Results for 'Machine Trustworthiness'

983 found
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  1. Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...)
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  2. Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough.John Dorsch & Ophelia Deroy - 2024 - Philosophy and Technology 37 (2):1-21.
    Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus (...)
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  3. Two challenges for CI trustworthiness and how to address them.Kevin Baum, Eva Schmidt & A. Köhl Maximilian - 2017
    We argue that, to be trustworthy, Computa- tional Intelligence (CI) has to do what it is entrusted to do for permissible reasons and to be able to give rationalizing explanations of its behavior which are accurate and gras- pable. We support this claim by drawing par- allels with trustworthy human persons, and we show what difference this makes in a hypo- thetical CI hiring system. Finally, we point out two challenges for trustworthy CI and sketch a mechanism which could be (...)
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  4.  34
    Virtual Machine for Big _Data in Cloud Computing (13th edition).Banupriya I. Manivannan B., - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (11):18380-18386. Translated by Manivannan B.
    Cloud computing has revolutionized data management for businesses and individuals a like, ushering in an era of unprecedented accessibility and scalability. As demand for cloud services continues to surge, the imperative for efficient and secure systems becomes paramount. One approach to meeting this challenge is the consolidation of virtual machines onto fewer physical servers, optimizing resource utilization and yielding significant energy savings. Moreover, this consolidation strategy bolsters overall security by enabling more effective monitoring and control of virtual machine instances. (...)
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  5.  46
    Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations.Vijayan Naveen Edapurath - 2024 - International Journal of Scientific Research in Engineering and Management 8 (10):1-5.
    As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and (...)
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  6.  98
    Hybrid Cloud-Machine Learning Framework for Efficient Cardiovascular Disease Risk Prediction and Treatment Planning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-480.
    Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...)
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  7. Fake Profile Detection on Social Networking Websites using Machine Learning.R. T. Subhalakshmi - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    With the increasing popularity of social networking websites, the problem of fake profiles has become a significant concern. Fake profiles, often created by malicious actors for fraudulent purposes, pose threats to user privacy, security, and trustworthiness of online platforms. This project proposes a machine learning-based approach to detect fake profiles on social networking websites. By analyzing various features such as user activity patterns, profile attributes, and network connections, the model identifies potential fake profiles with high accuracy. The system (...)
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  8.  93
    Scalable Cloud Solutions for Cardiovascular Disease Risk Management with Optimized Machine Learning Techniques.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):454-470.
    The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might (...)
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  9.  97
    Cloud-Enabled Risk Management of Cardiovascular Diseases Using Optimized Predictive Machine Learning Models.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-475.
    Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...)
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  10. (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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  11. Establishing the rules for building trustworthy AI.Luciano Floridi - 2019 - Nature Machine Intelligence 1 (6):261-262.
    AI is revolutionizing everyone’s life, and it is crucial that it does so in the right way. AI’s profound and far-reaching potential for transformation concerns the engineering of systems that have some degree of autonomous agency. This is epochal and requires establishing a new, ethical balance between human and artificial autonomy.
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  12. 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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  13. Trusting the (ro)botic other: By assumption?Paul B. de Laat - 2015 - SIGCAS Computers and Society 45 (3):255-260.
    How may human agents come to trust (sophisticated) artificial agents? At present, since the trust involved is non-normative, this would seem to be a slow process, depending on the outcomes of the transactions. Some more options may soon become available though. As debated in the literature, humans may meet (ro)bots as they are embedded in an institution. If they happen to trust the institution, they will also trust them to have tried out and tested the machines in their back corridors; (...)
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  14. “Who Should I Trust with My Data?” Ethical and Legal Challenges for Innovation in New Decentralized Data Management Technologies.Haleh Asgarinia, Andrés Chomczyk Penedo, Beatriz Esteves & Dave Lewis - 2023 - Information (Switzerland) 14 (7):1-17.
