Results for 'clustering algorithms'

986 found
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  1. Responding to the Watson-Sterkenburg debate on clustering algorithms and natural kinds.Warmhold Jan Thomas Mollema - manuscript
    In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” (...)
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  2. Crime Prediction and Forecasting Using MLP & K-Means Clustering Algorithm.Mehaa P. Yamunathangam D., Bharath P., Harshini M. - 2024 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 7 (5):9859-9863.
    The most significant and pervasive issue in our society is crime. Rising crime rates contribute to an unbalanced societal makeup within a nation. Over the past few years, machine learning techniques have been deployed to scrutinize crime data, offering valuable insights for forecasting and thwarting forthcoming criminal activities. In this paper, crime prediction and forecasting using MLP (Multi-Layer Perceptron) & K-Means clustering algorithms, presents a novel approach that combines machine learning and deep learning techniques to achieve precise crime (...)
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  3.  44
    Applying Clustering technique on Climatic Data.K. K. Sharma G. Vimal Raja - 2015 - Envirogeochimica Acta 2 (1):21-27.
    Climate data analysis performed in order to understand climate change process and effect of different environmental factors in that change, has been focus of interest of researches for many years. Climate change disturbs natural balance, leading to endangerment of many species and their habitats. Data mining techniques provide better and faster analysis of large amounts of data in climatology. Data Mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It (...)
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  4. Advanced AI Algorithms for Automating Data Preprocessing in Healthcare: Optimizing Data Quality and Reducing Processing Time.Muthukrishnan Muthusubramanian Praveen Sivathapandi, Prabhu Krishnaswamy - 2022 - Journal of Science and Technology (Jst) 3 (4):126-167.
    This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate extensive preprocessing before being (...)
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  5. Algebraic structures of neutrosophic triplets, neutrosophic duplets, or neutrosophic multisets. Volume II.Florentin Smarandache, Xiaohong Zhang & Mumtaz Ali - 2019 - Basel, Switzerland: MDPI.
    The topics approached in this collection of papers are: neutrosophic sets; neutrosophic logic; generalized neutrosophic set; neutrosophic rough set; multigranulation neutrosophic rough set (MNRS); neutrosophic cubic sets; triangular fuzzy neutrosophic sets (TFNSs); probabilistic single-valued (interval) neutrosophic hesitant fuzzy set; neutro-homomorphism; neutrosophic computation; quantum computation; neutrosophic association rule; data mining; big data; oracle Turing machines; recursive enumerability; oracle computation; interval number; dependent degree; possibility degree; power aggregation operators; multi-criteria group decision-making (MCGDM); expert set; soft sets; LA-semihypergroups; single valued trapezoidal neutrosophic number; (...)
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  6. A Causal-Mentalist View of Propositions.Jeremiah Joven Joaquin & James Franklin - 2022 - Organon F: Medzinárodný Časopis Pre Analytickú Filozofiu 29 (1):47-77.
    In order to fulfil their essential roles as the bearers of truth and the relata of logical relations, propositions must be public and shareable. That requirement has favoured Platonist and other nonmental views of them, despite the well-known problems of Platonism in general. Views that propositions are mental entities have correspondingly fallen out of favour, as they have difficulty in explaining how propositions could have shareable, objective properties. We revive a mentalist view of propositions, inspired by Artificial Intelligence work on (...)
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  7. The mind, the lab, and the field: Three kinds of populations in scientific practice.Rasmus Grønfeldt Winther, Ryan Giordano, Michael D. Edge & Rasmus Nielsen - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 52:12-21.
    Scientists use models to understand the natural world, and it is important not to conflate model and nature. As an illustration, we distinguish three different kinds of populations in studies of ecology and evolution: theoretical, laboratory, and natural populations, exemplified by the work of R.A. Fisher, Thomas Park, and David Lack, respectively. Biologists are rightly concerned with all three types of populations. We examine the interplay between these different kinds of populations, and their pertinent models, in three examples: the notion (...)
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  8. Linguistic Corpora and Ordinary Language: On the Dispute Between Ryle and Austin About the Use of ‘Voluntary’, ‘Involuntary’, ‘Voluntarily’, and ‘Involuntarily’.Michael Zahorec, Robert Bishop, Nat Hansen, John Schwenkler & Justin Sytsma - 2023 - In David Bordonaba-Plou, Experimental Philosophy of Language: Perspectives, Methods, and Prospects. Springer Verlag. pp. 121-149.
