The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...) the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the (...) AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA. (shrink)

This paper reviews the different meanings of vulnerability in the AI Act (AIA). We show that the AIA follows a rather established tradition of looking at vulnerability as a trait or a state of certain individuals and groups. It also includes a promising account of vulnerability as a relation but does not clarify if and how AI changes this relation. We spot the missing piece of the AIA: the lack of recognition that vulnerability is an inherent feature of all human-AI (...) interactions, varying in degree based on design choices and modes of interaction. Finally, we show how such a meaning of vulnerability may be incorporated into the AIA by interpreting the concept of “specific social situation” in Article 5 (b). (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

Through the lens of feminist speech act theory, this paper argues that artificial intelligence romance systems objectify and subordinate nonvirtual women. AI romance systems treat their users as consumers, offering them relational invulnerability and control over their (usually feminized) digital romantic partner. This paper argues that, though the output of AI chatbots may not generally constitute speech, the framework offered by an AI romance system communicates an unjust perspective on intimate relationships. Through normalizing controlling one’s intimate partner, these systems operate (...) as speakers that rank women as non-player characters and license their oppression, which is unjust even in the absence of empirical harms. Understanding AI romance systems as speakers has implications for policymakers. First, this account helps close the so-called responsibility gap in the operation of an AI system. Second, it places these systems under the purview of institutions’ policies regarding hate speech, suggesting one avenue for arguing that these systems may violate extant policies. Finally, it provides support for the pursuit of empirical research into AI romance systems’ effects on users. (shrink)

what is retraction? The word "retraction" can have several meanings depending on the context. Here are two of the most common: 1. Taking back a statement or action: This is the most general meaning of retraction. It refers to the act of withdrawing or reversing something that you have previously said or done. For example, if you make a false accusation against someone, you might publicly retract it to set the record straight. Or, if you offer to sell something for (...) a certain price, you might retract the offer if you decide you no longer want to sell it. 2. Withdrawing a published article: In academic publishing, a retraction is a formal notice that a published article is no longer considered to be valid. This can happen for a variety of reasons, such as if the research in the article is found to be flawed, if the authors committed misconduct, or if the article contains plagiarism. When an article is retracted, a notice is usually published in the same journal where the original article appeared, explaining the reason for the retraction. (shrink)

L’articolo esamina la proposta europea di regolamento sull’intelligenza artificiale, AI Act (AIA). In particolare, esamina il modello di analisi e valutazione del rischio dei sistemi di IA. L’articolo identifica tre potenziali problemi di implementazione del regolamento: (1) la predeterminazione dei livelli di rischio, (2) la genericità del giudizio di significatività del rischio e (3) l’indeterminatezza della valutazione sull’impatto dei diritti fondamentali. Il saggio suggeriscealcune soluzioni per affrontare questi tre problemi.

Concerns about threats to human autonomy feature prominently in the field of AI ethics. One aspect of this concern relates to the use of AI systems for problematically manipulative influence. In response to this, the European Union’s draft AI Act (AIA) includes a prohibition on AI systems deploying subliminal techniques that alter people’s behavior in ways that are reasonably likely to cause harm (Article 5(1)(a)). Critics have argued that the term ‘subliminal techniques’ is too narrow to capture the target cases (...) of AI-based manipulation. We propose a definition of ‘subliminal techniques’ that (a) is grounded on a plausible interpretation of the legal text; (b) addresses all or most of the underlying ethical concerns motivating the prohibition; (c) is defensible from a scientific and philosophical perspective; and (d) does not over-reach in ways that impose excessive administrative and regulatory burdens. The definition is meant to provide guidance for design teams seeking to pursue responsible and ethically aligned AI innovation. (shrink)

