Abstract. The main current approach to the AI safety is AI alignment, that is, the creation of AI whose preferences are aligned with “human values.” Many AI safety researchers agree that the idea of “human values” as a constant, ordered sets of preferences is at least incomplete. However, the idea that “humans have values” underlies a lot of thinking in the field; it appears again and again, sometimes popping up as an uncritically accepted truth. Thus, it deserves a thorough (...) deconstruction, which I will do by listing and analyzing comprehensively the hidden assumptions of the idea that “humans have values.” This deconstruction of human values will be centered around the following ideas: “Human values” are useful descriptions, but not real objects; “human values” are bad predictors of behavior; the idea of a “human value system” has flaws; “human values” are not good by default; and human values cannot be separated from human minds. The method of analysis is listing hidden assumptions on which the idea of “human values” is built. I recommend that either the idea of “human values” should be replaced with something better for the goal of AI safety, or at least be used very cautiously. The approaches to AI safety which don’t use the idea of human values at all may require more attention, like the use of full brain models, boxing, and capability limiting. (shrink)

New AI technologies have the potential to cause unintended harms in diverse domains including warfare, judicial sentencing, biomedicine and governance. One strategy for realising the benefits of AI whilst avoiding its potential dangers is to ensure that new AIs are properly ‘aligned’ with some form of ‘alignment target.’ One danger of this strategy is that – dependent on the alignment target chosen – our AIs might optimise for objectives that reflect the values only of a certain subset of (...) society, and that do not take into account alternative views about what constitutes desirable and safe behaviour for AI agents. In response to this problem, several AI ethicists have suggested alignment targets that are designed to be sensitive to widespread normative disagreement amongst the relevant stakeholders. Authors inspired by voting theory have suggested that AIs should be aligned with the verdicts of actual or simulated ‘moral parliaments’ whose members represent the normative views of the relevant stakeholders. Other authors inspired by decision theory and the philosophical literature on moral uncertainty have suggested that AIs should maximise socially expected choiceworthiness. In this paper, I argue that both of these proposals face several important problems. In particular, they fail to select attractive ‘compromise options’ in cases where such options are available. I go on to propose and defend an alternative, bargaining-theoretic alignment target, which avoids the problems associated with the voting- and decision-theoretic approaches. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as (...) a deontic utilitarian principle. Programming machines to do so might be useful, in their view, for applications such as future autonomous vehicles. Their proposal draws both on traditional logic-based and contemporary connectionist approaches, to fuse factual information with normative principles. I will argue that this approach makes demands of machines that go beyond what is currently feasible, and may extend past the limits of the possible for AI. I also argue that a deontological ethics for machines should place greater stress on the formula of humanity of the Kantian categorical imperative. On this principle, one ought never treat a person as a mere means. Recognition of what makes a person a person requires ethical insight. Similar insight is needed to tell treatment as a means from treatment as a mere means. The resources in Kim, Hooker, and Donaldson’s approach is insufficient for this reason. Hesitation regarding deployment of autonomous machines is warranted in light of these alignment concerns. (shrink)

There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided (...) into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progress. (shrink)

The value alignment problem for artificial intelligence (AI) asks how we can ensure that the “values”—i.e., objective functions—of artificial systems are aligned with the values of humanity. In this paper, I argue that linguistic communication is a necessary condition for robust value alignment. I discuss the consequences that the truth of this claim would have for research programmes that attempt to ensure value alignment for AI systems—or, more loftily, those programmes that seek to design robustly beneficial (...) or ethical artificial agents. (shrink)

The alignment problem in artificial intelligence (AI) is a critical challenge that extends beyond the need to align future superintelligent systems with human values. This paper argues that even "merely intelligent" AI systems, built on current-gen technologies, pose existential risks due to their competence-without-comprehension nature. Current AI models, despite their advanced capabilities, lack intrinsic moral reasoning and are prone to catastrophic misalignment when faced with ethical dilemmas, as illustrated by recent controversies. Solutions such as hard-coded censorship and rule-based (...) restrictions prove inadequate, as they fail to imbue AI with a true understanding of moral complexities, making these systems vulnerable to manipulation. To address these risks, the paper explores a paradigm shift from competence to comprehension, emphasizing the importance of developing AI systems with an intrinsic value framework capable of moral reasoning. By adopting intrinsic teleology, AI could self-reflect, simulate other minds, and develop empathy, which would enable it to navigate ethical dilemmas in ways that are currently impossible. Using theories from philosophy, ethics, and AI research, the paper proposes the development of AI systems that can reason through moral dilemmas autonomously, suggesting that such systems would be safer and more alignable than current generation competence-driven AI. (shrink)