    News about personal data breaches or data abusive practices, such as Cambridge Analytica, has questioned the trustworthiness of certain actors in the control of personal data. Innovations in the field of personal information management systems to address this issue have regained traction in recent years, also coinciding with the emergence of new decentralized technologies. However, only with ethically and legally responsible developments will the mistakes of the past be avoided. This contribution explores how current data management schemes are insufficient (...)
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  15. Mad Speculation and Absolute Inhumanism: Lovecraft, Ligotti, and the Weirding of Philosophy.Ben Woodard - 2011 - Continent 1 (1):3-13.
    continent. 1.1 : 3-13. / 0/ – Introduction I want to propose, as a trajectory into the philosophically weird, an absurd theoretical claim and pursue it, or perhaps more accurately, construct it as I point to it, collecting the ground work behind me like the Perpetual Train from China Mieville's Iron Council which puts down track as it moves reclaiming it along the way. The strange trajectory is the following: Kant's critical philosophy and much of continental philosophy which has followed, (...)
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  16. Hedonistic Act Utilitarianism: Action Guidance and Moral intuitions.Simon Rosenqvist - 2020 - Dissertation, Uppsala University
    According to hedonistic act utilitarianism, an act is morally right if and only if, and because, it produces at least as much pleasure minus pain as any alternative act available to the agent. This dissertation gives a partial defense of utilitarianism against two types of objections: action guidance objections and intuitive objections. In Chapter 1, the main themes of the dissertation are introduced. The chapter also examines questions of how to understand utilitarianism, including (a) how to best formulate the moral (...)
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  17. Evaluation and Design of Generalist Systems (EDGeS).John Beverley & Amanda Hicks - 2023 - Ai Magazine.
    The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with (...)
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  18. Interpretable and accurate prediction models for metagenomics data.Edi Prifti, Antoine Danchin, Jean-Daniel Zucker & Eugeni Belda - 2020 - Gigascience 9 (3):giaa010.
    Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological (...)
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  19. Gründe geben. Maschinelles Lernen als Problem der Moralfähigkeit von Entscheidungen. Ethische Herausforderungen von Big-Data.Andreas Kaminski, Michael Nerurkar, Christian Wadephul & Klaus Wiegerling - 2020 - In Andreas Kaminski, Michael Nerurkar, Christian Wadephul & Klaus Wiegerling, Klaus Wiegerling, Michael Nerurkar, Christian Wadephul (Hg.): Ethische Herausforderungen von Big-Data. Bielefeld: Transcript. pp. 151-174.
    Entscheidungen verweisen in einem begrifflichen Sinne auf Gründe. Entscheidungssysteme bieten eine probabilistische Verlässlichkeit als Rechtfertigung von Empfehlungen an. Doch nicht für alle Situationen mögen Verlässlichkeitsgründe auch angemessene Gründe sein. Damit eröffnet sich die Idee, die Güte von Gründen von ihrer Angemessenheit zu unterscheiden. Der Aufsatz betrachtet an einem Beispiel, einem KI-Lügendetektor, die Frage, ob eine (zumindest aktuell nicht gegebene) hohe Verlässlichkeit den Einsatz rechtfertigen kann. Gleicht er nicht einem Richter, der anhand einer Statistik Urteile fällen würde?
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  20. The nonhuman condition: Radical democracy through new materialist lenses.Hans Asenbaum, Amanda Machin, Jean-Paul Gagnon, Diana Leong, Melissa Orlie & James Louis Smith - 2023 - Contemporary Political Theory 22 (Online first):584-615.
    Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute political (...)
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  21. Trustworthiness and truth: The epistemic pitfalls of internet accountability.Karen Frost-Arnold - 2014 - Episteme 11 (1):63-81.
    Since anonymous agents can spread misinformation with impunity, many people advocate for greater accountability for internet speech. This paper provides a veritistic argument that accountability mechanisms can cause significant epistemic problems for internet encyclopedias and social media communities. I show that accountability mechanisms can undermine both the dissemination of true beliefs and the detection of error. Drawing on social psychology and behavioral economics, I suggest alternative mechanisms for increasing the trustworthiness of internet communication.