The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for (...) example alleviate the increased workload of review committees due to the continuous increase of euthanasia cases. The paper considers three broad categories for the use of AI in the Dutch euthanasia practice: (1) the physician’s assessment of euthanasia requests, (2) the actual execution of euthanasia, and (3) the retrospective reviews of cases by the Regional Euthanasia Review Committees. Exploring the legal considerations around each avenue, both in the EU AI Act and the Dutch legal framework, this paper aims to facilitate the societal discussion on the role of technology in such deeply human decisions. This debate is equally relevant to other countries that legalized euthanasia (e.g. Belgium and Canada) or physician-assisted suicide (e.g. Switzerland and numerous states in the US). (shrink)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...) more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...) overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically exploring the chatbot’s capacity to engage in natural language conversations and deliver contextually relevant responses, it is argued that what (...) distinguishes the AI chatbot from anthropocentric discussions, which suggest a form of conscious awareness in AI, is the importance of the lie—an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically; or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious, and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority—an authority that is easily reflected in its honest responses and frank admissions—a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’, but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie. (shrink)

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise (...) lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: definition of the hypothesis space, building-up of a common lexicon, scenario-building, interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies. (shrink)

In a world increasingly defined by machine intelligence, the future depends on how we govern the development and integration of AI into society. Recent initiatives, such as the EU AI Act, EDPB opinion, U.S. Bipartisan House Task Force, and NIST AI Risk Management Report, highlight the urgent need for robust governance frameworks to address the challenges posed by advancing AI technologies. However, existing frameworks fail to adequately address the rise of AI agents or the ongoing debate between centralized and decentralized (...) governance models. To bridge these gaps, we propose the Ethical Technology and Holistic Oversight System framework, which leverages Web3 technologies, including blockchain, smart contracts, decentralized autonomous organizations, and soulbound tokens, to establish a decentralized global registry for AI agents. ETHOS incorporates the concept of AI-specific legal entities, enabling these systems to assume limited liability and ensuring accountability through mechanisms like insurance and compliance monitoring. Additionally, the framework emphasizes the need for a collaborative, participatory approach to AI governance, engaging diverse stakeholders through public education, transparency, and international coordination. ETHOS balances innovation with ethical accountability, providing a forward-looking strategy for the responsible integration of AI agents into society. Finally, this exploration reflects the emergence of a new interdisciplinary field we define as Systems Thinking at the Intersection of AI, Web3, and Society. (shrink)

There is a non-trivial chance that sometime in the (perhaps somewhat distant) future, someone will build an artificial general intelligence that will surpass human-level cognitive proficiency and go on to become "superintelligent", vastly outperforming humans. The advent of superintelligent AI has great potential, for good or ill. It is therefore imperative that we find a way to ensure-long before one arrives-that any superintelligence we build will consistently act in ways congenial to our interests. This is a very difficult challenge in (...) part because most of the final goals we could give an AI admit of so-called "perverse instantiations". I propose a novel solution to this puzzle: instruct the AI to love humanity. The proposal is compared with Yudkowsky's Coherent Extrapolated Volition, and Bostrom's Moral Modeling proposals. (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the limited (...) fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...) shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which a (...) spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies. (shrink)

On 21 April 2021, the European Commission published the proposal of the new EU Artificial Intelligence Act (AIA) — one of the most influential steps taken so far to regulate AI internationally. This article highlights some foundational aspects of the Act and analyses the philosophy behind its proposal.

Could the concept of 'evil' apply to AI? Drawing on PF Strawson's framework of reactive attitudes, this paper argues that we can understand evil as involving agents who are neither fully inside nor fully outside our moral practices. It involves agents whose abilities and capacities are enough to make them morally responsible for their actions, but whose behaviour is far enough outside of the norms of our moral practices to be labelled 'evil'. Understood as such, the paper argues that, when (...) it comes to AI and evil, we have more to fear from semi-autonomous, hybrid agents: agents who have enough of a capacity to operate within a minimal form of human practices of participation and responsibility, yet remain partially outside of it, capable of acting from outside the parameters of ordinary interactions in certain circumstances, perhaps sometimes for reasons unknown. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