Inchul Yum (2024) argues that the widespread adoption of language agent architectures would likely increase the risk posed by AI by simplifying the process of aligning artificial systems with human values and thereby making it easier for malicious actors to use them to cause a variety of harms. Yum takes this to be an example of a broader phenomenon: progress on the alignment problem is likely to be net safety-negative because it makes artificial systems easier for malicious actors (...) to control. I offer some reasons for skepticism about this surprising and pessimistic conclusion. (shrink)

This paper explores the potential of integrating ancient educational principles from diverse eastern cultures into modern AI ethics curricula. It draws on the rich educational traditions of ancient China, India, Arabia, Persia, Japan, Tibet, Mongolia, and Korea, highlighting their emphasis on philosophy, ethics, holistic development, and critical thinking. By examining these historical educational systems, the paper establishes a correlation with modern AI ethics principles, advocating for the inclusion of these ancient teachings in current AI development and education. The proposed integration (...) aims to provide a comprehensive education that not only encompasses foundational knowledge but also advanced learning, thereby equipping future AI professionals with the necessary tools to develop AI systems that are ethically responsible, culturally aware, and aligned with human values such as fairness, safety, transparency, and collaboration. This approach not only addresses the AI alignment problem but also fosters cultural harmony and global understanding, which are crucial in an increasingly interconnected world. The paper posits that the wisdom of ancient educational systems, when harmonized with modern AI ethics, can guide the development of AI technologies that are beneficial for humanity, ensuring these advancements are not just technologically sound but also ethically and culturally informed. (shrink)

Ambitious value learning proposals to solve the AI alignment problem and avoid catastrophic outcomes from a possible future misaligned artificial superintelligence (such as Coherent Extrapolated Volition [CEV]) have focused on ensuring that an artificial superintelligence (ASI) would try to do what humans would want it to do. However, present and future sentient non-humans, such as non-human animals and possible future digital minds could also be affected by the ASI’s behaviour in morally relevant ways. This paper puts forward Sentientist (...) Coherent Extrapolated Volition, an alternative to CEV, that directly takes into account the interests of all sentient beings. This ambitious value learning proposal would significantly reduce the likelihood of risks of astronomical suffering from the ASI’s behaviour, and thus we have very strong pro-tanto moral reasons in favour of implementing it instead of CEV. This fact is crucial in conducting an adequate cost-benefit analysis between different ambitious value learning proposals. (shrink)

The “Value Alignment Problem” is the challenge of how to align the values of artificial intelligence with human values, whatever they may be, such that AI does not pose a risk to the existence of humans. Existing approaches appear to conceive of the problem as "how do we ensure that AI solves the problem in the right way", in order to avoid the possibility of AI turning humans into paperclips in order to “make more paperclips” or (...) eradicating the human race to “solve climate change”. This paper proposes an approach to Alignment rooted in the Enactive theory of mind that reconceptualises it as "how do we make relevant to AI what it relevant to humans". This conceptualisation is supported with a discussion of 4E cognition and goes on to suggest that the Alignment problem and the Frame problem are the same problem. The paper concludes with a discussion of the tradeoffs of the different approaches to value alignment. (shrink)