    The fact that Gilbert Ryle and J.L. Austin seem to disagree about the ordinary use of words such as ‘voluntary’, ‘involuntary’, ‘voluntarily’, and ‘involuntarily’ has been taken to cast doubt on the methods of ordinary language philosophy. As Benson Mates puts the worry, ‘if agreement about usage cannot be reached within so restricted a sample as the class of Oxford Professors of Philosophy, what are the prospects when the sample is enlarged?’ (Mates, Inquiry 1:161–171, 1958, p. 165). In this chapter, (...)
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  9. A Neutrosophic Approach to Study Agnotology: A Case Study on Climate Change Beliefs.Maikel Leyva & Florentin Smarandache - 2024 - Hypersoft Set Methods in Engineering 2 (1).
    Misinformation and biased information significantly impact public perception and political decisions, especially on critical issues such as climate change and environmental conservation. This study aims to understand how indeterminacy and contradiction influence public perception and policy formulation by applying neutrosophic theory to model the complexity and multi-dimensionality of ignorance. Using neutrosophic Likert scales, we capture a nuanced spectrum of opinions on the scientific certainty of human impact on climate change. The results are analyzed through a k-means clustering algorithm to (...)
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  10. Algebraic structures of neutrosophic triplets, neutrosophic duplets, or neutrosophic multisets. Volume I.Florentin Smarandache, Xiaohong Zhang & Mumtaz Ali - 2018 - Basel, Switzerland: MDPI. Edited by Florentin Smarandache, Xiaohong Zhang & Mumtaz Ali.
    The topics approached in the 52 papers included in this book are: neutrosophic sets; neutrosophic logic; generalized neutrosophic set; neutrosophic rough set; multigranulation neutrosophic rough set (MNRS); neutrosophic cubic sets; triangular fuzzy neutrosophic sets (TFNSs); probabilistic single-valued (interval) neutrosophic hesitant fuzzy set; neutro-homomorphism; neutrosophic computation; quantum computation; neutrosophic association rule; data mining; big data; oracle Turing machines; recursive enumerability; oracle computation; interval number; dependent degree; possibility degree; power aggregation operators; multi-criteria group decision-making (MCGDM); expert set; soft sets; LA-semihypergroups; single valued (...)
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  11. Limiting Access to Certain Anonymous Information: From the Group Right to Privacy to the Principle of Protecting the Vulnerable.Haleh Asgarinia - 2024 - Journal of Value Inquiry 58 (1):1-27.
    An issue about the privacy of the clustered groups designed by algorithms arises when attempts are made to access certain pieces of information about those groups that would likely be used to harm them. Therefore, limitations must be imposed regarding accessing such information about clustered groups. In the discourse on group privacy, it is argued that the right to privacy of such groups should be recognised to respect group privacy, protecting clustered groups against discrimination. According to this viewpoint, this (...)
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  12. Affiliative Subgroups in Preschool Classrooms: Integrating Constructs and Methods from Social Ethology and Sociometric Traditions.António J. Santos, João R. Daniel, Carla Fernandes & Brian E. Vaughn - 2015 - PLoS ONE 7 (10):1-17.
    Recent studies of school-age children and adolescents have used social network analyses to characterize selection and socialization aspects of peer groups. Fewer network studies have been reported for preschool classrooms and many of those have focused on structural descriptions of peer networks, and/or, on selection processes rather than on social functions of subgroup membership. In this study we started by identifying and describing different types of affiliative subgroups (HMP- high mutual proximity, LMP- low mutual proximity, and ungrouped children) in a (...)
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  13. How a neural net grows symbols.James Franklin - 1996 - In Peter Bartlett, Proceedings of the Seventh Australian Conference on Neural Networks, Canberra. ACNN '96. pp. 91-96.
    Brains, unlike artificial neural nets, use symbols to summarise and reason about perceptual input. But unlike symbolic AI, they “ground” the symbols in the data: the symbols have meaning in terms of data, not just meaning imposed by the outside user. If neural nets could be made to grow their own symbols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine the good features (...)
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  14.  68
    Prediction of Probability of Disease_ Based on Symptoms (7th edition).A. A. Mamanabad - 2024 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 7 (3):4695-4699. Translated by S.V Jadhav.
    Today's health information management systems collect enormous volumes of healthcare data and information, including complex personal and medical history information. In order to find utilization patterns for research, medical data mining techniques are being used more and more. Nowadays, the greatest cause of death for humans is sickness; a single person may be afflicted with several ailments. This method attempts to predict sickness by using symptoms that are related to a patient's condition and behavior. Based on the user-provided health information, (...)