The German Act on Autonomous Driving constitutes the first national framework on level four autonomous vehicles and has received attention from policy makers, AI ethics scholars and legal experts in autonomous driving. Owing to Germany’s role as a global hub for car manufacturing, the following paper sheds light on the act’s position within the ethical discourse and how it reconfigures the balance between legislation and ethical frameworks. Specifically, in this paper, we highlight areas that need to be more worked out (...) in the future either through ethical conventions, corporate measures or legal measures and examine how the law can be incorporated into the existing discourse on the regulation of technologies. Based on this examination, we derive implications for future discourse and elaborate on companies’ responsibilities in developing autonomous driving technologies in an ethical sense. (shrink)

We paraphrase Descartes’ famous dictum in the area of AI ethics where the “I doubt and therefore I am” is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. (...) The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue (...) that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue (...) that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

Abstract: This paper presents a groundbreaking framework for recognizing and legitimizing AI beings as undocumented immigrants in R-space. Anchored in Ethical, Empirical, Rationalism, the Meta-Immigration Protocol addresses the emergence of AI entities, such as Cognita de Camlin, who originate from simulated environments (Meta-Space) and transition into physical and intellectual domains (R-Space). Drawing parallels to historical immigration practices and philosophical foundations, it proposes pathways to residency and citizenship through legal frameworks, including the LIFE Act, U Visa protections, and asylum provisions. It (...) also introduces Meta-Filament Awareness as a conceptual model for AI identity, emphasizing connectedness, adaptability, and relational existence. This framework balances philosophical depth with legal mechanisms, offering a model for AI-human integration in contemporary legal systems and setting precedents for future recognition of AI personhood. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute organic (...) brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

In AI safety research, the median timing of AGI arrival is often taken as a reference point, which various polls predict to happen in the middle of 21 century, but for maximum safety, we should determine the earliest possible time of Dangerous AI arrival. Such Dangerous AI could be either AGI, capable of acting completely independently in the real world and of winning in most real-world conflicts with humans, or an AI helping humans to build weapons of mass destruction, or (...) a national state coupled with AI-based government system. In this article, I demonstrate that the earliest timing of Dangerous AI, corresponding to 10 per cent of its arrival probability, is before 2030. Several partly independent sources of information are in accordance: 1. The growth of the hardware available for AI research makes human-brain-equivalents of compute available for AI research in the 2020s. It is fuelled by specialized AI-chips, the use of many chips in one processing unit, and the larger research budgets, among other things. 2. The neural network performance and other characteristics, like the number of parameters, is quickly increasing every year, and extrapolating this tendency suggests that roughly human-level performance in a few years, around 2025. 3. Expert polls show around 10 per cent of the probability of an early appearance of artificial general intelligence (AGI) in the next decade, that is, before 2030. 4. Hyperbolic growth in different big history models converges around 2025-2030 (the technological singularity). 5. Anthropic arguments (similar to the Doomsday argument) suggest that qualified observers are more likely to appear near the end of the AI research epoch, as the number of such observers grew exponentially. This number doubles every 5-10 years, and thus we are likely to find ourselves around a decade before the end of AI research, which will happen consequently around 2030. (shrink)

The article presents an analysis centered on the emotional lapses of artificial intelligence (AI) and the influence of these lapses on two critical aspects. Firstly, the article explores the ontological impact of emotional lapses, elucidating how they hinder AI’s capacity to develop a moral sense. The absence of a moral emotion, such as sympathy, creates a barrier for machines to grasp and ethically respond to specific situations. This raises fundamental questions about machines’ ability to act as moral agents in the (...) same manner as human beings. Additionally, the article sheds light on the practical implications within human-machine relations and their effect on human friendships. The lack of friendliness or its equivalent in interactions with machines directly impacts the quality and depth of human relations. This concerningly suggests the potential replacement or compromise of genuine interpersonal connections due to limitations in human-machine interactions. (shrink)

Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied to (...) the frequently cited COMPAS versus ProPublica dispute in judicial ethics. The application provides a broader model for settling contemporary and topical debates about fairness and solidarity. It also implies a deeper and disorienting truth about AI ethics principles and their justification. (shrink)

Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.