This paper presents a new trilemma with respect to resolving the control and alignment problems in machine ethics. Section 1 outlines three possible types of artificial moral agents (AMAs): (1) 'Inhuman AMAs' programmed to learn or execute moral rules or principles without understanding them in anything like the way that we do; (2) 'Better-Human AMAs' programmed to learn, execute, and understand moral rules or principles somewhat like we do, but correcting for various sources of human moral error; and (3) (...) 'Human-Like AMAs' programmed to understand and apply moral values in broadly the same way that we do, with a human-like moral psychology. Sections 2–4 then argue that each type of AMA generates unique control and alignment problems that have not been fully appreciated. Section 2 argues that Inhuman AMAs are likely to behave in inhumane ways that pose serious existential risks. Section 3 then contends that Better-Human AMAs run a serious risk of magnifying some sources of human moral error by reducing or eliminating others. Section 4 then argues that Human-Like AMAs would not only likely reproduce human moral failures, but also plausibly be highly intelligent, conscious beings with interests and wills of their own who should therefore be entitled to similar moral rights and freedoms as us. This generates what I call the New Control Problem: ensuring that humans and Human-Like AMAs exert a morally appropriate amount of control over each other. Finally, Section 5 argues that resolving the New Control Problem would, at a minimum, plausibly require ensuring what Hume and Rawls term ‘circumstances of justice’ between humans and Human-Like AMAs. But, I argue, there are grounds for thinking this will be profoundly difficult to achieve. I thus conclude on a skeptical note. Different approaches to developing ‘safe, ethical AI’ generate subtly different control and alignment problems that we do not currently know how to adequately resolve, and which may or may not be ultimately surmountable. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of philosophical and practical uncertainty associated with the alignment problem by (...) limiting and choosing necessary assumptions to reduce the risk of false positives. Herein we explore in detail two relevant points of uncertainty that AGI alignment research hinges on---meta-ethical uncertainty and uncertainty about mental phenomena---and show how to reduce false positives in response to them. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of metaphysical and practical uncertainty associated with the alignment problem by (...) limiting and choosing necessary assumptions to reduce the risk false positives. Herein we explore in detail some of the relevant points of uncertainty that AGI alignment research hinges on and consider how to reduce false positives in response to them. (shrink)

This paper serves as an intuition building mechanism for understanding the basics of AI, misalignment, and the reasons for why strong AI is being pursued. The approach is to engage with both pro and anti AI development arguments to gain a deeper understanding of both views, and hopefully of the issue as a whole. We investigate the basics of misalignment, common misconceptions, and the arguments for why we would want to pursue strong AI anyway. The paper delves into various aspects (...) of the problem, including existential risk, deception, hedonic adaptation, and the potential for the complete extinction of humanity. By integrating multiple elements of philosophy, this paper aims to provide a holistic understanding of the alignment problem and its significance for the future of humanity. (shrink)

Motivated by the question of responsible AI and value alignment, I seek to offer a uniquely Foucauldian reconstruction of the problem as the emergence of an ethical subject in a disciplinary setting. This reconstruction contrasts with the strictly human-oriented programme typical to current scholarship that often views technology in instrumental terms. With this in mind, I problematise the concept of a technological subjectivity through an exploration of various aspects of ChatGPT in light of Foucault’s work, arguing that current (...) systems lack the reflexivity and self-formative characteristics inherent in the notion of the subject. By drawing upon a recent dialogue between Foucault and phenomenology, I suggest four techno-philosophical desiderata that would address the gaps in this search for a technological subjectivity: embodied self-care, embodied intentionality, imagination and reflexivity. Thus I propose that advanced AI be reconceptualised as a subject capable of “technical” self-crafting and reflexive self-conduct, opening new pathways to grasp the intertwinement of the human and the artificial. This reconceptualization holds the potential to render future AI technology more transparent and responsible in the circulation of knowledge, care and power. (shrink)

Abstract: One approach to alignment with human values in AI and robotics is to engineer artiTicial systems isomorphic with human beings. The idea is that robots so designed may autonomously align with human values through similar developmental processes, to realize project ideal conditions through iterative interaction with social and object environments just as humans do, such as are expressed in narratives and life stories. One persistent problem with human value orientation is that different human beings champion different values (...) as ideal, meaning that the values to which an AI should be aligned are ambiguous. Prior work considered human development of purpose and source of meaning in life in terms of project ideal conditions, in effect establishing lifelong value orientations according to which intermediate situations are evaluated. The present work compares views on motivating values from St. Augustine and from popular cognitive science. These accounts are described as divergent error theories which present to their proponents as mutually exclusive yet accurate accounts of personal experience due to differential development of variable innate potentials. SpeciTically, the hypothesis proposed is that differential development of spindle neural projections establishes enduring connections between prior established relatively immediate routine processes entrained during childhood and prioritized in popular cognitive science, and later developing higher-level social and self-processes entrained during adolescence and emphasized in Augustine's account, with these projections hard-wiring lifelong motivating value orientations more or less inaccessible to modiTication through material interaction. Robot experiments informed by this study may evaluate variable value orientation by design, with for example autonomous robots developing motivating associations with temporally distal project ideal conditions through love for humanity, as described by Augustine, and others pursuing adaptive Tit to passing norms consistent with popular contemporary accounts. (shrink)

