Results for 'machine killing'

960 found
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  1. Gardner, L. C. Killing Machine: The American Presidency in the Age of Drone Warfare. [REVIEW]Edmund Byrne - 2014 - Michigan War Studies Review 2014 (045).
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  2. Euthanasia, or Mercy Killing.Nathan Nobis - 2019 - 1000-Word Philosophy: An Introductory Anthology.
    Sadly, there are people in very bad medical conditions who want to die. They are in pain, they are suffering, and they no longer find their quality of life to be at an acceptable level anymore. -/- When people like this are kept alive by machines or other medical treatments, can it be morally permissible to let them die? -/- Advocates of “passive euthanasia” argue that it can be. Their reasons, however, suggest that it can sometimes be not wrong to (...)
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  3. Frowe's Machine Cases.Simkulet William - 2015 - Filosofiska Notiser 2 (2): 93-104.
    Helen Frowe (2006/2010) contends that there is a substantial moral difference between killing and letting die, arguing that in Michael Tooley's infamous machine case it is morally wrong to flip a coin to determine who lives or dies. Here I argue that Frowe fails to show that killing and letting die are morally inequivalent. However, I believe that she has succeeded in showing that it is wrong to press the button in Tooley's case, where pressing the button (...)
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  4. Fire and Forget: A Moral Defense of the Use of Autonomous Weapons in War and Peace.Duncan MacIntosh - 2021 - In Jai Galliott, Duncan MacIntosh & Jens David Ohlin (eds.), Lethal Autonomous Weapons: Re-Examining the Law and Ethics of Robotic Warfare. New York: Oxford University Press. pp. 9-23.
    Autonomous and automatic weapons would be fire and forget: you activate them, and they decide who, when and how to kill; or they kill at a later time a target you’ve selected earlier. Some argue that this sort of killing is always wrong. If killing is to be done, it should be done only under direct human control. (E.g., Mary Ellen O’Connell, Peter Asaro, Christof Heyns.) I argue that there are surprisingly many kinds of situation where this is (...)
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  5. Is 'the monstrous thesis' truly Cartesian?Rodrigo González - 2017 - Discusiones Filosóficas 18 (30):15-33.
    According to Kemp Smith, Descartes believed that animals were devoid of feelings and sensations. This is the so-called ‘monstrous thesis,’ which I explore here in light of two Cartesian approaches to animals. Firstly, I examine their original treatment in function of Descartes’ early metaphysical approach, i.e., all natural phenomena are to be elucidated in terms of mental scrutiny. As pain would only exist in the understanding, and animals have neither understanding nor souls, Descartes held that they did not suffer. Secondly, (...)
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  6. Kantian Ethics in the Age of Artificial Intelligence and Robotics.Ozlem Ulgen - 2017 - Questions of International Law 1 (43):59-83.
    Artificial intelligence and robotics is pervasive in daily life and set to expand to new levels potentially replacing human decision-making and action. Self-driving cars, home and healthcare robots, and autonomous weapons are some examples. A distinction appears to be emerging between potentially benevolent civilian uses of the technology (eg unmanned aerial vehicles delivering medicines), and potentially malevolent military uses (eg lethal autonomous weapons killing human com- batants). Machine-mediated human interaction challenges the philosophical basis of human existence and ethical (...)
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  7. Introduction to Ethics: An Open Educational Resource, collected and edited by Noah Levin.Noah Levin, Nathan Nobis, David Svolba, Brandon Wooldridge, Kristina Grob, Eduardo Salazar, Benjamin Davies, Jonathan Spelman, Elizabeth Cady Stanton, Kristin Seemuth Whaley, Jan F. Jacko & Prabhpal Singh (eds.) - 2019 - Huntington Beach, California: N.G.E Far Press.
    Collected and edited by Noah Levin -/- Table of Contents: -/- UNIT ONE: INTRODUCTION TO CONTEMPORARY ETHICS: TECHNOLOGY, AFFIRMATIVE ACTION, AND IMMIGRATION 1 The “Trolley Problem” and Self-Driving Cars: Your Car’s Moral Settings (Noah Levin) 2 What is Ethics and What Makes Something a Problem for Morality? (David Svolba) 3 Letter from the Birmingham City Jail (Martin Luther King, Jr) 4 A Defense of Affirmative Action (Noah Levin) 5 The Moral Issues of Immigration (B.M. Wooldridge) 6 The Ethics of our (...)