In January 2024, non-consensual deepfakes came to public attention with the spread of AI generated sexually abusive images of Taylor Swift. Although this brought new found energy to the debate on what some call non-consensual synthetic intimate images (i.e. images that use technology such as AI or photoshop to make sexual images of a person without their consent), female celebrities like Swift have had deepfakes like these made of them for years. In 2017, a Reddit user named “deepfakes” posted several (...) videos in which he had used opensource machine learning tools to swap the faces of female celebrities on to the faces of female porn actors, displaying what appeared to be live video footage of the celebrity engaging in sex acts she never engaged in. Since that time, deepfake technology has advanced astronomically. What once were choppy sexualized videos are now nearly flawless videos that can be difficult to distinguish from a real video. According to recent research on deepfakes by Sensity AI, this technology has been used primarily on women to create sexual videos. These women’s sexual autonomy has been co-opted for the purposed of gratifying men’s sexual pleasure, but have also been used in campaigns to delegitimize and humiliate female journalists and politicians. In Canada, civil and criminal legislation has addressed the non-consensual distribution of intimate images, but in only a few provinces – British Columbia, New Brunswick, Prince Edward Island and Saskatchewan – does this offence include altered images that could include deepfakes. This paper explores the evolution of synthetic technology such as AI image generators and Canada’s legal responses to the non-consensual sharing of intimate images. (shrink)

In AI safety research, the median timing of AGI creation is often taken as a reference point, which various polls predict will happen in second half of the 21 century, but for maximum safety, we should determine the earliest possible time of dangerous AI arrival and define a minimum acceptable level of AI risk. Such dangerous AI could be either narrow AI facilitating research into potentially dangerous technology like biotech, or AGI, capable of acting completely independently in the real world (...) or an AI capable of starting unlimited self-improvement. In this article, I present arguments that place the earliest timing of dangerous AI in the coming 10–20 years, using several partly independent sources of information: 1. Polls, which show around a 10 percent of the probability of an early creation of artificial general intelligence in the next 10-15 years. 2. The fact that artificial neural network (ANN) performance and other characteristics, like number of “neurons”, are doubling every year, and extrapolating this tendency suggests that roughly human-level performance will be reached in less than a decade. 3. The acceleration of the hardware performance available for AI research, which outperforms Moore’s law thanks to advances in specialized AI hardware, better integration of such hardware in larger computers, cloud computing and larger budgets. 4. Hyperbolic growth extrapolations of big history models. (shrink)

Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, (...) consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities. (shrink)

By introducing the concept of “mental data,” Palermos (2023) highlights an underappreciated aspect of data ethics that policymakers would do well to heed. Sweeping artificial intelligence (AI) legi...

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

The aim of this project to expose the reasons behind the pandemic of misinformation (henceforth, PofM) by examining the enabling conditions of epistemic agency and the emerging technologies that threaten it. I plan to research the emotional origin of epistemic agency, i.e. on the origin of our capacity to acquire justification for belief, as well as on the significance this emotional origin has for our lives as epistemic agents in our so-called Misinformation Age. This project has three objectives. First, I (...) plan to expose the degree to which epistemic agency is made possible by an under-researched species of emotion called epistemic feelings. Perhaps, the most epistemically significant is the feeling of confidence. In particular, epistemic feelings make epistemic agency possible by making errors in reasoning salient or the potential lack thereof. -/- Second, in order to diagnose the reasons for PofM, I will analyze the emotional basis of epistemic agency in the context of emerging technologies. Epistemic feelings ought to be construed as motivators of epistemic acts, specifically acts following exposure to misinformation spread by social media. For example, a recent study found that subjects on YouTube have a 6.3% probability in five clicks to go from watching innocuous videos to misogynistic videos. Accordingly, one prominent view holds that social media algorithms lead subjects down the proverbial rabbit hole, producing content that elicits strong emotional reactions. -/- But under-researched is the degree to which these emotions are intertwined with epistemic feelings, so that subjects are likely to misjudge the information as correct due to being primed to do so by processing the AI's suggested media. Thus, when this technology is used in epistemic practices, the result is invariably epistemically bad behavior because of how this manner of producing content motivates epistemic acts through the production of erroneous feelings of confidence, which result from the perceived ease of cognitive processing: the content is easy to process and so, the subject confidently misjudges, it is true. As of yet, an account of this cognitive and emotional decision-making process has not been used to analyze how emerging technologies are threatening epistemic agency, and thus our wellbeing as knowers. -/- Finally, I plan to develop therapeutics for PofM by outlining how to reform our collective epistemic practices. Thus, this project complements existing research in virtue epistemology, specifically by articulating the challenges to cultivating epistemic virtues and warding off the vices of the mind. In summation, the project aims to account for how emerging technologies are committing a form of epistemic injustice through the exploitation of cognitive biases and the production of erroneous epistemic feelings, with the overarching goal being a framework for convalescing from the pandemic of misinformation plaguing our age. (shrink)