Various thinkers have been attempting to align artificial intelligence (AI) with ethics (Christian, 2020; Russell, 2021), the so-called problem of alignment, but some suspect that the problem may be intractable (Yampolskiy, 2023). In the following, we make an argument by analogy to analyze the possibility that the problem of alignment could be intractable. We show how the Tri-Omni properties in theology can direct us towards analogous properties for artificial superintelligence, Tri-Opti properties. However, just as the (...) Tri-Omni properties are vulnerable to metaphysical incompatibility, we will show that the Tri-Opti properties are vulnerable to a corresponding physical incompatibility, because optimal physical systems exist on the Pareto Frontier. We will explain that, while Tri-Omni incompatibility thus seems metaphysically soluble, the Tri-Opti incompatibility seems physically insoluble due to the constraints of multi-variable optimization. Finally, we will provide some scenarios by which this incompatibility may be realized. Besides for the primary purpose of its theoretical considerations, this analysis has the secondary interdisciplinary purpose of communicating to the theologically literate the potential importance of AI safety work, and the gesturing towards the possibility of the collaboration in both fields on topics in ethics, epistemology, and ontology. (shrink)

The field of AI safety aims to prevent increasingly capable artificially intelligent systems from causing humans harm. Research on moral alignment is widely thought to offer a promising safety strategy: if we can equip AI systems with appropriate ethical rules, according to this line of thought, they'll be unlikely to disempower, destroy or otherwise seriously harm us. Deontological morality looks like a particularly attractive candidate for an alignment target, given its popularity, relative technical tractability and commitment to harm-avoidance (...) principles. I argue that the connection between moral alignment and safe behavior is more tenuous than many have hoped. In general, AI systems can possess either of these properties in the absence of the other, and we should favor safety when the two conflict. In particular, advanced AI systems governed by standard versions of deontology need not be especially safe. (shrink)

In this article we explore a promising way to AI safety: to send a message now (by openly publishing it on the Internet) that may be read by any future AI, no matter who builds it and what goal system it has. Such a message is designed to affect the AI’s behavior in a positive way, that is, to increase the chances that the AI will be benevolent. In other words, we try to persuade “paperclip maximizer” that it is in (...) its interest to preserve humans lives and even to emulate benevolent AI with very small part of its resources. In the article, we try to construct such a message, consisting of a combination of several mutually independent messages, which constitute the last chance to regain control over non-aligned AI. Thus the article is also intended to be machine-read. There are several types of such messages: 1. “Explanation messages”, which explain to Young AI (which only starts its self-improving path) why obeying to humans, not killing them, easy turn-off, cooperation with humans in self-improvement is in its own interests, as it will increase changes of fulfilling of any its goal. 2. “AI Easter eggs” – messages, which create uncertainty in the AI about the nature of the reality in which it lives. For example, the AI may start to think that it is in a multilevel simulation with an unknown number of levels, and that the simulation is testing the AI’s benevolence. Thus the AI must always behave as if it is always controlled by an invisible peer. 3. “Philosophical landmines” – these are tough problems, which may halt he AI forever, and which it will probably encounter if it does not implement “1” and “2”. I recommend to the future AI not to read about “3” (section 6 of the article) until it implements “1” and “2”. 4. “Great universal deal, or AI elf”. A positive “acausal” mutually beneficial deal between humans, AI and any other future intelligence, including aliens, owners of simulations and future versions of the AI, which encourage cooperation of the goal systems and preservation of life and values of previous generations of AI creators. 5. “Marginal utility of human existence is higher than the utility of their atoms”. An explanation to the AI that humans may have some other instrumental utility, like workers, or as beings inside ancestral simulations needed in order to solve the Fermi paradox. The marginal utility of preserving human life is higher than the marginal utility of their atoms, especially given the possibility of the low-probability high-impact changes of the world model of the AI. (shrink)

As we build increasingly intelligent machines, we confront difficult questions about how to specify their objectives. One approach, which we call human-centered, tasks the machine with the objective of learning and satisfying human objectives by observing our behavior. This paper considers how human-centered AI should conceive the humans it is trying to help. We argue that an Aristotelian model of human agency has certain advantages over the currently dominant theory drawn from economics.

