Scott Robbins

Across academia, industry, and government, “AI” has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how “AI” may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally “AI”, a broad term admitting many (types of) systems used for a variety of tasks and each coming with its own set of limitations, challenges, and potential use cases. In this article, we focus on the military domain as a case study and present both a loose enumerative taxonomy of systems captured under the umbrella term “military AI”, as well as discussion of the challenges of each. In doing so, we highlight that critiques of one (type of) system will not always transfer to other (types of) systems. Building on this, we argue that in order for debates to move forward fruitfully, it is imperative that the discussions be made more precise and that “AI” be excised from debates to the extent possible, especially as discussions move more toward discrete systems and use cases. Researchers, developers, and policy-makers should make clear exactly what systems they have in mind and what possible benefits and risks attend the deployment of those particular systems. While we focus on AI in the military as an exemplar for the overall trends in discussions of “AI”, the argument’s conclusions are broad and have import for discussions of AI across a host of domains.

Across academia, industry, and government, "AI" has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how "AI" may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally "AI", a broad term admitting many (types of) systems used for a variety of tasks and each coming with its own set of limitations, challenges, and potential use cases. In this article, we focus on the military domain as a case study and present both a loose enumerative taxonomy of systems captured under the umbrella term "military AI", as well as discussion of the challenges of each. In doing so, we highlight that critiques of one (type of) system will not always transfer to other (types of) systems. Building on this, we argue that in order for debates to move forward fruitfully, it is imperative that the discussions be made more precise and that "AI" be excised from debates to the extent possible. Researchers, developers, and policy-makers should make clear exactly what systems they have in mind and what possible benefits and risks attend the deployment of those particular systems. While we focus on AI in the military as an exemplar for the overall trends in discussions of "AI", the argument's conclusions are broad and have import for discussions of AI across a host of domains.

Opponents of digital sovereignty characterize the methods to achieve it as authoritarian, protectionist, and anti-innovation – ultimately leading to digital fragmentation. Digital fragmentation, roughly, is the idea that “the internet is in some danger of splintering into loosely coupled islands of connectivity.” The internet is built on, so the argument goes, the foundation of open accessibility, free movement of data and interoperability. The exercise of digital sovereignty, it is claimed, chips away at this foundation – and threatens to fragment the internet as we know it. The argument against digital sovereignty is largely premised on an assumption that is in no way obviously true: the way the internet is benefits individuals. The current functioning of internet services requires consumers to give up large amounts of sensitive personal information that are used to generate targeted advertisements. These targeted advertisements have been associated with election tampering as well as a driving force in the dissemination of conspiracy theories leading to genocide, an armed insurrection, and making it much more difficult to get the world vaccinated. Opponents of digital sovereignty may be correct in their analysis that, in the current context, methods to achieve it will cause the internet to somewhat fragment. However, too often these opponents seem to defend a status quo that is fundamentally broken and dominated by a few companies which have time and again abused their power. Until the world agrees upon rules which protect privacy and ensure the functioning of democracy, the states must accept the trade of a fragmented internet for the protection of their citizens.

This paper examines the implications of generative AI (GenAI) emulating human expression, i.e. human communication and human creative expression. While GenAI seems to offer benefits such as increased efficiency and productivity, its use raises significant practical and conceptual concerns: GenAI comes with the increased efforts of prompting, verification and editing, and causes the deskilling of its users. It also comes at a monetary cost and causes various ethical issues e.g., a lack of authenticity. We further show that GenAI’s fundamental issue is that it is by design not able to output human expression but only human-like expression. Using AI for tasks that are fundamentally about communicating is replacing communication with something that is not communication. Finally, we show that the consequences of the use of GenAI cannot be avoided on an individual level by those individuals avoiding the use of GenAI and will necessarily lead to an erosion of human expression in general. This is because GenAI: will lead to a distrust in human expression when the authenticity of authorship over time becomes unclear; will cause a devaluation of human expression when human expression can be mimicked with less effort by GenAI; and, will discourage human expression altogether when GenAI has set the bar too high.

