Research into the ethics of artificial intelligence is often categorized into two subareas—robot ethics and machine ethics. Many of the definitions and classifications of the subject matter of these subfields, as found in the literature, are conflated, which I seek to rectify. In this essay, I infer that using the term ‘machine ethics’ is too broad and glosses over issues that the term computational ethics best describes. I show that the subject of inquiry of computational ethics is of great value and indeed is an important frontier in developing ethical artificial intelligence systems (AIS). I also show that computational is a distinct, often neglected field in the ethics of AI. In contrast to much of the literature, I argue that the appellation ‘machine ethics’ does not sufficiently capture the entire project of embedding ethics into AI/S, and hence the need for computational ethics. This essay is unique for two reasons; first, it offers a philosophical analysis of the subject of computational ethics that is not found in the literature. Second, it offers a finely grained analysis that shows the thematic distinction among robot ethics, machine ethics and computational ethics.
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As is well known, Asimov’s three laws of robotics are as follows: 1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. 3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. The fourth law, which is also referred to as the zeroth law states that a robot may not harm humanity, or, by inaction, allow humanity to come to harm.
Clarke identifies certain constraints to Asimov’s laws of robotics, which would make it computationally difficult to implement. These are the ambiguity and cultural dependence of terms used in the formulation of the laws; the role of judgment in decision-making, which would be quite tricky to implement given the degree of programming required in decision-making; the sheer complexity, this also bothers on having to account for all possible scenarios; the scope for dilemma and deadlock, the robot autonomy, audit of robot compliance, and scope of adaptation.
Smith and Anderson in the 2014 published Pew Research titled “AI, Robotics, and the Future of Jobs”, discuss the economic and social impact of AI on society. As we continue to build more autonomous intelligent systems, we are likely to delegate responsibilities around security, environment, healthcare, food production etc. to these systems. These all raise concerns about the impact of AI on jobs and society.
Closely linked to the moral issue with AI is the debate around its legal status, agency, and responsibility. With the imminent disruption in the transport sector by the introduction of self-driving cars, questions around who bears responsibility for harm caused by a self-driving car comes to mind. Also, there are more technical questions around insurance and liabilities that have to be addressed Chopra and White (2011).
In much of the literature, Asmivo is unarguably seen as a forerunner in the development of guidelines to regulate the operations of autonomous intelligent systems.
We might consider, for instance, a desktop printer as a machine but it is uniquely different from a self-driving car, which can also be said to be a machine. The difference here is the degree of autonomy of these systems and the attendant moral burden they carry. The actions of the printer may have a moral impact; an example is if it is used to print documents for whistleblowing activities. On the other hand, a self-driving car appears to carry a greater ethical burden because it is active in the moral decision-making process. As Lumbreras (2017) mentions, the goal of machine ethics is ultimately to ‘endow’ self-governing systems with ethical comportments. In the case above, a desktop printer would not count as ‘self-governing’ but a self-driving car would.
Putting Moor’s alongside Asaro’s classification, amoral agents are those I have identified as ethical impact agents. Systems with moral significance are represented as implicit moral agents. Explicit moral agents are systems with dynamic moral intelligence that can make moral decisions while employing moral principles explicitly. The final type of moral agent identified by Moore is the full ethical agent, which shares human-like properties.
In explicating the importance of these criteria, Floridi and Sanders note: “(a) Interactivity means that the agent and its environment (can) act upon each other… (b) Autonomy means that the agent is able to change state without direct response to interaction: it can perform internal transitions to change its state. So an agent must have at least two states. This property imbues an agent with a certain degree of complexity and decoupled-ness from its environment. (c) Adaptability means that the agent’s interactions (can) change the transition rules by which it changes state. This property ensures that an agent might be viewed, at the given LoA, as learning its own mode of operation in a way, which depends critically on its experience” (Floridi and Sanders 2004, p. 7).
In answering the question of how to go about the embedding of ethical principles into AIS, it behoves machine ethicists to decide on the best approaches to use. So far, three approaches standout, top-down, bottom-up and hybrid. In the top-down approach, an ethical principle is selected and applied in a theoretical form to the AIS using a rule-based method such as Asimov’s three laws of robots (Allen et al. 2005). The bottom-up approach, on the other hand, does not refer to any particular ethical principle; instead, through machine learning, these intelligent systems can learn subsets of ethical principles and over time integrate these into a whole and possibly unique ethical system (Wallach and Allen 2008). Then there is the hybrid approach, which simply is the fusion of the two approaches.
Parthmore and Whitby make a strong case for why embodiment constitutes an important aspect in the project to build artificial moral agents. This is because embodiment appeals to the human tendency to relate and nurture, and does so regardless of the form these systems come in—biological or synthetic. Usually, we tend to care for things we anthropomorphise.
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I would like to thank Prof Thaddeus Metz and Prof Emma Ruttkamp-Bloem, who both took the time to read the initial drafts of this paper and made significant comments and suggestions. I would also like to thank my research group members at the Centre for Artificial Intelligence Research, University of Pretoria.
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Segun, S.T. From machine ethics to computational ethics. AI & Soc 36, 263–276 (2021). https://doi.org/10.1007/s00146-020-01010-1