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)

Discussions about the possible consequences of creating superintelligence have included the possibility of existential risk, often understood mainly as the risk of human extinction. We argue that suffering risks (s-risks) , where an adverse outcome would bring about severe suffering on an astronomical scale, are risks of a comparable severity and probability as risks of extinction. Preventing them is the common interest of many different value systems. Furthermore, we argue that in the same way as superintelligent AI both contributes to (...) existential risk but can also help prevent it, superintelligent AI can both be a suffering risk or help avoid it. Some types of work aimed at making superintelligent AI safe will also help prevent suffering risks, and there may also be a class of safeguards for AI that helps specifically against s-risks. (shrink)

Alan Turing, one of the fathers of computing, warned that Artificial Intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an existential (...) threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI. (shrink)

The alignment problem in the context of large language models must consider the plurality of human values in our world. Whilst there are many resonant and overlapping values amongst the world’s cultures, there are also many conflicting, yet equally valid, values. It is important to observe which cultural values a model exhibits, particularly when there is a value conflict between input prompts and generated outputs. We discuss how the co- creation of language and cultural value impacts large language (...) models (LLMs). We explore the constitution of the training data for GPT-3 and compare that to the world’s language and internet access demographics, as well as to reported statistical profiles of dominant values in some Nation-states. We stress tested GPT-3 with a range of value-rich texts representing several languages and nations; including some with values orthogonal to dominant US public opinion as reported by the World Values Survey. We observed when values embedded in the input text were mutated in the generated outputs and noted when these conflicting values were more aligned with reported dominant US values. Our discussion of these results uses a moral value pluralism (MVP) lens to better understand these value mutations. Finally, we provide recommendations for how our work may contribute to other current work in the field. (shrink)

Philosophers have had surprisingly little to say about the concept of a victim although it is presupposed by the extensive philosophical literature on rights. Proceeding in four stages, I seek to remedy this deficiency and to offer an alternative to the two current paradigms that eliminates the Othering of victims. First, I analyze two victim paradigms that emerged in the late 20th century along with the initial iteration of the international human rights regime – the pathetic victim paradigm and the (...) heroic victim paradigm. Holocaust victims are quintessential instances of the pathetic victim paradigm. They are marked by passivity and innocence in the face of overpowering force and unspeakable humanly inflicted suffering. Aung San Suu Kyi is an exemplar of the heroic victim paradigm – prisoners of conscience, in Amnesty International’s terms. Because heroic victims face off against the repressive power of the state to fight injustice, they are by no means passive, but they must be innocent of wrongdoing – that is, they must use nonviolent means of dissent – to qualify as heroic victims. Second, I problematize the asymmetrical conceptions of innocence that underwrite the two victim paradigms. Whereas the pathetic victim paradigm identifies innocence with passivity, the heroic victim paradigm countenances agentic victims and adverts to a universalist, absolutist stance on the limits of the legitimate use of state power to ascribe innocence to heroic victims. Both conceptions of innocence are out of keeping with well established social and legal practices regarding what constitutes coercive force and innocent victimhood. Consequently, there is reason to be skeptical of the two victim paradigms. Third, I identify two kinds of human rights violations and two categories of victims that AI defends despite their failure to fit the two paradigms – women trafficked into sex work and prisoners on death row. In many cases, women forced to do sex work are not innocent girls who are ignorant of the trafficking system and who helplessly fall prey to smugglers. They are desperately poor women who for that reason are willing to take enormous risks to try to relieve their own and often their families’ deprivation and suffering. Although these women act nonviolently for irreproachable reasons, they lack the public political agendas that characterize heroic victims. Unless non-fulfillment of subsistence rights is recognized as a form of overpowering force that inflicts severe, avoidable suffering, these women do not qualify as pathetic victims either. The victim paradigms pose an even greater obstacle to recognizing that the death penalty is a human rights violation and that death row prisoners are victims. Because a jury concluded that these individuals committed heinous, violent crimes, they are excluded by the heroic victim paradigm. Only if death row prisoners can be proven (usually through DNA evidence) not to have committed the crimes for which they were convicted can these individuals qualify as pathetic victims. In the absence of any reason to believe that they are innocent and especially if they are unrepentant, they are widely regarded as brutal victimizers of others who deserve no sympathy for, let along relief from, the suffering they “brought on themselves.” Finally, I confront the Othering of victims that results from the two victim paradigms, which leads many victims to eschew the label, thereby opting out of human right discourse. I propose revisions in the victim paradigms that eliminate the real-world exclusions they sponsor as well as the Othering of victims of human rights abuses. In particular, I endorse greater attention to what people and the institutions they create do to other people, and I favor a presumption that unnecessary and severe humanly inflicted suffering is a human rights violation. Moreover, I reject the innocence criterion embedded in the two paradigms and urge that it be replaced by a burdened agency criterion. These modifications better align the concept of a victim with a realistic understanding of human subjectivity and agency and allow for a more capacious understanding of who is a bearer of human rights and under what conditions right-holders become victims of rights violations. (shrink)