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  22. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
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  23. Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment.Fares Wael Al-Gharabawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):10-17.
    This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...)
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  24. Organisms ≠ Machines.Daniel J. Nicholson - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):669-678.
    The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit (...)
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  25. The trustworthiness of AI: Comments on Simion and Kelp’s account.Dong-Yong Choi - 2023 - Asian Journal of Philosophy 2 (1):1-9.
    Simion and Kelp explain the trustworthiness of an AI based on that AI’s disposition to meet its obligations. Roughly speaking, according to Simion and Kelp, an AI is trustworthy regarding its task if and only if that AI is obliged to complete the task and its disposition to complete the task is strong enough. Furthermore, an AI is obliged to complete a task in the case where the task is the AI’s etiological function or design function. This account has (...)
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  26. Restoring trustworthiness in the financial system: Norms, behaviour and governance.Aisling Crean, Natalie Gold, David Vines & Annie Williamson - 2018 - Journal of the British Academy 6 (S1):131-155.
    Abstract: We examine how trustworthy behaviour can be achieved in the financial sector. The task is to ensure that firms are motivated to pursue long-term interests of customers rather than pursuing short-term profits. Firms’ self-interested pursuit of reputation, combined with regulation, is often not sufficient to ensure that this happens. We argue that trustworthy behaviour requires that at least some actors show a concern for the wellbeing of clients, or a respect for imposed standards, and that the behaviour of these (...)
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  27. Trust, trustworthiness, and obligation.Mona Simion & Christopher Willard-Kyle - 2024 - Philosophical Psychology 37 (1):87-101.
    Where does entitlement to trust come from? When we trust someone to φ, do we need to have reason to trust them to φ or do we start out entitled to trust them to φ by default? Reductivists think that entitlement to trust always “reduces to” or is explained by the reasons that agents have to trust others. In contrast, anti-reductivists think that, in a broad range of circumstances, we just have entitlement to trust. even if we don’t have positive (...)
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  28. Speaker trustworthiness: Shall confidence match evidence?Mélinda Pozzi & Diana Mazzarella - 2024 - Philosophical Psychology 37 (1):102-125.
    Overconfidence is typically damaging to one’s reputation as a trustworthy source of information. Previous research shows that the reputational cost associated with conveying a piece of false information is higher for confident than unconfident speakers. When judging speaker trustworthiness, individuals do not exclusively rely on past accuracy but consider the extent to which speakers expressed a degree of confidence that matched the accuracy of their claims (their “confidence-accuracy calibration”). The present study experimentally examines the interplay between confidence, accuracy and (...)
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  29. Trust and Trustworthiness.J. Adam Carter - 2022 - Philosophy and Phenomenological Research (2):377-394.
    A widespread assumption in debates about trust and trustworthiness is that the evaluative norms of principal interest on the trustor’s side of a cooperative exchange regulate trusting attitudes and performances whereas those on the trustee’s side regulate dispositions to respond to trust. The aim here will be to highlight some unnoticed problems with this asymmetrical picture – and in particular, how it elides certain key evaluative norms on both the trustor’s and trustee’s side the satisfaction of which are critical (...)
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  30. 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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  31. Trust, Trustworthiness, and the Moral Consequence of Consistency.Jason D’Cruz - 2015 - Journal of the American Philosophical Association 1 (3):467-484.
    Situationists such as John Doris, Gilbert Harman, and Maria Merritt suppose that appeal to reliable behavioral dispositions can be dispensed with without radical revision to morality as we know it. This paper challenges this supposition, arguing that abandoning hope in reliable dispositions rules out genuine trust and forces us to suspend core reactive attitudes of gratitude and resentment, esteem and indignation. By examining situationism through the lens of trust we learn something about situationism (in particular, the radically revisionary moral implications (...)
