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)

1. In the last decade, technological systems based on Artificial Intelligence (AI) architectures entered our lives at an increasingly fast pace. Virtual assistants facilitate our daily tasks, recom...

This article sets in with the question whether current or foreseeable transformer-based large language models (LLMs), such as the ones powering OpenAI’s ChatGPT, could be language users in a way comparable to humans. It answers the question negatively, presenting the following argument. Apart from niche uses, to use language means to act. But LLMs are unable to act because they lack intentions. This, in turn, is because they are the wrong kind of being: agents with intentions need to be autonomous (...) organisms while LLMs are heteronomous mechanisms. To conclude, the article argues, based on structural aspects of transformer-based LLMs, that these LLMs have taken a first step away from mechanistic artificiality to autonomous self-constitution, which means that these models are (slowly) moving into a direction that someday might result in non-human, but equally non-artificial agents, thus subverting the time-honored Kantian distinction between organism and mechanism. (shrink)

Despite facing significant criticism for assigning human-like characteristics to artificial intelligence, phrases like “trustworthy AI” are still commonly used in official documents and ethical guidelines. It is essential to consider why institutions continue to use these phrases, even though they are controversial. This article critically evaluates various reasons for using these terms, including ontological, legal, communicative, and psychological arguments. All these justifications share the common feature of trying to justify the official use of terms like “trustworthy AI” by appealing to (...) the need to reflect pre-existing facts, be it the ontological status, ways of representing AI or legal categories. The article challenges the justifications for these linguistic practices observed in the field of AI ethics and AI science communication. In particular, it takes aim at two main arguments. The first is the notion that ethical discourse can move forward without the need for philosophical clarification, bypassing existing debates. The second justification argues that it’s acceptable to use anthropomorphic terms because they are consistent with the common concepts of AI held by non-experts—exaggerating this time the existing evidence and ignoring the possibility that folk beliefs about AI are not consistent and come closer to semi-propositional beliefs. The article sounds a strong warning against the use of human-centric language when discussing AI, both in terms of principle and the potential consequences. It argues that the use of such terminology risks shaping public opinion in ways that could have negative outcomes. (shrink)

Increasingly, artists describe the feeling of creating images with generative AI systems as like working with a “collaborator”—a term that is also common in the scholarly literature on AI image-generation. If it is appropriate to describe these dynamics in terms of collaboration, as I demonstrate, it is important to determine the form and nature of these joint efforts, given the appreciative relevance of different types of contribution to the production of an artwork. Accordingly, I examine three kinds of collaboration that (...) can be found in the philosophical literature on artistic authorship—collective authorship, co-creatorship, and co-production—to determine whether human-AI interactions comprise joint efforts as per such kinds. As I find, collaboration is a concept that invokes rich psychological terms and so one to used be with care in relation to generative AI, which does not yet meet the conditions to count as an artistic collaborator in the senses derived from the literature. To progress discussions on ethical and legal issues that are raised by image-making practices involving generative AI, and further research into the distinctive qualities afforded by interactions with these systems, I argue that we ought to frame their contributions to the production of visual artworks in terms of a “generative” contribution and describe the interactions between humans and generative AI systems as “AI-assisted production”. (shrink)

This article addresses uncertainties over authorship in the age of generative AI by developing the theoretical underpinnings of a systematic approach to attributing authorship in visual art practices involving generative AI, which build on the workings of multiple agents and technologies. By using an analytic philosophical methodology to analyze the practices and key concepts under discussion it is clarified what is meant by authorship in these practices and what kind of works are at stake. As this analysis finds, authorship becomes (...) complicated in these practices not just because of the non-deterministic workings of generative AI systems, but because the works under discussion can be viewed on the one hand as the outputs of generative AI systems and on the other as works of AI art, including kinds referred to here as “synthetic images”. Importantly, authorship in the latter case requires intentionality and autonomy from agents to both initiate the creative process and ratify, or evaluate, the work as theirs, something that only humans can do at this point. Nevertheless, the dependence of synthetic images on AI-generated images means that in attributing authorship in the practices under discussion, we ought to account for the dual identity of these items and, accordingly, the different forms of authorship and contributions attached to them. This dual approach, it is demonstrated, provides the tools to assess when the potentially many and varied figures involved in these practices should count as authors, or other kinds of contributors, and facilitates more rewarding appreciation of the artworks. (shrink)