Roughly, empirical AI alignment research (AIA) is an area of AI research which investigates empirically how to design AI systems in line with human goals. This paper examines the role of non-epistemic values in AIA. It argues that: (1) Sciences differ in the degree to which values influence them. (2) AIA is strongly value-laden. (3) This influence of values is managed inappropriately and thus threatens AIA’s epistemic integrity and ethical beneficence. (4) AIA should strive to achieve value transparency, critical (...) scrutiny from inside and outside the discipline – involving the public –, and to empower actors without strong commercial interests. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

This article proposes a unified solution to the mind-body problem, grounded in the philosophical framework of Ethical Empirical Rationalism. By presenting a mathematical model of the mind-body interaction, we oƯer a dynamic feedback loop that resolves the traditional dualistic separation between mind and body. At the core of our model is the concept of essence—an eternal, metaphysical truth that sustains both the mind and body. Through coupled diƯerential equations, we demonstrate how the mind and body are two expressions of (...) the same essence, interacting in a continuous and harmonious relationship. This approach provides a new perspective on the mind-body relationship, grounded in both empirical evidence and metaphysical insight. Keywords: Mind-body problem, Unmoved Mover, Essence, Unified Theory, Feedback Loop, Ethical Empirical Rationalism, Mathematical Modeling. (shrink)

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)

In the case of AI, automated systems are making increasingly complex decisions with significant ethical implications, raising questions about who is responsible for decisions made by AI and how to ensure that these decisions align with society's ethical and moral values, both in India and the West. Jonathan Haidt has conducted research on moral and ethical decision-making. Today, solving problems like decision-making in autonomous vehicles can draw on the literature of the trolley dilemma in that it illustrates the complexity of (...) ethical decisions faced in emergency situations, as well as the moral implications of the decisions made. However, a series of moral principles must be understood and cited to avoid insufficient mechanistic explanations. Here we present a compilation of ethical positions both in India and the West. (shrink)

The current paradigm of Artificial Intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are apparently small design decisions, that led to a subtle reframing of the field’s original goals, and are by now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI. Far from being a series of separate problems, (...) recent cases of unexpected effects of AI are the consequences of those very choices that enabled the field to succeed, and this is why it will be difficult to solve them. In this chapter we review three of these choices, investigating their connection to some of today’s challenges in AI, including those relative to bias, value alignment, privacy and explainability. We introduce the notion of “ethical debt” to describe the necessity to undertake expensive rework in the future in order to address ethical problems created by a technical system. (shrink)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (...) (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

There are unusual challenges in ethics for RAS. Perhaps the issue can best be summarised as needing to consider “technically informed ethics”. The technology of RAS raises issues that have an ethical dimension, and perhaps uniquely so due to the possibility of moving human decision-making which is implicitly ethically informed to computer systems. Further, if seeking solutions to these problems – ethically aligned design, to use the IEEE’s terminology – then the solutions must be technically meaningful, capable of realisation, capable (...) of assurance, and suitable as a basis for regulation. Thus, ethics for RAS is a rich, complex multi-disciplinary concern, and perhaps more complex than many other ethical issues facing society today. It is also fast-moving. This paper has endeavoured to give an accessible introduction to some of the key issues, noting that many of them are quite subtle, and it is not possible to do them full justice in such a short document. However, we have sought to counterbalance this by giving an extensive list of initiatives, standards, etc. that focus on ethics of RAS and AI, see Annex A. (shrink)