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  15.  7
    Discovering Learning Patterns through Data Mining in E-Learning Platforms Data Mining for Anomaly Detection in Network Traffic.Savitha R. & Nagaratnamma R. - 2024 - International Journal of Scientific Research in Science, Engineering and Technology 11 (2).
    This paper explores the application of data mining techniques to discover learning patterns in e-learning platforms. With the increasing adoption of e-learning systems, vast amounts of user interaction data are generated, providing valuable insights into student behavior, engagement, and learning outcomes. This research aims to apply various data mining algorithms, including clustering, classification, and association rule mining, to analyze the data collected from e-learning platforms. The primary objective is to uncover hidden patterns related to student activity, learning progression, (...)
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  16. Beyond categorical definitions of life: a data-driven approach to assessing lifeness.Christophe Malaterre & Jean-François Chartier - 2019 - Synthese 198 (5):4543-4572.
    The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...)
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  17. Half a century of bioethics and philosophy of medicine: A topic‐modeling study.Piotr Bystranowski, Vilius Dranseika & Tomasz Żuradzki - 2022 - Bioethics 36 (9):902-925.
    Topic modeling—a text‐mining technique often used to uncover thematic structures in large collections of texts—has been increasingly frequently used in the context of the analysis of scholarly output. In this study, we construct a corpus of 19,488 texts published since 1971 in seven leading journals in the field of bioethics and philosophy of medicine, and we use a machine learning algorithm to identify almost 100 topics representing distinct themes of interest in the field. On the basis of intertopic correlations, we (...)
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  18.  67
    Rough set theory-based feature selection and FGA-NN classifier for medical data classification (14th edition).Rajendran Sugumar - 2019 - Int. J. Business Intelligence and Data Mining 14 (3):322-358.
    The prediction of heart disease is a difficult task, which needs much experience and knowledge. In order to reduce the risk of heart disease prediction, in this paper we proposed a rough set theory-based feature selection and FGA-NN classifier. The overall process of the proposed system consists of two main steps, such as: 1) feature reduction; 2) heart disease prediction. At first, the kernel fuzzy c-means clustering with roughest theory (KFCMRS) algorithm is applied to the high dimensional data to (...)
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  19.  33
    Automated Plant Disease Detection and Classification in Leaf Image.Vamshi KrishnaK JanishaJ, Ramesh Gandreddi, Pavan KumarA, Parthu SekharA - 2025 - International Journal of Innovative Research in Science Engineering and Technology 14 (4):8839-8845.
    The identification of disease on the plant is a very important key to prevent a heavy loss of yield and the quantity of agricultural product. The symptoms can be observed on the parts of the plants such as leaf, stems, lesions and fruits. The leaf shows the symptoms by changing color, showing the spots on it. This identification of the disease is done by manual observation and pathogen detection which can consume more time and may prove costly. The aim of (...)
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  20. Occam's Razor For Big Data?Birgitta Dresp-Langley - 2019 - Applied Sciences 3065 (9):1-28.
    Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...)
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  21. On Philomatics and Psychomatics for Combining Philosophy and Psychology with Mathematics.Benyamin Ghojogh & Morteza Babaie - manuscript
    We propose the concepts of philomatics and psychomatics as hybrid combinations of philosophy and psychology with mathematics. We explain four motivations for this combination which are fulfilling the desire of analytical philosophy, proposing science of philosophy, justifying mathematical algorithms by philosophy, and abstraction in both philosophy and mathematics. We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth. The first example is the analysis of relation between the context principle, semantic holism, and the (...)
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  22.  54
    Scalable Kubernetes Workload Orchestration for Multi- Cloud Environments.Tambi Varun Kumar - 2025 - The Research Journal (Trj): A Unit of I2Or 11 (1):1-6.
    As organizations increasingly adopt cloud-native architectures, the demand for flexible, efficient, and scalable orchestration solutions across multi-cloud environments has grown significantly. Kubernetes, as a leading container orchestration platform, has become the de facto standard for managing workloads across heterogeneous cloud infrastructures. However, orchestrating workloads across multiple cloud providers introduces complex challenges related to resource optimization, workload portability, latency management, inter-cluster communication, and security. This paper presents a comprehensive framework for Scalable Kubernetes Workload Orchestration in Multi-Cloud Environments, aiming to optimize resource (...)
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  23. Scientific essentialism in the light of classification practice in biology – a case study of phytosociology.Adam P. Kubiak & Rafał R. Wodzisz - 2012 - Zagadnienia Naukoznawstwa 48 (194):231-250.
