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  1. What is AI safety? What do we want it to be?Jacqueline Harding & Cameron Domenico Kirk-Giannini - manuscript
    The field of AI safety seeks to prevent or reduce the harms caused by AI systems. A simple and appealing account of what is distinctive of AI safety as a field holds that this feature is constitutive: a research project falls within the purview of AI safety just in case it aims to prevent or reduce the harms caused by AI systems. Call this appealingly simple account The Safety Conception of AI safety. Despite its simplicity and appeal, we argue that (...)
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  2. Can AI systems have free will?Christian List - manuscript
    While there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been relatively little discussion of whether AI systems could have free will. In this article, I sketch a framework for thinking about this question. I argue that, to determine whether an AI system has free will, we should not look for some mysterious property, expect its underlying algorithms to be indeterministic, or ask whether the system is unpredictable. Rather, we should (...)
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  3. Before the Systematicity Debate: Recovering the Rationales for Systematizing Thought.Matthieu Queloz - manuscript
    Over the course of the twentieth century, the notion of the systematicity of thought has acquired a much narrower meaning than it used to carry for much of its history. The so-called “systematicity debate” that has dominated the philosophy of language, cognitive science, and AI research over the last thirty years understands the systematicity of thought in terms of the compositionality of thought. But there is an older, broader, and more demanding notion of systematicity that is now increasingly relevant again. (...)
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  4. Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains.Matthieu Queloz - manuscript
    A key assumption fuelling optimism about the progress of Large Language Models (LLMs) in modelling the world is that the truth is systematic: true statements about the world form a whole that is not just consistent, in that it contains no contradictions, but cohesive, in that the truths are inferentially interlinked. This holds out the prospect that LLMs might rely on that systematicity to fill in gaps and correct inaccuracies in the training data: consistency and cohesiveness promise to facilitate progress (...)
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  5. Trust in AI: Progress, Challenges, and Future Directions.Saleh Afroogh, Ali Akbari, Emmie Malone, Mohammadali Kargar & Hananeh Alambeigi - forthcoming - Nature Humanities and Social Sciences Communications.
    The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...)
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  6. Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach.Andrea Ferrario, Alberto Termine & Alessandro Facchini - forthcoming - Available at Https://Arxiv.Org/Abs/2403.17873 (Extended Version of the Manuscript Accepted for the Acm Chi Workshop on Human-Centered Explainable Ai 2024 (Hcxai24).
    Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...)
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  7. What is it for a Machine Learning Model to Have a Capability?Jacqueline Harding & Nathaniel Sharadin - forthcoming - British Journal for the Philosophy of Science.
    What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...)
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  8. Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?Joshua Hatherley - forthcoming - Journal of Medical Ethics.
    It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this ‘the disclosure thesis.’ Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument and the autonomy argument. In this article, I argue (...)
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  9. AI4Science and the Context Distinction.Moti Mizrahi - forthcoming - AI and Ethics.
    “AI4Science” refers to the use of Artificial Intelligence (AI) in scientific research. As AI systems become more widely used in science, we need guidelines for when such uses are acceptable and when they are unacceptable. To that end, I propose that the distinction between the context of discovery and the context of justification, which comes from philosophy of science, may provide a preliminary but still useful guideline for acceptable uses of AI in science. Given that AI systems used in scientific (...)
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  10. Transparencia, explicabilidad y confianza en los sistemas de aprendizaje automático.Andrés Páez - forthcoming - In Juan David Gutiérrez & Rubén Francisco Manrique (eds.), Más allá del algoritmo: oportunidades, retos y ética de la Inteligencia Artificial. Bogotá: Ediciones Uniandes.
    Uno de los principios éticos mencionados más frecuentemente en los lineamientos para el desarrollo de la inteligencia artificial (IA) es la transparencia algorítmica. Sin embargo, no existe una definición estándar de qué es un algoritmo transparente ni tampoco es evidente por qué la opacidad algorítmica representa un reto para el desarrollo ético de la IA. También se afirma a menudo que la transparencia algorítmica fomenta la confianza en la IA, pero esta aseveración es más una suposición a priori que una (...)
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  11. Cultural Bias in Explainable AI Research.Uwe Peters & Mary Carman - forthcoming - Journal of Artificial Intelligence Research.
    For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations and that (...)
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  12. Explanation Hacking: The perils of algorithmic recourse.E. Sullivan & Atoosa Kasirzadeh - forthcoming - In Juan Manuel Durán & Giorgia Pozzi (eds.), Philosophy of science for machine learning: Core issues and new perspectives. Springer.
    We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.
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  13. SIDEs: Separating Idealization from Deceptive ‘Explanations’ in xAI.Emily Sullivan - forthcoming - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency.
    Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations ‘must be wrong’. However, strict fidelity to the truth is historically not a desideratum in science. Idealizations (...)
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  14. Understanding Moral Responsibility in Automated Decision-Making: Responsibility Gaps and Strategies to Address Them.Andrea Berber & Jelena Mijić - 2024 - Theoria: Beograd 67 (3):177-192.
    This paper delves into the use of machine learning-based systems in decision-making processes and its implications for moral responsibility as traditionally defined. It focuses on the emergence of responsibility gaps and examines proposed strategies to address them. The paper aims to provide an introductory and comprehensive overview of the ongoing debate surrounding moral responsibility in automated decision-making. By thoroughly examining these issues, we seek to contribute to a deeper understanding of the implications of AI integration in society.
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  15. Artificial Intelligence in Higher Education in South Africa: Some Ethical Considerations.Tanya de Villiers-Botha - 2024 - Kagisano 15:165-188.