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  8. The Life of Ṣaḥābī ʿUrwa b. Masʿūd el-Thaqafī.Mithat Eser - 2020 - Cumhuriyet İlahiyat Dergisi 24 (2):591-609.
    One of the ṣaḥābīs of Prophet Muḥammad is ʿUrwa b. Masʿūd from the Ṭāʾif tribe of Thaqīf. He belongs to the Ahlâf part of the Thaqīf tribe and he is the ruler of this part. ʿUrwa’s ancestry is known without any controversy until Kasî (Thaqīf). According to a narrative his epithet was Abū Yaʿfur and another of his epithet was Abū Masʿūd. Father of ʿUrwa an important person too. He is one of the leaders of his tribe and he commanded (...)
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  9. A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers.Lorna Green - manuscript
    June 2022 A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers We are in a unique moment of our history unlike any previous moment ever. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. As I write, the planet is in deep trouble, heat, fires, great storms, and record flooding, (...)
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  10. The Soldier’s Share: Considering Narrow Responsibility for Lethal Autonomous Weapons.Kevin Schieman - 2023 - Journal of Military Ethics (3):228-245.
    Robert Sparrow (among others) claims that if an autonomous weapon were to commit a war crime, it would cause harm for which no one could reasonably be blamed. Since no one would bear responsibility for the soldier’s share of killing in such cases, he argues that they would necessarily violate the requirements of jus in bello, and should be prohibited by international law. I argue this view is mistaken and that our moral understanding of war is sufficient to determine (...)
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  11. 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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  12. On the Logical Impossibility of Solving the Control Problem.Caleb Rudnick - manuscript
    In the philosophy of artificial intelligence (AI) we are often warned of machines built with the best possible intentions, killing everyone on the planet and in some cases, everything in our light cone. At the same time, however, we are also told of the utopian worlds that could be created with just a single superintelligent mind. If we’re ever to live in that utopia (or just avoid dystopia) it’s necessary we solve the control problem. The control problem asks how (...)
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  13. (1 other version)Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017.Michael Starks - 2017 - Las Vegas, NV USA: Reality Press.
    This collection of articles was written over the last 10 years and edited to bring them up to date (2017). The copyright page has the date of the edition and new editions will be noted there as I edit old articles or add new ones. All the articles are about human behavior (as are all articles by anyone about anything), and so about the limitations of having a recent monkey ancestry (8 million years or much less depending on viewpoint) and (...)
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  14. 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 (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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  15. 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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  16. Gestaticide: Killing the Subject of the Artificial Womb.Daniel Rodger, Nicholas Colgrove & Bruce Philip Blackshaw - 2020 - Journal of Medical Ethics 47 (12):e53.
    The rapid development of artificial womb technologies means that we must consider if and when it is permissible to kill the human subject of ectogestation—recently termed a ‘gestateling’ by Elizabeth Chloe Romanis—prior to ‘birth’. We describe the act of deliberately killing the gestateling as gestaticide, and argue that there are good reasons to maintain that gestaticide is morally equivalent to infanticide, which we consider to be morally impermissible. First, we argue that gestaticide is harder to justify than abortion, primarily (...)
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  17. 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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  18. Killing Minimally Responsible Threats.Saba Bazargan - 2014 - Ethics 125 (1):114-136.
    Minimal responsibility threateners are epistemically justified but mistaken in thinking that imposing a nonnegligible risk on others is permissible. On standard accounts, an MRT forfeits her right not to be defensively killed. I propose an alternative account: an MRT is liable only to the degree of harm equivalent to what she risks causing multiplied by her degree of responsibility. Harm imposed on the MRT above that amount is justified as a lesser evil, relative to allowing the MRT to kill her (...)