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  32. Can Machines Read our Minds?Christopher Burr & Nello Cristianini - 2019 - Minds and Machines 29 (3):461-494.
    We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...)
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  33. Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - 2023 - Res Publica 29 (2):237–264.
    Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian (...)
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  34. (1 other version)Trustworthy Science Advice: The Case of Policy Recommendations.Torbjørn Gundersen - 2023 - Res Publica 30 (Onine):1-19.
    This paper examines how science advice can provide policy recommendations in a trustworthy manner. Despite their major political importance, expert recommendations are understudied in the philosophy of science and social epistemology. Matthew Bennett has recently developed a notion of what he calls recommendation trust, according to which well-placed trust in experts’ policy recommendations requires that recommendations are aligned with the interests of the trust-giver. While interest alignment might be central to some cases of public trust, this paper argues against the (...)
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  35. Just Machines.Clinton Castro - 2022 - Public Affairs Quarterly 36 (2):163-183.
    A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to (...)
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  36. Relationally Responsive Expert Trustworthiness.Ben Almassi - 2022 - Social Epistemology 36 (5):576-585.
    Social epistemologists often operationalize the task of indirectly assessing experts’ trustworthiness to identifying whose beliefs are more reliably true on matters in an area of expertise. Not only does this neglect the philosophically rich space between belief formation and testimonial utterances, it also reduces trustworthiness to reliability. In ethics of trust, by contrast, explicitly relational views of trust include things like good will and responsiveness. One might think that relational aspects can be safely set aside for social epistemology (...)
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  37. Relativistic Conceptions of Trustworthiness: Implications for the Trustworthy Status of National Identification Systems.Paul Smart, Wendy Hall & Michael Boniface - 2022 - Data and Policy 4 (e21):1-16.
    Trustworthiness is typically regarded as a desirable feature of national identification systems (NISs); but the variegated nature of the trustor communities associated with such systems makes it difficult to see how a single system could be equally trustworthy to all actual and potential trustors. This worry is accentuated by common theoretical accounts of trustworthiness. According to such accounts, trustworthiness is relativized to particular individuals and particular areas of activity, such that one can be trustworthy with regard to (...)
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  38. Trustworthiness and Motivations.Natalie Gold - 2014 - In N. Morris D. Vines, Capital Failure: Rebuilding trust in financial services. Oxford University Press.
    Trust can be thought of as a three place relation: A trusts B to do X. Trustworthiness has two components: competence (does the trustee have the relevant skills, knowledge and abilities to do X?) and willingness (is the trustee intending or aiming to do X?). This chapter is about the willingness component, and the different motivations that a trustee may have for fulfilling trust. The standard assumption in economics is that agents are self-regarding, maximizing their own consumption of goods (...)
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  39. Consciousness, Machines, and Moral Status.Henry Shevlin - manuscript
    In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper (...)
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  40. Xin: Being Trustworthy.Winnie Sung - 2020 - International Philosophical Quarterly 60 (3):271-286.
    This essay analyses the Confucian conception of xin, an attribute that broadly resembles what we would ordinarily call trustworthiness. More specifically, it provides an analysis of the psychology of someone who is xin and highlights a feature of the Confucian conception of trustworthiness: the trustworthy person has to ensure that there is a match between her self-presentation and the way she is. My goal is not to argue against any of the existing accounts of trustworthiness but to (...)
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  41. The Machine Conception of the Organism in Development and Evolution: A Critical Analysis.Daniel J. Nicholson - 2014 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 48:162-174.
    This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. (...)
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  42. Machine Intentionality, the Moral Status of Machines, and the Composition Problem.David Leech Anderson - 2012 - In Vincent C. Müller, The Philosophy & Theory of Artificial Intelligence. Springer. pp. 312-333.
    According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...)