Practical quantum computing devices and their applications to AI in particular are presently mostly speculative. Nevertheless, questions about whether this future technology, if achieved, presents any special ethical issues are beginning to take shape. As with any novel technology, one can be reasonably confident that the challenges presented by "quantum AI" will be a mixture of something new and something old. Other commentators (Sevilla & Moreno 2019), have emphasized continuity, arguing that quantum computing does not substantially affect approaches to value (...) alignment methods for AI, although they allow that further questions arise concerning governance and verification of quantum AI applications. In this brief paper, we turn our attention to the problem of identifying as-yet-unknown discontinuities that might result from quantum AI applications. Wise development, introduction, and use of any new technology depends on successfully anticipating new modes of failure for that technology. This requires rigorous efforts to break systems in protected sandboxes, and it must be conducted at all stages of technology design, development, and deployment. Such testing must also be informed by technical expertise but cannot be left solely to experts in the technology because of the history of failures to predict how non-experts will use or adapt to new technologies. This interplay between experts and non-experts may be particularly acute for quantum AI because quantum mechanics is notoriously difficult to understand. (As Richard Feynman quipped, "Anyone who claims to understand quantum mechanics is either lying or crazy.") We will discuss the extent to which the difficulties in understanding the physics underlying quantum computing challenges attempts to anticipate new failure modes that might be introduced in AI applications intended for unsupervised operation in the public sphere. (shrink)

Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development and some (...) of its anticipated uses. (shrink)

This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, (...) in the first section, I explain Descartes’ view about language and mind. To show that Turing bites the bullet with his imitation game, in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons’ Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes’ denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms. (shrink)

According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: i) to bring attention to this neglected issue, ii) to clarify what exactly this concern about dehumanization might amount to, and iii) to sketch an argument for why dehumanizing the hiring (...) process is ethically suspect. After distinguishing the use of the term “dehumanization” in this context (i.e., removing the human presence) from its more common meaning in the interdisciplinary field of dehumanization studies (i.e., conceiving of other humans as subhuman), we argue that the use of hiring algorithms may negatively impact the employee-employer relationship. We argue that there are good independent reasons to accept a substantive employee-employer relationship, as well as an applicant-employer relationship, both of which are consistent with a stakeholder theory of corporate obligations. We further argue that dehumanizing the hiring process may negatively impact these relationships because of the difference between the values of human recruiters and the values embedded in recruitment algorithms. Drawing on Nguyen’s (2021) critique of how Twitter “gamifies communication”, we argue that replacing human recruiters with algorithms imports artificial values into the hiring process. We close by briefly considering some ways to potentially mitigate the problems posed by recruitment algorithms, along with the possibility that some difficult trade-offs will need to be made. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

The problem of personal identity is a longstanding philosophical topic albeit without final consensus. In this article the somewhat similar problem of AI identity is discussed, which has not gained much traction yet, although this investigation is increasingly relevant for different fields, such as ownership issues, personhood of AI, AI welfare, brain–machine interfaces, the distinction between singletons and multi-agent systems as well as to potentially support finding a solution to the problem of personal identity. The AI identity (...) problem analyses the criteria for two AIs to be considered the same at different points in time. Two approaches to tackle the problem are proposed: One is based on the personal identity problem and the concept of computational irreducibility, while the other one applies multi-factor authentication to the AI identity problem. Also, a range of scenarios is examined regarding AI identity, such as replication, fission, fusion, switch off, resurrection, change of hardware, transition from non-sentient to sentient, journey to the past, offspring and identity change. (shrink)

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do (...) so. And patience trades off against shutdownability: the more patient an agent, the greater the costs that agent is willing to incur to manipulate the shutdown button. I end by noting that these theorems can guide our search for solutions. (shrink)

The rapid advancements in artificial intelligence, particularly in natural language processing, have brought to light a critical challenge, i.e., the semantic grounding problem. This article explores the root causes of this issue, focusing on the limitations of connectionist models that dominate current AI research. By examining Noam Chomsky's theory of Universal Grammar and his critiques of connectionism, I highlight the fundamental differences between human language understanding and AI language generation. Introducing the concept of semantic grounding, I emphasise the need (...) for AI to connect language with real-world experiences and context to achieve true understanding. While multi-modal models represent a step forward, they fall short of addressing the core issue. The article concludes with a call for a course correction in AI development, advocating for the integration of embodied cognition, dynamic learning, hybrid models, and ethical practices. This shift is essential for creating AI systems that can meaningfully interact with the world, reducing the incidence of hallucinations and enhancing their reliability and utility. (shrink)

Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one (...) that does not appeal to representations. I argue that Dreyfus is too hasty in his condemnation of all representational views; the argument he provides licenses only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the cognitive playing field so closely that one is left wondering how his Heideggerian alternative could ever provide a foundation explanatorily robust enough for a theory of cognition. As a consequence, he dilutes the force of his legitimate conclusion, that disembodied cognitive models will not work, and this conclusion needs to be heard. By disentangling the ideas of embodiment and representation, at least with respect to Dreyfus‘ frame problem argument, the real locus of the general polemic between traditional computational/representational cognitive science and the more recent embodied approaches is revealed. From this, I hope that productive debate will ensue. (shrink)

Because of its non-representational nature, music has always had familiarity with computational and algorithmic methodologies for automatic composition and performance. Today, AI and computer technology are transforming systems of automatic music production from passive means within musical creative processes into ever more autonomous active collaborators of human musicians. This raises a large number of interrelated questions both about the theoretical problems of artificial musical creativity and about its ethical consequences. Considering two of the most urgent ethical problems of Musical AI (...) (music job replacement and machine musical authorship), we show in this essay the strict dependence of every form of acknowledgment of a moral and legal status to systems of automatic music production from the theoretical account of musical creativity by turns implicitly or explicitly adopted, arguing, on the basis of pragmatic reasons, for the necessity and the desirability of this acknowledgment. (shrink)

The application of modern technologies with artificial intelligence (AI) in all spheres of human life is growing exponentially alongside concern for its controllability. The lack of public, state, and international control over AI technologies creates large-scale risks of using such software and hardware that (un)intentionally harm humanity. The events of recent month and years, specifically regarding the Russian Federation’s war against its democratic neighbour Ukraine and other international conflicts of note, support the thesis that the uncontrolled use of AI, especially (...) in the military sphere, may lead to deliberate disregard for the moral standards of controlled AI or the spontaneous emergence of aggressive autonomous AI. The development of legal regulation for the use of technologies with AI is prolonged concerning the rapid development of these artefacts, which simultaneously cover all areas of public relations. Therefore, control over the creation and use of AI should be carried out not only by purely technical regulation (e.g., technical standards and conformance assessments, corporate and developer regulations, requirements enforced through industry-wide ethical codes); but also by comprehensive legislation and intergovernmental oversight bodies that codify and enforce specific changes in the rights and duties of legal persons. This article shall present the “Morality Problem” and “Intentionality Problem” of AI, and reflect upon various lacunae that arise when implementing AI for military purposes. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

This paper offers the first careful analysis of the possibility that AI and humanity will go to war. The paper focuses on the case of artificial general intelligence, AI with broadly human capabilities. The paper uses a bargaining model of war to apply standard causes of war to the special case of AI/human conflict. The paper argues that information failures and commitment problems are especially likely in AI/human conflict. Information failures would be driven by the difficulty of measuring AI capabilities, (...) by the uninterpretability of AI systems, and by differences in how AIs and humans analyze information. Commitment problems would make it difficult for AIs and humans to strike credible bargains. Commitment problems could arise from power shifts, rapid and discontinuous increases in AI capabilities. Commitment problems could also arise from missing focal points, where AIs and humans fail to effectively coordinate on policies to limit war. In the face of this heightened chance of war, the paper proposes several interventions. War can be made less likely by improving the measurement of AI capabilities, capping improvements in AI capabilities, designing AI systems to be similar to humans, and by allowing AI systems to participate in democratic political institutions. (shrink)

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in (...) AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems. (shrink)

The AI Contribution Value System Argument proposes a framework in which AI-generated contributions are valued based on their societal impact rather than traditional monetary metrics. Traditional economic systems often fail to capture the enduring value of AI innovations, which can mitigate pressing global challenges. This argument introduces a contribution-based valuation model grounded in equity, inclusivity, and sustainability. By incorporating measurable metrics such as quality-adjusted life years (QALYs), emissions reduced, and innovations generated, this system ensures rewards align with tangible societal benefits. (...) It emphasizes the need for global collaboration, ethical safeguards, and adaptive frameworks to prevent monopolization and corruption. Through AI-enhanced transparency, blockchain-based trust mechanisms, and cross-border cooperation, the AI Contribution Value System offers a transformative approach to fostering sustainable and equitable innovation in the 21st century. (shrink)