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  19. Risky Killing: How Risks Worsen Violations of Objective Rights.Seth Lazar - 2019 - Journal of Moral Philosophy 16 (1):1-26.
    I argue that riskier killings of innocent people are, other things equal, objectively worse than less risky killings. I ground these views in considerations of disrespect and security. Killing someone more riskily shows greater disrespect for him by more grievously undervaluing his standing and interests, and more seriously undermines his security by exposing a disposition to harm him across all counterfactual scenarios in which the probability of killing an innocent person is that high or less. I argue that (...)
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  20. Machine vs. Human Translation.Elona Limaj - 2014 - Journal of Turkish Studies 9 (Volume 9 Issue 6):783-783.
    The advantages and disadvantages of machine translation have been the subject of increasing debate among human translators lately because of the growing strides made in the last year by the newest major entrant in the field, Google Translate. The progress and potential of machine translation has been debated much through its history. But this debate actually began with the birth of machine translation itself. Behind this simple procedure lies a complex cognitive operation. To decode the meaning of (...)
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  21. 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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  22. The responsibility dilemma for killing in war: A review essay.Seth Lazar - 2010 - Philosophy and Public Affairs 38 (2):180-213.
    Killing in War presents the Moral Equality of Combatants with serious, and in my view insurmountable problems. Absent some novel defense, this thesis is now very difficult to sustain. But this success is counterbalanced by the strikingly revisionist implications of McMahan’s account of the underlying morality of killing in war, which forces us into one of two unattractive positions, contingent pacifism, or near-total war. In this article, I have argued that his efforts to mitigate these controversial implications fail. (...)
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  23. 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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  24. 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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  25. Painlessly Killing Predators.Ben Bramble - 2020 - Journal of Applied Philosophy 38 (2):217-225.
    Animals suffer harms not only in human captivity but in the wild as well. Some of these latter harms are due to humans, but many of them are not. Consider, for example, the harms of predation, i.e. of being hunted, killed, and eaten by other animals. Should we intervene in nature to prevent these harms? In this article, I consider two possible ways in which we might do so: (1) by herbivorising predators (i.e. genetically modify them so that their offspring (...)
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  26. 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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  27. 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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  28. 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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  29. Targeted Killings: Legal and Ethical Justifications.Tomasz Zuradzki - 2015 - In Marcelo Galuppo (ed.), Human Rights, Rule of Law and the Contemporary Social Challenges in Complex Societies. pp. 2909-2923.
    The purpose of this paper is the analysis of both legal and ethical ways of justifying targeted killings. I compare two legal models: the law enforcement model vs the rules of armed conflicts; and two ethical ones: retribution vs the right of self-defence. I argue that, if the targeted killing is to be either legally or ethically justified, it would be so due to fulfilling of some criteria common for all acceptable forms of killing, and not because terrorist (...)
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  30. The Case for an International Hard Law on Corporate Killing.Marc Johnson - 2024 - Keele Law Review 5 (1):1-28.
    On 4 December 2006, during discussions on the Corporate Manslaughter and Corporate Homicide Bill, Andrew Dismore, Member of Parliament and then Chair of the Joint Committee on Human Rights, said, ‘Organisations can kill people … but it is the actions and omissions of people in organisations that cumulatively cause death’. However, the corporate entity is a vehicle for the communal actions of those who guide the business activities. Attempting to seek out persons or people that are solely responsible for deaths (...)
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  31. 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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  32. Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  33. 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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  34. (1 other version)Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen C. King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...)
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  35. Machine Intentionality, the Moral Status of Machines, and the Composition Problem.David Leech Anderson - 2012 - In Vincent C. Müller (ed.), 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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  36. Why Machines Can Neither Think nor Feel.Douglas C. Long - 1994 - In Dale W. Jamieson (ed.), Language, Mind and Art. Boston: Kluwer Academic Publishers.
    Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings. In the first half of my paper I review Ziff's arguments against the idea that robots could be conscious, especially his appeal to (...)
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  37. Credit Score Classification Using Machine Learning.Mosa M. M. Megdad & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (5):1-10.
    Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the (...)
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  38. What is wrong with killing people?R. E. Ewin - 1972 - Philosophical Quarterly 22 (87):126-139.