Our goal in this paper is to establish a set of criteria for understanding the meaning and sources of attributing (un)fairness to AI algorithms. To do so, we first establish that (un)fairness, like other normative notions, can be understood in a proper primary sense and in secondary senses derived by analogy. We argue that AI algorithms cannot be said to be (un)fair in the proper sense due to a set of criteria related to normativity and agency. However, we demonstrate how (...) and why AI algorithms can be qualified as (un)fair by analogy and explore the sources of this (un)fairness and the associated problems of responsibility assignment. We conclude that more user-driven AI approaches could alleviate some of these difficulties. (shrink)

Mises’ and Hayek’s arguments against central economic planning have long been taken as definitive proof that a centrally planned economy managed by the government would be impossible. Today, however, the exponential rise in the capacities of AI has opened up the possibility that supercomputers could have what it takes to plan the national economy. The ‘economic calculation debate’ has thus reignited. Arguably, this is because neither Mises nor Hayek have given a clear and conclusive argument why central planning of the (...) economy is impossible in principle. The paper frames the problem of economic planning as an agent–environment interaction, offering a taxonomy of the different sets of agents at play a) in a market economy and b) in a centrally planned economy equipped with the most sophisticated AI technology. The argument is that public institutions as planning bodies cannot replace the market order, no matter the AI technology behind them, for the elimination of the market entails the elimination of crucial kinds of agents that cannot be recreated or emulated through AI or careful social planning: the proactive action of entrepreneurs driving market allocation. (shrink)

In this paper, I argue against the view that existing AI systems can be deemed agents comparably to human beings or other organisms. I especially focus on the criteria of interactivity, autonomy, and adaptivity, provided by the seminal work of Luciano Floridi and José Sanders to determine whether an artificial system can be considered an agent. I argue that the tentacles of octopuses also fit those criteria. However, I argue that octopuses’ tentacles cannot be attributed agency because their behavior can (...) be meaningfully interpreted only in reference to the octopus’ organism as an entire system. I argue that attributing agency to AI systems faces similar difficulties, and propose an alternative characterization of these systems as responsive and contested scaffolds. (shrink)

The developers of ecological ethics claim that the rationale of anthropocentrism is false. Its main message is that natural complexes and resources exist to be useful to the human being who sees them only from the perspective of using them and does not take into account their intrinsic value. Kant’s anthropocentric teaching argues that the instrumental attitude to nature has its limits. These limits are hard to determine because the anthropocentrists claim that the human being is above nature. Indeed, the (...) “human exclusiveness thesis” (J.- M. Schaeffer) assumes that its advocates underestimate nature’s impact on humans and their connection to other living beings. Constructing a dialogue between Kant and Schaeffer helps to solve two problems. First, to determine whether Kant’s ethical and teleological concept is immune to Schaeffer’s critique. Second, to clarify the German philosopher’s contribution to environmental ethics. I come to the conclusion that it is premature to claim that consciousness and free will exist only in the framework of their being conditioned by nature. I also demonstrate that in his teleological teaching Kant sees the mechanism of nature as the dependence of living things on forces that act in an unpremeditated manner. Recognition of these forces sets limits to humans’ instrumental attitude to nature and paves the way for them to become moral subjects. Thus, Kant’s teaching combines “the human exclusiveness thesis” and the intuitions of the representatives of deep ecology and the opponents of anthropocentrism as such. (shrink)

Should artificial intelligences ever be included as co-authors of democratic decisions? According to the conventional view in democratic theory, the answer depends on the relationship between the political unit and the entity that is either affected or subjected to its decisions. The relational conditions for inclusion as stipulated by the all-affected and all-subjected principles determine the spatial extension of democratic inclusion. Thus, AI qualifies for democratic inclusion if and only if AI is either affected or subjected to decisions by the (...) political unit in relevant ways. This paper argues that the conventional view is too simple; that it neglects democratic reasons to recognize only agents and/or moral patients as participants in decision-making. The claim defended is that AAP and ASP implicitly affirm requirements for agency and patiency. In ASP, the entity included must be an agent understood either in terms of legal status, capacity to comply with the law or ability to recognize legitimate authority. In AAP, the entity included must be a patient, understood either in terms of capacity for sentience or consciousness. Thus, the idea here is to explore the potential democratic inclusion of artificial intelligences by an updated account of the relevant conditions of agency and patiency that are implicit in democratic theory. Although conceivable that AI is or will be either affected or subjected in relevant ways to decisions made by political units, it is far less clear that AI will ever be agents or patients in the sense required for democratic inclusion. (shrink)