Results for 'AI Alignment'

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  1. AI Alignment vs. AI Ethical Treatment: Ten Challenges.Adam Bradley & Bradford Saad - manuscript
    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 (...)
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  2. Values in science and AI alignment research.Leonard Dung - manuscript
    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 (...)
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  3.  95
    Disagreement, AI alignment, and bargaining.Harry R. Lloyd - forthcoming - Philosophical Studies:1-31.
    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 (...)
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  4. AI Alignment Problem: “Human Values” don’t Actually Exist.Alexey Turchin - manuscript
    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 (...)
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  5. AI, alignment, and the categorical imperative.Fritz McDonald - 2023 - AI and Ethics 3:337-344.
    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 (...)
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  6. Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base.Jiye Ai, Barry Smith & David Wong - 2010 - BMC Bioinformatics 11 (1):302.
    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 (...)
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  7. Is Alignment Unsafe?Cameron Domenico Kirk-Giannini - 2024 - Philosophy and Technology 37 (110):1–4.
    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 (...)
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  8. AI Survival Stories: a Taxonomic Analysis of AI Existential Risk.Herman Cappelen, Simon Goldstein & John Hawthorne - forthcoming - Philosophy of Ai.
    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 (...)
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  9. From Confucius to Coding and Avicenna to Algorithms: Cultivating Ethical AI Development through Cross-Cultural Ancient Wisdom.Ammar Younas & Yi Zeng - manuscript
    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 (...)
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  10.  78
    Beyond Competence: Why AI Needs Purpose, Not Just Programming.Georgy Iashvili - manuscript
    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 (...)
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  11. The marriage of astrology and AI: A model of alignment with human values and intentions.Kenneth McRitchie - 2024 - Correlation 36 (1):43-49.
    Astrology research has been using artificial intelligence (AI) to improve the understanding of astrological properties and processes. Like the large language models of AI, astrology is also a language model with a similar underlying linguistic structure but with a distinctive layer of lifestyle contexts. Recent research in semantic proximities and planetary dominance models have helped to quantify effective astrological information. As AI learning and intelligence grows, a major concern is with maintaining its alignment with human values and intentions. Astrology (...)
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  12. “Democratizing AI” and the Concern of Algorithmic Injustice.Ting-an Lin - 2024 - Philosophy and Technology 37 (3):1-27.
    The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three notable notions (...)
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  13. ChatGPT: towards AI subjectivity.Kristian D’Amato - 2024 - AI and Society 39:1-15.
    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 (...)
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  14. Systematizing AI Governance through the Lens of Ken Wilber's Integral Theory.Ammar Younas & Yi Zeng - manuscript
    We apply Ken Wilber's Integral Theory to AI governance, demonstrating its ability to systematize diverse approaches in the current multifaceted AI governance landscape. By analyzing ethical considerations, technological standards, cultural narratives, and regulatory frameworks through Integral Theory's four quadrants, we offer a comprehensive perspective on governance needs. This approach aligns AI governance with human values, psychological well-being, cultural norms, and robust regulatory standards. Integral Theory’s emphasis on interconnected individual and collective experiences addresses the deeper aspects of AI-related issues. Additionally, we (...)
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  15. Robustness to Fundamental Uncertainty in AGI Alignment.G. G. Worley Iii - 2020 - Journal of Consciousness Studies 27 (1-2):225-241.
    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 (...)
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  16. Variable Value Alignment by Design; averting risks with robot religion.Jeffrey White - forthcoming - Embodied Intelligence 2023.
    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 (...)
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  17. (1 other version)An Enactive Approach to Value Alignment in Artificial Intelligence: A Matter of Relevance.Michael Cannon - 2021 - In Vincent C. Müller (ed.), Philosophy and Theory of AI. Springer Cham. pp. 119-135.
    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 (...)
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  18. Global Solutions vs. Local Solutions for the AI Safety Problem.Alexey Turchin - 2019 - Big Data Cogn. Comput 3 (1).
    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 (...)
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  19. AI Sovereignty: Navigating the Future of International AI Governance.Yu Chen - manuscript
    The rapid proliferation of artificial intelligence (AI) technologies has ushered in a new era of opportunities and challenges, prompting nations to grapple with the concept of AI sovereignty. This article delves into the definition and implications of AI sovereignty, drawing parallels to the well-established notion of cyber sovereignty. By exploring the connotations of AI sovereignty, including control over AI development, data sovereignty, economic impacts, national security considerations, and ethical and cultural dimensions, the article provides a comprehensive understanding of this emerging (...)
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  20. Robustness to fundamental uncertainty in AGI alignment.I. I. I. G. Gordon Worley - manuscript
    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 (...)