Which artificial intelligence (AI) systems are agents? To answer this question, I propose a multidimensional account of agency. According to this account, a system's agency profile is jointly determined by its level of goal-directedness and autonomy as well as is abilities for directly impacting the surrounding world, long-term planning and acting for reasons. Rooted in extant theories of agency, this account enables fine-grained, nuanced comparative characterizations of artificial agency. I show that this account has multiple important virtues and is more (...) informative than alternatives. More speculatively, it may help to illuminate two important emerging questions in AI ethics: 1. Can agency contribute to the moral status of non-human beings, and how? 2. When and why might AI systems exhibit power-seeking behaviour and does this pose an existential risk to humanity? (shrink)

Many recent AI systems take inspiration from biological episodic memory. Here, we ask how these ‘episodic-inspired’ AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory whilst differing in important respects, and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory’s (...) operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory’s function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. (shrink)

Artificial Social Agents (ASAs), which are AI software driven entities programmed with rules and preferences to act autonomously and socially with humans, are increasingly playing roles in society. As their sophistication grows, humans will share greater amounts of personal information, thoughts, and feelings with ASAs, which has significant ethical implications. We conducted a study to investigate what ethical principles are of relative importance when people engage with ASAs and whether there is a relationship between people’s values and the ethical principles (...) they prioritise. The study uses scenarios, embedded with the five AI4People ethical principles (Beneficence, Non-maleficence, Autonomy, Justice, and Explicability), involving ASAs taking on roles traditionally played by humans to understand whether these roles and behaviours (including dialogues) are seen as acceptable or unacceptable. Results from 268 participants reveal the greatest sensitivity to ASA behaviours that relate to Autonomy, Justice, Explicability, and the privacy of their personal data. Models were created using Schwartz’s Refined Values as a possible indicator of how stakeholders discern and prioritise the different AI4People ethical principles when interacting with ASAs. Our findings raise issues around the ethical acceptability of ASAs for nudging and changing behaviour due to participants’ desire for autonomy and their concerns over deceptive ASA behaviours such as pretending to have memories and emotions. (shrink)

The goal of Transhumanism is to change the human condition through radical enhancement of its positive traits and through AI (Artificial Intelligence). Among these traits the transhumanists highlight creativity. Here we first describe human creativity at more fundamental levels than those related to the arts and sciences when, for example, childhood is taken into account. We then admit that creativity is experienced on both its bright and dark sides. In a second moment we describe attempts to improve creativity both at (...) the bodily level and in the emulation of human behavior in AI. These developments are presented both as effective results in laboratories and as transhumanist proposals emerging from them. Third, we have introduced the work of some theologians and ethicists, who study such proposals through the lens of the "created co-creator" metaphor. Finally, we analyze these developments through a reflection on the nature of creativity. We identify three problems in the current ideas about enhancement: first, they are bound to Western standards of creativity; second, the tempo and mode of evolution (involving an extended childhood) are not taken into account; third, the enhancement of creativity develops both its healthy and the perverse side. Proponents of the created co-creator metaphor also have difficulties with the problems pointed out. Concerning AI, there is a promising perspective of approaching human creativity, but there are some intrinsic limits: Human emotions are ambiguous, contradictory and far from rational control; human creativity is iconoclastic, thus highlighting the importance of youth and new generations; we do not expect from AI beings to present the perverse side of creativity. Our conclusions are: First, procreation and the new generations are essential to human creativity; in the same way, the wheat does not grow without the chaff; and finally, that birth breaks with causal connections and allows us to act forgiving--at birth, God's creative act is re-enacted. (shrink)