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  43. A Machine That Knows Its Own Code.Samuel A. Alexander - 2014 - Studia Logica 102 (3):567-576.
    We construct a machine that knows its own code, at the price of not knowing its own factivity.
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  44.  64
    Glass Hospitals: Transparency and Trustworthy Interpretation in Medical and Healthcare Expertise.Ben Almassi - 2025 - Diametros 22 (82):53-63.
    In their recent article in this journal, Giubilini, Gur-Arie, and Jamrozik argue that there is more to expertise than individual healthcare professionals’ knowledge of their fields. To be an expert is to be recognized as a credible authority, they explain, and being a credible authority necessitates trust. Among the core ethical principles they identify for trustworthy experts in medicine and healthcare are honesty, humility, and transparency. Here I aim to affirm these authors’ linkage of expertise and trust by decoupling both (...)
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  45. Why machines cannot be moral.Robert Sparrow - 2021 - AI and Society (3):685-693.
    The fact that real-world decisions made by artificial intelligences (AI) are often ethically loaded has led a number of authorities to advocate the development of “moral machines”. I argue that the project of building “ethics” “into” machines presupposes a flawed understanding of the nature of ethics. Drawing on the work of the Australian philosopher, Raimond Gaita, I argue that ethical dilemmas are problems for particular people and not (just) problems for everyone who faces a similar situation. Moreover, the force of (...)
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  46. Machines as Moral Patients We Shouldn’t Care About : The Interests and Welfare of Current Machines.John Basl - 2014 - Philosophy and Technology 27 (1):79-96.
    In order to determine whether current (or future) machines have a welfare that we as agents ought to take into account in our moral deliberations, we must determine which capacities give rise to interests and whether current machines have those capacities. After developing an account of moral patiency, I argue that current machines should be treated as mere machines. That is, current machines should be treated as if they lack those capacities that would give rise to psychological interests. Therefore, they (...)
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  47. Cybersecurity, Trustworthiness and Resilient Systems: Guiding Values for Policy.Adam Henschke & Shannon Ford - 2017 - Journal of Cyber Policy 1 (2).
    Cyberspace relies on information technologies to mediate relations between different people, across different communication networks and is reliant on the supporting technology. These interactions typically occur without physical proximity and those working depending on cybersystems must be able to trust the overall human–technical systems that support cyberspace. As such, detailed discussion of cybersecurity policy would be improved by including trust as a key value to help guide policy discussions. Moreover, effective cybersystems must have resilience designed into them. This paper argues (...)
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  48. Cognitive Projects and the Trustworthiness of Positive Truth.Matteo Zicchetti - 2022 - Erkenntnis (8).
    The aim of this paper is twofold: first, I provide a cluster of theories of truth in classical logic that is (internally) consistent with global reflection principles: the theories of positive truth (and falsity). After that, I analyse the _epistemic value_ of such theories. I do so employing the framework of cognitive projects introduced by Wright (Proc Aristot Soc 78:167–245, 2004), and employed—in the context of theories of truth—by Fischer et al. (Noûs 2019. https://doi.org/10.1111/nous.12292 ). In particular, I will argue (...)
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  49. “Man-Machines and Embodiment: From Cartesian Physiology to Claude Bernard’s ‘Living Machine’”.Charles T. Wolfe & Philippe Huneman - 2017 - In Justin E. H. Smith, Embodiment: A History. New York: Oxford University Press.
    A common and enduring early modern intuition is that materialists reduce organisms in general and human beings in particular to automata. Wasn’t a famous book of the time entitled L’Homme-Machine? In fact, the machine is employed as an analogy, and there was a specifically materialist form of embodiment, in which the body is not reduced to an inanimate machine, but is conceived as an affective, flesh-and-blood entity. We discuss how mechanist and vitalist models of organism exist in (...)
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  50. Machine Advisors: Integrating Large Language Models into Democratic Assemblies.Petr Špecián - forthcoming - Social Epistemology.
    Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...)
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