This paper examines the implications of generative AI (GenAI) emulating human expression, i.e. human communication and human creative expression. While GenAI seems to offer benefits such as increased efficiency and productivity, its use raises significant practical and conceptual concerns: GenAI comes with the increased efforts of prompting, verification and editing, and causes the deskilling of its users. It also comes at a monetary cost and causes various ethical issues e.g., a lack of authenticity. We further show that GenAI’s fundamental issue is that it is by design not able to output human expression but only human-like expression. Using AI for tasks that are fundamentally about communicating is replacing communication with something that is not communication. Finally, we show that the consequences of the use of GenAI cannot be avoided on an individual level by those individuals avoiding the use of GenAI and will necessarily lead to an erosion of human expression in general. This is because GenAI: will lead to a distrust in human expression when the authenticity of authorship over time becomes unclear; will cause a devaluation of human expression when human expression can be mimicked with less effort by GenAI; and, will discourage human expression altogether when GenAI has set the bar too high.

The use of terms like “collaboration” and “co-workers” to describe interactions between human beings and certain artificial intelligence (AI) systems has gained significant traction in recent years. Yet, it remains an open question whether such anthropomorphic metaphors provide either a fertile or even a purely innocuous lens through which to conceptualize designed commercial products. Rather, a respect for human dignity and the principle of transparency may require us to draw a sharp distinction between real and faux peers. At the heart of the concept of collaboration lies the assumption that the collaborating parties are (or behave as if they are) of similar status: two agents capable of comparable forms of intentional action, moral agency, or moral responsibility. In application to current AI systems, this not only seems to fail ontologically but also from a socio-political perspective. AI in the workplace is primarily an extension of capital, not of labor, and the AI “co-workers” of most individuals will likely be owned and operated by their employer. In this paper, we critically assess both the accuracy and desirability of using the term “collaboration” to describe interactions between humans and AI systems. We begin by proposing an alternative ontology of human–machine interaction, one which features not two equivalently autonomous agents, but rather one machine that exists in a relationship of heteronomy to one or more human agents. In this sense, while the machine may have a significant degree of independence concerning the means by which it achieves its ends, the ends themselves are always chosen by at least one human agent, whose interests may differ from those of the individuals interacting with the machine. We finally consider the motivations and risks inherent to the continued use of the term “collaboration,” exploring its strained relation to the concept of transparency, and consequences for the future of work.

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents (AMA). Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs (from funders like Elon Musk) coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further.

Meaningful human control (MHC) is increasingly becoming an important topic in AI ethics beyond the domain of autonomous weapons systems. MHC has been conceptualized, analyzed, and applied. However, in this article, I show how all the current attempts at realizing MHC have fallen short because we have not taken the important first step of deciding what machines should and should not be doing in the first place. We must first ensure that the output we have delegated to the machine is appropriate – only then do we have to do the work required to ensure that MHC is realized. Here, I argue that machines should not be evaluating – that is, we should not be delegating evaluative outputs to machines. This is practically important because machines that have evaluative outputs cannot be evaluated for efficacy. We can’t say how effective they are. This is ethically important because machines don’t have the moral agency required to make evaluations. Furthermore, machines should not be in a position to change our values – which they would be if they were evaluating. Finally, evaluations are judged based on the considerations used to reach a particular judgment. Contemporary AI cannot provide these justifying considerations – so we have no way of evaluating their evaluative outputs.

Recommender systems are increasingly being used for many purposes. This is creating a deeply problematic situation. Recommender systems are likely to be wrong when used for these purposes because there are distorting forces working against them. RS’s are based on past evaluative standards which will often not align with current evaluative standards. RS’s algorithms must reduce everything to computable information – which will often, in these cases, be incorrect and will leave out information that we normally consider to be important for such evaluations. The algorithms powering these RSs also must use proxies for the evaluative ‘good’. These proxies are not equal to the ‘good’ and therefore will often go off track. Finally, these algorithms are opaque. We do not have access to the considerations that lead to a particular recommendation. Without these considerations we are taking the machine’s output on faith. These algorithms also have the potential to modify how we evaluate. YouTube has modified its algorithm explicitly to ‘expand our tastes’. This is an extraordinary amount of power – and one that if my first argument goes through, is likely to take us away from the good. This influences our behavior which feeds back into the algorithms that make recommendations. It is important that we establish some meaningful human control over this process before we lose control over the evaluative.

