Marten H. L. Kaas

There is a growing need to ensure that autonomous artificially intelligent (AI) systems are capable of behaving ethically, and I argue that virtue ethics, but in particular the normative theory of aretaic-exemplarism, can play a central role in cultivating the ethical behavior of machines. When coupled with the value inherent in and commonplace practice of training AI systems using simulated environments, it may be possible to raise ethical machines by training them to imitate simulated exemplars of moral excellence, like a digital Jesus or virtual Confucius. This bottom-up approach to implementing machine ethics has advantages over top-down approaches and is similarly not beset by some of the challenges that arise when attempting to use real people as the imitable training set. In short, machines may be able to imitate simulated moral exemplars and thereby exhibit virtuous behavior themselves.

In this chapter, we integrate three distinct strands of thought to argue that the concept of “fairness” varies significantly across cultures. As a result, ensuring that human-AI interactions meet relevant fairness standards requires a deep understanding of the cultural contexts in which AI-enabled systems are deployed. Failure to do so will not only result in the generation of unfair outcomes by an AI-enabled system, but it will also degrade legitimacy of and trust in the system. The first strand concerns the dominant approach taken in the technology industry to ensure that AI-enabled systems are fair. This approach is to reduce fairness to some mathematical formalism that can be applied universally, a quintessentially Western conception of fairness. The second strand concerns alternative conceptions of fairness that have their roots in philosophical traditions of the East, namely Confucian virtue ethics. Understanding how individuals from diverse cultural backgrounds perceive fairness—particularly their beliefs about fairness in human-to-human interactions—is crucial for understanding how they will interpret fairness in human-AI interactions. Building on these philosophical and behavioral differences, as highlighted by empirical research, the third strand integrates insights from political science and interdisciplinary studies. This perspective offers valuable guidance on designing AI-enabled systems to align with contextually relevant standards of fairness. Examining existing beliefs about fairness within the context of institutional decision-making provides valuable insights into what people expect from AI-generated decisions. These expectations often include key elements such as a sufficient degree of transparency, clear lines of accountability, and mechanisms to contest decisions made by the system—all of which are essential components of procedural fairness. Rather than adopting a one-size-fits-all approach to ensuring fairness, the design and deployment of AI systems must carefully account for the operating environment, including the socio-political and cultural context, to ensure that the system aligns with the relevant standards of fairness.

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 Brian 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.

In this paper we argue that safety claims, when justified by a safety case, are descriptive fallible knowledge claims. Even if the aim of a safety case was to justify infallible knowledge about the safety of a system, such infallible safety knowledge is impossible to attain in the case of AI-enabled systems. By their nature AI-enabled systems preclude the possibility of obtaining infallible knowledge concerning their safety or lack thereof. We suggest that one can communicate knowledge of an AI-enabled system’s safety by structuring their exchange according to Paul Grice’s Cooperative Principle which can be achieved via adherence to the Gricean maxims of communication. Furthermore, these same maxims can be used to evaluate the calibre of the exchange, with the aim being to ensure that communicating knowledge about an AI-enabled system’s safety is of the highest calibre, in short, that the communication is relevant, of sufficient quantity and quality, and communicated perspicuously. The high calibre communication of safety claims to an epistemically diverse group of stakeholders is vitally important given the increasingly participatory nature of AI-enabled system design, development and assessment.

The deployment and use of AI systems should be both safe and broadly ethically acceptable. The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape that seeks to support and achieve that aim. The purpose of this argument pattern or framework is to structure reasoning about, and to communicate and foster confidence in, the ethical acceptability of uses of specific real-world AI systems in complex socio-technical contexts. This paper presents the interim findings of a case study applying this ethics assurance framework to the use of Dora, an AI-based telemedicine system, to assess its viability and usefulness as an approach. The case study process to date has revealed some of the positive ethical impacts of the Dora platform, as well as unexpected insights and areas to prioritise for evaluation, such as risks to the frontline clinician, particularly in respect of clinician autonomy. The ethics assurance argument pattern offers a practical framework not just for identifying issues to be addressed, but also to start to construct solutions in the form of adjustments to the distribution of benefits, risks and constraints on human autonomy that could reduce ethical disparities across affected stakeholders. Though many challenges remain, this research represents a step in the direction towards the development and use of safe and ethically acceptable AI systems and, ideally, a shift towards more comprehensive and inclusive evaluations of AI systems in general.

An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be acceptably safe within its intended context. Assurance cases often inform third party approval of a system. One emerging proposition within the trustworthy AI and autonomous systems (AI/AS) research community is to use assurance cases to instil justified confidence that specific AI/AS will be ethically acceptable when operational in well-defined contexts. This paper substantially develops the proposition and makes it concrete. It brings together the assurance case methodology with a set of ethical principles to structure a principles-based ethics assurance argument pattern. The principles are justice, beneficence, non-maleficence, and respect for human autonomy, with the principle of transparency playing a supporting role. The argument pattern—shortened to the acronym PRAISE—is described. The objective of the proposed PRAISE argument pattern is to provide a reusable template for individual ethics assurance cases, by which engineers, developers, operators, or regulators could justify, communicate, or challenge a claim about the overall ethical acceptability of the use of a specific AI/AS in a given socio-technical context. We apply the pattern to the hypothetical use case of an autonomous ‘robo-taxi’ service in a city centre.

Among the many common criticisms of the Turing test, a valid criticism concerns its scope. Intelligence is a complex and multi-dimensional phenomenon that will require testing using as many different formats as possible. The Turing test continues to be valuable as a source of evidence to support the inductive inference that a machine possesses a certain kind of intelligence and when interpreted as providing a behavioural test for a certain kind of intelligence. This paper raises the novel criticism that the Turing test represents an example of Goodhart’s Law operating in the field of artificial intelligence. As one measure towards the goal of creating genuinely intelligent machines, the Turing test must not be confused with the goal itself. Moreover, the Turing test ought to be augmented such that through its use additional evidence could be secured to support the strong inference that a machine, were it to pass the Turing Test, could think like a human.

The ethical decision-making and behaviour of artificially intelligent systems is increasingly important given the prevalence of these systems and the impact they can have on human well-being. Many current approaches to implementing machine ethics utilize top-down approaches, that is, ensuring the ethical decision-making and behaviour of an agent via its adherence to explicitly defined ethical rules or principles. Despite the attractiveness of this approach, this chapter explores how all top-down approaches to implementing machine ethics are fundamentally limited and how bottom-up approaches, in particular, reinforcement learning methods, are not beset by the same problems as top-down approaches. Bottom-up approaches possess significant advantages that make them better suited for implementing machine ethics.