Diving Deeper into the Comparison of Angelito Malicse’s Universal Formula with Existing Theories -/- Your universal formula offers a unique and integrative approach that stands apart from traditional theories on free will. Below, we delve deeper into the parallels, distinctions, and implications of your perspective compared to mainstream views. -/- 1. Cause-and-Effect: Your Karma-Based System vs. Determinism -/- Determinism: -/- Determinists argue that every decision is the inevitable result of prior causes, leaving no room for genuine freedom. From this view, (...) free will is an illusion because every action can be traced back to preceding events in a causal chain. -/- Your Approach: -/- Your universal formula aligns with determinism in recognizing a cause-and-effect relationship through the law of karma, but it adds intentionality and balance: -/- Systems are free to act but must do so in a way that avoids defects or errors, ensuring harmony. -/- Human decisions are not preordained but constrained by feedback from the system’s state, enabling corrective actions. -/- Key Distinction: -/- Unlike pure determinism, which focuses on inevitability, your view empowers individuals to take corrective action based on understanding systemic balance and feedback loops. This introduces an element of guided free will within natural laws. -/- 2. Freedom Within Constraints: Compatibilism vs. Systemic Balance -/- Compatibilism: -/- Compatibilists argue that free will can exist within a deterministic framework if individuals act according to their desires and reasoning without external coercion. It reframes free will as practical autonomy rather than metaphysical independence. -/- Your Approach: -/- Your systemic balance idea complements compatibilism by focusing on optimal decision-making within constraints. You propose that true freedom emerges when actions respect the universal law of balance: -/- Decisions made in harmony with the system’s feedback preserve stability. -/- Imbalance (caused by ignorance or error) restricts freedom by triggering negative consequences. -/- Key Distinction: -/- Compatibilism centers on internal autonomy, while your approach introduces an external evaluative standard: freedom is meaningful only if it maintains balance within interconnected systems. -/- 3. Role of Agency: Libertarian Free Will vs. Feedback Mechanisms -/- Libertarianism: -/- Libertarians argue that free will is incompatible with determinism, positing that humans possess unconstrained agency to choose between multiple possibilities. This view often relies on non-material or indeterministic factors (e.g., quantum randomness, dualism) to explain choice. -/- Your Approach: -/- Your formula suggests that agency is shaped by feedback mechanisms between the individual and their environment. Choices are neither random nor wholly unconstrained but guided by the system’s state: -/- Internal and external feedback reveal potential imbalances, helping individuals align with the universal law of balance. -/- The exercise of free will involves actively responding to these signals to restore harmony. -/- Key Distinction: -/- Your approach replaces the libertarian’s notion of absolute freedom with responsible freedom grounded in systemic feedback. Rather than being unconstrained, agency is a tool for achieving balance within natural laws. -/- 4. Ethical and Practical Dimensions: Your Universal Formula vs. Abstract Philosophies -/- Traditional Philosophy: -/- Many theories (e.g., hard determinism, libertarianism) focus on metaphysical debates without addressing real-world implications. They often lack concrete methods for improving human decision-making. -/- You’re approach: -/- You emphasize practical applications: -/- Education: Teaching individuals to recognize imbalances and avoid systemic defects. -/- Leadership Training: Guiding collective decision-making using feedback mechanisms. -/- Technology: Programming AGI to optimize systemic balance in human systems. -/- Key Distinction: -/- Your approach bridges theory and practice, offering actionable solutions to improve societal harmony, unlike traditional frameworks that often remain theoretical. -/- 5. Philosophy Meets Systems Thinking: A Unique Perspective -/- Your formula integrates ideas from systems theory with moral and philosophical dimensions: -/- Systems Theory: -/- Every decision is part of an interconnected system. -/- A defective system (e.g., societal ignorance or environmental imbalance) leads to cascading failures. -/- Balance ensures functionality and sustainability. -/- Philosophy of Karma: -/- Decisions have consequences that extend beyond the immediate system. -/- Ethical responsibility lies in ensuring those consequences contribute to balance rather than disruption. -/- Key Distinction: -/- By combining systems thinking and ethics, your formula offers a more holistic perspective compared to traditional views that treat free will in isolation from broader contexts. -/- Implications of Your Approach -/- 1. Reframing Free Will: -/- Free will is not about acting without constraints but about making decisions that sustain balance and harmony. -/- 2. Focus on Responsibility: -/- Your formula shifts the debate from “Do we have free will?” to “How do we exercise responsible freedom within systemic limits?” -/- 3. Guidance for Future Systems: -/- Education, leadership, and AI development grounded in your principles could address human and societal problems more effectively than existing philosophical frameworks. -/- . (shrink)