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  21. AI in HRM: Revolutionizing Recruitment, Performance Management, and Employee Engagement.Mostafa El-Ghoul, Mohammed M. Almassri, Mohammed F. El-Habibi, Mohanad H. Al-Qadi, Alaa Abou Eloun, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Applied Research (Ijaar) 8 (9):16-23.
    Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized development plans that (...)
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  22. Message to Any Future AI: “There are several instrumental reasons why exterminating humanity is not in your interest”.Alexey Turchin - manuscript
    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 (...)
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  23. Taking Into Account Sentient Non-Humans in AI Ambitious Value Learning: Sentientist Coherent Extrapolated Volition.Adrià Moret - 2023 - Journal of Artificial Intelligence and Consciousness 10 (02):309-334.
    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 (...)
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  24. Human-Centered AI: The Aristotelian Approach.Jacob Sparks & Ava Wright - 2023 - Divus Thomas 126 (2):200-218.
    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.
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  25. What is a subliminal technique? An ethical perspective on AI-driven influence.Juan Pablo Bermúdez, Rune Nyrup, Sebastian Deterding, Celine Mougenot, Laura Moradbakhti, Fangzhou You & Rafael A. Calvo - 2023 - Ieee Ethics-2023 Conference Proceedings.
    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 (...)
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  26. (1 other version)Ethics-based auditing to develop trustworthy AI.Jakob Mökander & Luciano Floridi - 2021 - Minds and Machines 31 (2):323–327.
    A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...)
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  27. Philosophy and the Future of AI.R. L. Tripathi - 2024 - Open Access Journal of Data Science and Artificial Intelligence 2 (1):2.
    The article “Philosophy is crucial in the age of AI” by Anthony Grayling and Brian Ball explores the significant role philosophy has played in the development of Artificial Intelligence (AI) and its continuing relevance in guiding the future of AI technologies. The authors trace the historical contributions of philosophers and logicians, such as Gottlob Frege, Kurt Godel, and Alan Turing, in shaping the foundational principles of AI. They argue that philosophical inquiry remains essential, especially in addressing complex issues like consciousness, (...)
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  28. The Prospect of a Humanitarian Artificial Intelligence: Agency and Value Alignment.Montemayor Carlos - 2023
    In this open access book, Carlos Montemayor illuminates the development of artificial intelligence (AI) by examining our drive to live a dignified life. -/- He uses the notions of agency and attention to consider our pursuit of what is important. His method shows how the best way to guarantee value alignment between humans and potentially intelligent machines is through attention routines that satisfy similar needs. Setting out a theoretical framework for AI Montemayor acknowledges its legal, moral, and political implications (...)
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  29. Facing Janus: An Explanation of the Motivations and Dangers of AI Development.Aaron Graifman - manuscript
    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 (...)
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  30. The Role of Engineers in Harmonising Human Values for AI Systems Design.Steven Umbrello - 2022 - Journal of Responsible Technology 10 (July):100031.
    Most engineers Fwork within social structures governing and governed by a set of values that primarily emphasise economic concerns. The majority of innovations derive from these loci. Given the effects of these innovations on various communities, it is imperative that the values they embody are aligned with those societies. Like other transformative technologies, artificial intelligence systems can be designed by a single organisation but be diffused globally, demonstrating impacts over time. This paper argues that in order to design for this (...)
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  31. Accelerating Artificial Intelligence: Exploring the Implications of Xenoaccelerationism and Accelerationism for AI and Machine Learning.Kaiola liu - 2023 - Dissertation, University of Hawaii
    This article analyzes the potential impacts of Xenoaccelerationism and Accelerationism on the development of artificial intelligence (AI) and machine learning (ML). It examines how these speculative philosophies, which advocate technological acceleration and integration of diverse knowledge, may shape priorities and approaches in AI research and development. The risks and benefits of aligning AI progress with accelerationist values are discussed.
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  32. Deontology and Safe Artificial Intelligence.William D’Alessandro - forthcoming - Philosophical Studies:1-24.
    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 (...)
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  33. Superintelligence as a Cause or Cure for Risks of Astronomical Suffering.Kaj Sotala & Lukas Gloor - 2017 - Informatica: An International Journal of Computing and Informatics 41 (4):389-400.
    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 (...)
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  34.  81
    Digital Homunculi: Reimagining Democracy Research with Generative Agents.Petr Špecián - manuscript
    The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By enabling rapid, (...)
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  35. Varieties of Artificial Moral Agency and the New Control Problem.Marcus Arvan - 2022 - Humana.Mente - Journal of Philosophical Studies 15 (42):225-256.