When a person recommends a restaurant, movie or book, he or she is usually thanked for this recommendation. The person receiving the information will then evaluate, based on his or her knowledge about the situation, whether to follow the recommendation. With the rise of AI-powered recommender systems, however, restaurants, movies, books, and other items relevant for many aspects of life are generally recommended by an algorithm rather than a person. This volume aims to shed light on the implications of this transnational development from both legal and ethical perspectives and to spark further interdisciplinary thinking about algorithmic recommender systems.

This open access contributed volume examines the ethical and legal foundations of (future) policies on recommender systems and offers a transdisciplinary approach to tackle important issues related to their development, use and integration into online eco-systems. This volume scrutinizes the values driving automated recommendations - what is important for an individual receiving the recommendation, the company on which that platform was received, and society at large might diverge. The volume addresses concerns about manipulation of individuals and risks for personal autonomy. From a legal perspective, the volume offers a much-needed evaluation of regulatory needs and lawmakers’ answers in various legal disciplines. The focus is on European Union measures of platform regulation, consumer protection and anti-discrimination law. The volume will be of particular interest to the community of legal scholars dealing with platform regulation and algorithmic decision making. By including specific use cases, the volume also exposes pitfalls associated with current models of regulation. Beyond the juxtaposition of purely ethical and legal perspectives, the volume contains truly interdisciplinary work on various aspects of recommender systems.

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents. Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further.

There is widespread agreement that there should be a principle requiring that artificial intelligence be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” :689–707, 2018). There is a strong intuition that if an algorithm decides, for example, whether to give someone a loan, then that algorithm should be explicable. I argue here, however, that such a principle is misdirected. The property of requiring explicability should attach to a particular action or decision rather than the entity making that decision. It is the context and the potential harm resulting from decisions that drive the moral need for explicability—not the process by which decisions are reached. Related to this is the fact that AI is used for many low-risk purposes for which it would be unnecessary to require that it be explicable. A principle requiring explicability would prevent us from reaping the benefits of AI used in these situations. Finally, the explanations given by explicable AI are only fruitful if we already know which considerations are acceptable for the decision at hand. If we already have these considerations, then there is no need to use contemporary AI algorithms because standard automation would be available. In other words, a principle of explicability for AI makes the use of AI redundant.

With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines.

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents. Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further.

Contemporary literature investigating the significant impact of technology on our lives leads many to conclude that ethics must be a part of the discussion at an earlier stage in the design process i.e., before a commercial product is developed and introduced. The problem, however, is the question regarding how ethics can be incorporated into an earlier stage of technological development and it is this question that we argue has not yet been answered adequately. There is no consensus amongst scholars as to the kind of ethics that should be practiced, nor the individual selected to perform this ethical analysis. One school of thought holds that ethics should have pragmatic value in research and design and that it should be implemented by the engineers and/or scientists themselves, while another school of thought holds that ethics need not be so pragmatic. For the latter, the ethical reflection can aim at a variety of goals, and be carried out by an ethicist. None of the approaches resulting from these lines of thinking have been adopted on a wide-scale basis. To that end, the approach presented here is intended to bridge the gap between these schools of thought. It is our contention that ethics ought to be pragmatic and to provide utility for the design process and we maintain that adequate ethical reflection, and all that it entails, ought to be conducted by an ethicist. Thus, we propose a novel role for the ethicist—the ethicist as designer—who subscribes to a pragmatic view of ethics in order to bring ethics into the research and design of artifacts—no matter the stage of development. In this paper we outline the series of steps that a pragmatic value analysis entails: uncovering relevant values, scrutinizing these values and, working towards the translation of values into technical content. In conclusion, we present a list of tasks for the ethicist in his/her role as designer on the interdisciplinary team