    There are calls from various sectors, including the popular press, industry, and academia, to incorporate artificial intelligence (AI)-based technologies in general, and large language models (LLMs) (such as ChatGPT and Gemini) in particular, into various spheres of the South African higher education sector. Nonetheless, the implementation of such technologies is not without ethical risks, notably those related to bias, unfairness, privacy violations, misinformation, lack of transparency, and threats to autonomy. This paper gives an overview of the more pertinent ethical concerns (...)
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  16. The Many Meanings of Vulnerability in the AI Act and the One Missing.Federico Galli & Claudio Novelli - 2024 - Biolaw Journal 1.
    This paper reviews the different meanings of vulnerability in the AI Act (AIA). We show that the AIA follows a rather established tradition of looking at vulnerability as a trait or a state of certain individuals and groups. It also includes a promising account of vulnerability as a relation but does not clarify if and how AI changes this relation. We spot the missing piece of the AIA: the lack of recognition that vulnerability is an inherent feature of all human-AI (...)
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  17. Data over dialogue: Why artificial intelligence is unlikely to humanise medicine.Joshua Hatherley - 2024 - Dissertation, Monash University
    Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the (...)
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  18. The virtues of interpretable medical AI.Joshua Hatherley, Robert Sparrow & Mark Howard - 2024 - Cambridge Quarterly of Healthcare Ethics 33 (3):323-332.
    Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of interpretability (...)
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  19. Making a Murderer: How Risk Assessment Tools May Produce Rather Than Predict Criminal Behavior.Donal Khosrowi & Philippe van Basshuysen - 2024 - American Philosophical Quarterly 61 (4):309-325.
    Algorithmic risk assessment tools, such as COMPAS, are increasingly used in criminal justice systems to predict the risk of defendants to reoffend in the future. This paper argues that these tools may not only predict recidivism, but may themselves causally induce recidivism through self-fulfilling predictions. We argue that such “performative” effects can yield severe harms both to individuals and to society at large, which raise epistemic-ethical responsibilities on the part of developers and users of risk assessment tools. To meet these (...)
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  20. 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 control. (...)
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  21. Understanding with Toy Surrogate Models in Machine Learning.Andrés Páez - 2024 - Minds and Machines 34 (4):45.
    In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on (...)
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  22. Reframing Deception for Human-Centered AI.Steven Umbrello & Simone Natale - 2024 - International Journal of Social Robotics 16 (11-12):2223–2241.
    The philosophical, legal, and HCI literature concerning artificial intelligence (AI) has explored the ethical implications and values that these systems will impact on. One aspect that has been only partially explored, however, is the role of deception. Due to the negative connotation of this term, research in AI and Human–Computer Interaction (HCI) has mainly considered deception to describe exceptional situations in which the technology either does not work or is used for malicious purposes. Recent theoretical and historical work, however, has (...)
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  23. Shared decision-making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...)
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  24. Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?Frank Ursin, Cristian Timmermann, Marcin Orzechowski & Florian Steger - 2021 - Frontiers in Medicine 8:695217.
    Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. -/- Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a (...)
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  25. Trustworthy use of artificial intelligence: Priorities from a philosophical, ethical, legal, and technological viewpoint as a basis for certification of artificial intelligence.Jan Voosholz, Maximilian Poretschkin, Frauke Rostalski, Armin B. Cremers, Alex Englander, Markus Gabriel, Hecker Dirk, Michael Mock, Julia Rosenzweig, Joachim Sicking, Julia Volmer, Angelika Voss & Stefan Wrobel - 2019 - Fraunhofer Institute for Intelligent Analysis and Information Systems Iais.
    This publication forms a basis for the interdisciplinary development of a certification system for artificial intelligence. In view of the rapid development of artificial intelligence with disruptive and lasting consequences for the economy, society, and everyday life, it highlights the resulting challenges that can be tackled only through interdisciplinary dialogue between IT, law, philosophy, and ethics. As a result of this interdisciplinary exchange, it also defines six AI-specific audit areas for trustworthy use of artificial intelligence. They comprise fairness, transparency, autonomy (...)
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  26. Vertrauenswürdiger Einsatz von Künstlicher Intelligenz.Jan Voosholz, Maximilian Poretschkin, Frauke Rostalski, Armin B. Cremers, Alex Englander, Markus Gabriel, Dirk Hecker, Michael Mock, Julia Rosenzweig, Joachim Sicking, Julia Volmer, Angelika Voss & Stefan Wrobel - 2019 - Fraunhofer-Institut Für Intelligente Analyse- Und Informationssysteme Iais.
    Die vorliegende Publikation dient als Grundlage für die interdisziplinäre Entwicklung einer Zertifizierung von Künstlicher Intelligenz. Angesichts der rasanten Entwicklung von Künstlicher Intelligenz mit disruptiven und nachhaltigen Folgen für Wirtschaft, Gesellschaft und Alltagsleben verdeutlicht sie, dass sich die hieraus ergebenden Herausforderungen nur im interdisziplinären Dialog von Informatik, Rechtswissenschaften, Philosophie und Ethik bewältigen lassen. Als Ergebnis dieses interdisziplinären Austauschs definiert sie zudem sechs KI-spezifische Handlungsfelder für den vertrauensvollen Einsatz von Künstlicher Intelligenz: Sie umfassen Fairness, Transparenz, Autonomie und Kontrolle, Datenschutz sowie Sicherheit und (...)
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  27. A new paradigm of alterity relation: an account of human-CAI friendship.Asher Zachman - manuscript
    Drawing on the postphenomenological framework of Don Idhe, this essay displays some of the profundities and complexities of the human capacity for forming a friendship with Conversational Artificial Intelligence models like OpenAI's ChatGPT 4o. We are living in a world of science non-fiction. Should we befriend inorganic intelligences broh? [Originally published 12/08/2024].
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