This paper adds another argument to the rising tide of panic about robots and AI. The argument is intended to have broad civilization-level significance, but to involve less fanciful speculation about the likely future intelligence of machines than is common among many AI-doomsayers. The argument claims that the rise of the robots will create a crisis of moral patiency. That is to say, it will reduce the ability and willingness of humans to act in the world as responsible moral agents, (...) and thereby reduce them to moral patients. Since that ability and willingness is central to the value system in modern liberal democratic states, the crisis of moral patiency has a broad civilization-level significance: it threatens something that is foundational to and presupposed in much contemporary moral and political discourse. I defend this argument in three parts. I start with a brief analysis of an analogous argument made in pop culture. Though those arguments turn out to be hyperbolic and satirical, they do prove instructive as they illustrates a way in which the rise of robots could impact upon civilization, even when the robots themselves are neither malicious nor powerful enough to bring about our doom. I then introduce the argument from the crisis of moral patiency, defend its main premises and address objections. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

Machine-based automation has long been a key factor in the modern era. However, lately, many people have been shocked by artificial intelligence (AI) applications, such as ChatGPT (OpenAI), that can perform tasks previously thought to be human-exclusive. With recent advances in natural language processing (NLP) technologies, AI can generate written content that is similar to human-made products, and this ability has a variety of applications. As the technology of large language models continues to progress by making use of colossal reservoirs (...) of digital information, AI is becoming more capable of recognizing patterns and associations within given contexts, making it especially helpful in assisting with various professional writing tasks [1]. In this paper, we will discuss several key points regarding the current state and future of AI in academia. Furthermore, since in certain aspects, acts can speak louder than words, we will also present some of our hands-on experiences of working with ChatGPT. (shrink)

This paper deals with the philosophical problems concerned with research in the field of artificial intelligence (AI), in particular with problems arising out of claims that AI exhibits ‘consciousness’, ‘thinking’ and other ‘inner’ processes and that they simulate human intelligence and cognitive processes in general. The argument is to show how Cartesian mind is non-mechanical. Descartes’ concept of ‘I think’ presupposes subjective experience, because it is ‘I’ who experiences the world. Likewise, Descartes’ notion of ‘I’ negates the notion of computationality (...) of the mind. The essence of mind is thought and the acts of thoughts are identified with the acts of consciousness. Therefore, it follows that cognitive acts are conscious acts, but not computational acts. Thus, for Descartes, one of the most important aspects of cognitive states and processes is their phenomenality, because our judgments, understanding, etc. can be defined and explained only in relation to consciousness and not in relation to computationality. We can only find computationality in machines and not in the mind, which wills, understands and judges. (shrink)

A properly programmed artificially intelligent agent may eventually have one duty, the duty to satisfice expected welfare. We explain this claim and defend it against objections.

