ethical research on the nuances of moral consideration of artificial entities. There is limited relevant empirical data collection, primarily in a few psychological studies on current moral and social attitudes of humans towards robots and other artificial entities. This suggests an important gap for psychological, sociological, economic, and organizational research on how artificial entities will be integrated into society and the factors that will determine how the interests of artificial entities are considered. (shrink)

In philosophy of mind, zombies are imaginary creatures that are exact physical duplicates of conscious subjects but for whom there is no first-personal experience. Zombies are meant to show that physicalism—the theory that the universe is made up entirely out of physical components—is false. In this paper, I apply the zombie thought experiment to the realm of morality to assess whether moral agency is something independent from sentience. Algorithms, I argue, are a kind of functional moral zombie, such that thinking (...) about the latter can help us better understand and regulate the former. I contend that the main reason why algorithms can be neither autonomous nor accountable is that they lack sentience. Moral zombies and algorithms are incoherent as moral agents because they lack the necessary moral understanding to be morally responsible. To understand what it means to inflict pain on someone, it is necessary to have experiential knowledge of pain. At most, for an algorithm that feels nothing, ‘values’ will be items on a list, possibly prioritised in a certain way according to a number that represents weightiness. But entities that do not feel cannot value, and beings that do not value cannot act for moral reasons. (shrink)

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody (...) certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

Deep learning AI systems have proven a wide capacity to take over human-related activities such as car driving, medical diagnosing, or elderly care, often displaying behaviour with unpredictable consequences, including negative ones. This has raised the question whether highly autonomous AI may qualify as morally responsible agents. In this article, we develop a set of four conditions that an entity needs to meet in order to be ascribed moral responsibility, by drawing on Aristotelian ethics and contemporary philosophical research. We encode (...) these conditions and generate a flowchart that we call the Moral Responsibility Test. This test can be used as a tool both to evaluate whether an entity is a morally responsible agent and to inform human moral decision-making over the influencing variables of the context of action. We apply the test to the case of Artificial Moral Advisors and conclude that this form of AI cannot qualify as morally responsible agents. We further discuss the implications for the use of AMAs as moral enhancement and show that using AMAs to offload human responsibility is inadequate. We argue instead that AMAs could morally enhance users if they are interpreted as enablers for moral knowledge of the contextual variables surrounding human moral decision-making, with the implication that such a use might actually enlarge human moral responsibility. (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)

I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value (...) alignment problem in AI. (shrink)

In the present article, I will advocate caution against developing artificial moral agents based on the notion that the utilization of preliminary forms of AMAs will potentially negatively feed back on the human social system and on human moral thought itself and its value—e.g., by reinforcing social inequalities, diminishing the breadth of employed ethical arguments and the value of character. While scientific investigations into AMAs pose no direct significant threat, I will argue against their premature utilization for practical and economical (...) use. I will base my arguments on two thought experiments. The first thought experiment deals with the potential to generate a replica of an individual’s moral stances with the purpose to increase, what I term, ’moral efficiency’. Hence, as a first risk, an unregulated utilization of premature AMAs in a neoliberal capitalist system is likely to disadvantage those who cannot afford ’moral replicas’ and further reinforce social inequalities. The second thought experiment deals with the idea of a ’moral calculator’. As a second risk, I will argue that, even as a device equally accessible to all and aimed at augmenting human moral deliberation, ’moral calculators’ as preliminary forms of AMAs are likely to diminish the breadth and depth of concepts employed in moral arguments. Again, I base this claim on the idea that the current most dominant economic system rewards increases in productivity. However, increases in efficiency will mostly stem from relying on the outputs of ’moral calculators’ without further scrutiny. Premature AMAs will cover only a limited scope of moral argumentation and, hence, over-reliance on them will narrow human moral thought. In addition and as the third risk, I will argue that an increased disregard of the interior of a moral agent may ensue—a trend that can already be observed in the literature. (shrink)

Social robotics are autonomous robots or Artificial Moral Agents (AMA), that will interact respect and embody human ethical values. However, the conceptual and practical problems of building such systems have not yet been resolved, playing a role of significant challenge for computational modeling. It seems that the lack of success in constructing robots, ceteris paribus, is due to the conceptual and algorithmic limitations of the current design of ethical robots. This paper proposes a new approach for developing ethical capacities in (...) robotic systems, one based on the concept of Aristotelian phronesis. Phronesis in principle reflexes closer human ethics than the ethical paradigms we employ today in ethical robotics. This paper describes the essential features of phronesis and proposes a high-level architecture for implementing phronetic principles in autonomous robots. Phronetic robotics is in its early stages of conceptualization, so many of the presented ideas are speculative and require further research.2. (shrink)

Artificially intelligent machines are different in kind from all previous machines and tools. While many are used for relatively benign purposes, the types of artificially intelligent machines that we should care about, the ones that are worth focusing on, are the machines that purport to replace humans entirely and thereby engage in what Brain Cantwell Smith calls “judgment.” As impressive as artificially intelligent machines are, their abilities are still derived from humans and as such lack the sort of normative commitments (...) that humans have. So while artificially intelligent machines possess a great capacity for “reckoning,” to use Smith’s terminology, i.e., a calculative prowess of extraordinary utility and importance, they still lack the kind of considered human judgment that accompanies the ethical commitment and responsible action we humans must ultimately aspire toward. But there is a perfect technological storm brewing. Artificially intelligent machines are analogous to a perfect storm in that such machines involve the convergence of a number of factors that threaten our ability to behave ethically and maintain meaningful human control over the outcomes of processes involving artificial intelligence. I argue that the storm in the context of artificially intelligent machines makes us vulnerable to moral complacency. That is, this perfect technological storm is capable of lulling people into a state in which they abdicate responsibility for decision-making and behaviour precipitated by the use of artificially intelligent machines, a state that I am calling “moral complacency.” I focus on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control. The first problem is that of transparency/opacity. The second problem is that of overtrust in machines, often referred to as the automation bias. The third problem is that of ascribing responsibility. I examine each of these problems and how together they threaten to render us morally complacent. (shrink)