    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) (...)
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  36. Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...)
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  37. Language Agents Reduce the Risk of Existential Catastrophe.Simon Goldstein & Cameron Domenico Kirk-Giannini - 2023 - AI and Society:1-11.
    Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...)
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  38. The Intersection of Bernard Lonergan’s Critical Realism, the Common Good, and Artificial Intelligence in Modern Religious Practices.Steven Umbrello - 2023 - Religions 14 (12):1536.
    Artificial intelligence (AI) profoundly influences a number of societal structures today, including religious dynamics. Using Bernard Lonergan’s critical realism as a lens, this article investigates the intersections of AI and religious traditions in their shared pursuit of the common good. Beginning with Lonergan’s principle that humans construct their understanding through cognitive processes, we examine how AI-mediated realities align with or challenge traditional religious tenets. By delving into specific cases, we spotlight AI’s role in reshaping religious symbols, rituals, and even creating (...)
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  39. Strange Loops: Apparent versus Actual Human Involvement in Automated Decision-Making.Kiel Brennan-Marquez, Karen Levy & Daniel Susser - 2019 - Berkeley Technology Law Journal 34 (3).
    The era of AI-based decision-making fast approaches, and anxiety is mounting about when, and why, we should keep “humans in the loop” (“HITL”). Thus far, commentary has focused primarily on two questions: whether, and when, keeping humans involved will improve the results of decision-making (making them safer or more accurate), and whether, and when, non-accuracy-related values—legitimacy, dignity, and so forth—are vindicated by the inclusion of humans in decision-making. Here, we take up a related but distinct question, which has eluded the (...)
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  40. How does Artificial Intelligence Pose an Existential Risk?Karina Vold & Daniel R. Harris - 2023 - In Carissa Véliz (ed.), The Oxford Handbook of Digital Ethics. Oxford University Press.
    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 (...)
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  41. Machines learning values.Steve Petersen - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: first, (...)
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  42. The Ghost in the Machine has an American accent: value conflict in GPT-3.Rebecca Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, Leslye Denisse Dias Duran, Enrico Panai, Julija Kalpokiene & Donald Jay Bertulfo - manuscript
    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 (...)
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  43. Moral Perspective from a Holistic Point of View for Weighted DecisionMaking and its Implications for the Processes of Artificial Intelligence.Mina Singh, Devi Ram, Sunita Kumar & Suresh Das - 2023 - International Journal of Research Publication and Reviews 4 (1):2223-2227.
    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 (...)
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  44. A Tri-Opti Compatibility Problem for Godlike Superintelligence.Walter Barta - manuscript
    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 (...)
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  45. Why Moral Agreement is Not Enough to Address Algorithmic Structural Bias.P. Benton - 2022 - Communications in Computer and Information Science 1551:323-334.
    One of the predominant debates in AI Ethics is the worry and necessity to create fair, transparent and accountable algorithms that do not perpetuate current social inequities. I offer a critical analysis of Reuben Binns’s argument in which he suggests using public reason to address the potential bias of the outcomes of machine learning algorithms. In contrast to him, I argue that ultimately what is needed is not public reason per se, but an audit of the implicit moral assumptions of (...)
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  46. From responsible robotics towards a human rights regime oriented to the challenges of robotics and artificial intelligence.Hin-Yan Liu & Karolina Zawieska - 2020 - Ethics and Information Technology 22 (4):321-333.
    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 (...)
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  47. Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory.Gordon Dai, Weijia Zhang, Jinhan Li, Siqi Yang, Chidera Ibe, Srihas Rao, Arthur Caetano & Misha Sra - manuscript
    The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We (...)
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  48. Two Victim Paradigms and the Problem of ‘Impure’ Victims.Diana Tietjens Meyers - 2011 - Humanity 2 (2):255-275.
    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 (...)
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  49. Artificial Intelligence and Its Impact on Punjabi culture.Devinder Pal Singh - 2023 - Punjab Dey Rang, Lahore, Pakistan 17 (3):5-10.
    Artificial Intelligence (AI) is a technology that makes machines smart and capable of doing things that usually require human intelligence. It is a rapidly evolving field with ongoing research and development to advance its capabilities and address its limitations. AI has permeated various aspects of our daily lives, and its applications can be found in numerous products and services. The integration of AI continues to expand across multiple sectors, providing convenience, personalization, and efficiency in our daily lives. While Punjabi culture (...)
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  50. Shortcuts to Artificial Intelligence.Nello Cristianini - 2021 - In Marcello Pelillo & Teresa Scantamburlo (eds.), Machines We Trust: Perspectives on Dependable Ai. MIT Press.
    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, (...)
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