    Qualifications are needed to make the point a tight one, but it seems quite plain that it is wrong to kill people. What is not so plain is why it is wrong to kill people, especially when one considers that the person killed will not be around to suffer the consequences afterwards. He does not suffer as a consequence of his death, and he need not suffer even while dying. There are various conditions more or less commonly accepted as making (...)
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  39. Police-Generated Killings: The Gap between Ethics and Law.Ben Jones - 2022 - Political Research Quarterly 75 (2):366-378.
    This article offers a normative analysis of some of the most controversial incidents involving police—what I call police-generated killings. In these cases, bad police tactics create a situation where deadly force becomes necessary, becomes perceived as necessary, or occurs unintentionally. Police deserve blame for such killings because they choose tactics that unnecessarily raise the risk of deadly force, thus violating their obligation to prioritize the protection of life. Since current law in the United States fails to ban many bad tactics, (...)
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  40. Two Ways to Kill a Patient.Ben Bronner - 2018 - Journal of Medicine and Philosophy 43 (1):44-63.
    According to the Standard View, a doctor who withdraws life-sustaining treatment does not kill the patient but rather allows the patient to die—an important distinction, according to some. I argue that killing can be understood in either of two ways, and given the relevant understanding, the Standard View is insulated from typical criticisms. I conclude by noting several problems for the Standard View that remain to be fully addressed.
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  41. Desired Machines: Cinema and the World in Its Own Image.Jimena Canales - 2011 - Science in Context 24 (3):329-359.
    ArgumentIn 1895 when the Lumière brothers unveiled their cinematographic camera, many scientists were elated. Scientists hoped that the machine would fulfill a desire that had driven research for nearly half a century: that of capturing the world in its own image. But their elation was surprisingly short-lived, and many researchers quickly distanced themselves from the new medium. The cinematographic camera was soon split into two machines, one for recording and one for projecting, enabling it to further escape from the (...)
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  42. 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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  43. Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...)
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  44. Killing fetuses and killing newborns.Ezio Di Nucci - 2013 - Journal of Medical Ethics 39 (5):19-20.
    The argument for the moral permissibility of killing newborns is a challenge to liberal positions on abortion because it can be considered a reductio of their defence of abortion. Here I defend the liberal stance on abortion by arguing that the argument for the moral permissibility of killing newborns on ground of the social, psychological and economic burden on the parents recently put forward by Giubilini and Minerva is not valid; this is because they fail to show that (...)
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  45. Why Machine-Information Metaphors are Bad for Science and Science Education.Massimo Pigliucci & Maarten Boudry - 2011 - Science & Education 20 (5-6):471.
    Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...)
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  46. 8 Rightful Machines.Ava Thomas Wright - 2022 - In Hyeongjoo Kim & Dieter Schönecker (eds.), Kant and Artificial Intelligence. De Gruyter. pp. 223-238.
    In this paper, I set out a new Kantian approach to resolving conflicts between moral obligations for highly autonomous machine agents. First, I argue that efforts to build explicitly moral autonomous machine agents should focus on what Kant refers to as duties of right, which are duties that everyone could accept, rather than on duties of virtue (or “ethics”), which are subject to dispute in particular cases. “Moral” machines must first be rightful machines, I argue. I then show (...)
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  47. The Wrongness of Killing.Rainer Ebert - 2016 - Dissertation, Rice University
    There are few moral convictions that enjoy the same intuitive plausibility and level of acceptance both within and across nations, cultures, and traditions as the conviction that, normally, it is morally wrong to kill people. Attempts to provide a philosophical explanation of why that is so broadly fall into three groups: Consequentialists argue that killing is morally wrong, when it is wrong, because of the harm it inflicts on society in general, or the victim in particular, whereas personhood and (...)
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  48. Machine intelligence: a chimera.Mihai Nadin - 2019 - AI and Society 34 (2):215-242.
    The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...)
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  49. 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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  50. rethinking machine ethics in the era of ubiquitous technology.Jeffrey White (ed.) - 2015 - Hershey, PA, USA: IGI.
    Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...)
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