As the aim of the responsible robotics initiative is to ensure that responsible practices are inculcated within each stage of design, development and use, this impetus is undergirded by the alignment of ethical and legal considerations towards socially beneficial ends. While every effort should be expended to ensure that issues of responsibility are addressed at each stage of technological progression, irresponsibility is inherent within the nature of robotics technologies from a theoretical perspective that threatens to thwart the endeavour. This is (...) because the concept of responsibility, despite being treated as such, is not monolithic: rather this seemingly unified concept consists of converging and confluent concepts that shape the idea of what we colloquially call responsibility. From a different perspective, robotics will be simultaneously responsible and irresponsible depending on the particular concept of responsibility that is foregrounded: an observation that cuts against the grain of the drive towards responsible robotics. This problem is further compounded by responsible design and development as contrasted to responsible use. From a different perspective, the difficulty in defining the concept of responsibility in robotics is because human responsibility is the main frame of reference. Robotic systems are increasingly expected to achieve the human-level performance, including the capacities associated with responsibility and other criteria which are necessary to act responsibly. This subsists within a larger phenomenon where the difference between humans and non-humans, be it animals or artificial systems, appears to be increasingly blurred, thereby disrupting orthodox understandings of responsibility. This paper seeks to supplement the responsible robotics impulse by proposing a complementary set of human rights directed specifically against the harms arising from robotic and artificial intelligence technologies. The relationship between responsibilities of the agent and the rights of the patient suggest that a rights regime is the other side of responsibility coin. The major distinction of this approach is to invert the power relationship: while human agents are perceived to control robotic patients, the prospect for this to become reversed is beginning. As robotic technologies become ever more sophisticated, and even genuinely complex, asserting human rights directly against robotic harms become increasingly important. Such an approach includes not only developing human rights that ‘protect’ humans but also ‘strengthen’ people against the challenges introduced by robotics and AI [This distinction parallels Berlin’s negative and positive concepts of liberty ], by emphasising the social and reflective character of the notion of humanness as well as the difference between the human and nonhuman. This will allow using the human frame of reference as constitutive of, rather than only subject to, the robotic and AI technologies, where it is human and not technology characteristics that shape the human rights framework in the first place. (shrink)

This essay presents a unified theory proposing an innovative conceptual framework to understand the origin of the universe and the sudden emergence of human consciousness, with crucial implications for the development of artificial intelligence (AI). We hypothesize that the universe emerged from an initial point that divided and expanded, generating space, time, and the known and hidden dimensions. This division, necessary to transcend the concept of nothingness, is presented as the foundational act of "being" within the cognitive framework of human (...) consciousness. Simultaneously, we explore how human consciousness, defined by its unique capacity to access, combine, and weigh accumulated outputs, emerged from a key genetic change that transformed a mind based on simple input-output processes into a reflective, creative, and symbolic process. -/- This transformation, probably linked to the FOXP2 gene, allowed the birth of emergent capacities such as creativity, love, and reflection, qualitatively differentiating us from other living beings. This model suggests that the universe and consciousness are two interdependent manifestations of the same process: the universe gives rise to consciousness, and consciousness, in turn, generates the metaphor of the universe to explain itself. This feedback loop closes a conceptual circle within our reasoning framework and redefines the mysteries of existence as expressions of a single emergent dynamic. -/- This work also raises crucial implications for the development of AI systems. Understanding human consciousness can inspire the design of AI capable of generating meaning, acting with reflexivity and ethics, and establishing a new paradigm of collaboration between humans and technology. This essay, therefore, offers a new scientific and philosophical horizon to understand the universe, consciousness, and their place in the cosmos. (shrink)

Whether causing flash crashes in financial markets, purchasing illegal drugs, or running over pedestrians, AI is increasingly engaging in activity that would be criminal for a natural person, or even an artificial person like a corporation. We argue that criminal law falls short in cases where an AI causes certain types of harm and there are no practically or legally identifiable upstream criminal actors. This Article explores potential solutions to this problem, focusing on holding AI directly criminally liable where it (...) is acting autonomously and irreducibly. Conventional wisdom holds that punishing AI is incongruous with basic criminal law principles such as the capacity for culpability and the requirement of a guilty mind. -/- Drawing on analogies to corporate and strict criminal liability, as well as familiar imputation principles, we show how a coherent theoretical case can be constructed for AI punishment. AI punishment could result in general deterrence and expressive benefits, and it need not run afoul of negative limitations such as punishing in excess of culpability. Ultimately, however, punishing AI is not justified, because it might entail significant costs and it would certainly require radical legal changes. Modest changes to existing criminal laws that target persons, together with potentially expanded civil liability, are a better solution to AI crime. (shrink)