    In our paper we investigate a difficulty arising when one tries to reconsiliateessentialis t’s thinking with classification practice in the biological sciences. The article outlinessome varieties of essentialism with particular attention to the version defended by Brian Ellis. Weunderline the basic difference: Ellis thinks that essentialism is not a viable position in biology dueto its incompatibility with biological typology and other essentialists think that these two elementscan be reconciled. However, both parties have in common metaphysical starting point and theylack explicit (...)
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  24. An Introduction to Artificial Psychology Application Fuzzy Set Theory and Deep Machine Learning in Psychological Research using R.Farahani Hojjatollah - 2023 - Springer Cham. Edited by Hojjatollah Farahani, Marija Blagojević, Parviz Azadfallah, Peter Watson, Forough Esrafilian & Sara Saljoughi.
    Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers (...)
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  25. Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
    Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as (...)
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  26. The Cluster Account of Art: A Historical Dilemma.Simon Fokt - 2014 - Contemporary Aesthetics 12:N/A.
    The cluster account, one of the best attempts at art classification, is guilty of ahistoricism. While cluster theorists may be happy to limit themselves to accounting for what art is now rather than how the term was understood in the past, they cannot ignore the fact that people seem to apply different clusters when judging art from different times. This paper shows that while allowing for this kind of historical relativity may be necessary to save the account, doing so could (...)
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  27. The ethics of algorithms: mapping the debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2):2053951716679679.
    In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can (...)
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  28. Algorithmic paranoia: the temporal governmentality of predictive policing.Bonnie Sheehey - 2019 - Ethics and Information Technology 21 (1):49-58.
    In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After (...)
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  29. Are Clusters Races? A Discussion of the Rhetorical Appropriation of Rosenberg et al.’s “Genetic Structure of Human Populations”.Melissa Wills - 2017 - Philosophy, Theory, and Practice in Biology 9 (12).
    Noah Rosenberg et al.'s 2002 article “Genetic Structure of Human Populations” reported that multivariate genomic analysis of a large cell line panel yielded reproducible groupings (clusters) suggestive of individuals' geographical origins. The paper has been repeatedly cited as evidence that traditional notions of race have a biological basis, a claim its authors do not make. Critics of this misinterpretation have often suggested that it follows from interpreters' personal biases skewing the reception of an objective piece of scientific writing. I contend, (...)
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  30. Algorithmic Profiling as a Source of Hermeneutical Injustice.Silvia Milano & Carina Prunkl - forthcoming - Philosophical Studies:1-19.
    It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate (...)
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  31. Algorithmic neutrality.Milo Phillips-Brown - manuscript
    Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?
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  32. Algorithmic Fairness Criteria as Evidence.Will Fleisher - forthcoming - Ergo: An Open Access Journal of Philosophy.
    Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion as a (...)
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  33. Crash Algorithms for Autonomous Cars: How the Trolley Problem Can Move Us Beyond Harm Minimisation.Dietmar Hübner & Lucie White - 2018 - Ethical Theory and Moral Practice 21 (3):685-698.
    The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...)
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  34. Algorithmic Fairness from a Non-ideal Perspective.Sina Fazelpour & Zachary C. Lipton - 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...)
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  35. Disambiguating Algorithmic Bias: From Neutrality to Justice.Elizabeth Edenberg & Alexandra Wood - 2023 - In Francesca Rossi, Sanmay Das, Jenny Davis, Kay Firth-Butterfield & Alex John, AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery. pp. 691-704.
    As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered (...)
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  36. Algorithm Evaluation Without Autonomy.Scott Hill - forthcoming - AI and Ethics.
    In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their (...)
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  37. Algorithmic decision-making: the right to explanation and the significance of stakes.Lauritz Munch, Jens Christian Bjerring & Jakob Mainz - 2024 - Big Data and Society.
    The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call (...)
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  38. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (7):4-20.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...)
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  39. Algorithms, Agency, and Respect for Persons.Alan Rubel, Clinton Castro & Adam Pham - 2020 - Social Theory and Practice 46 (3):547-572.
    Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways (...)
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  40. Algorithms and Autonomy: The Ethics of Automated Decision Systems.Alan Rubel, Clinton Castro & Adam Pham - 2021 - Cambridge University Press.
    Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. (...)
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  41. Clines, Clusters, and Clades in the Race Debate.Matthew Kopec - 2014 - Philosophy of Science 81 (5):1053-1065.
    Although there once was a general consensus among race scholars that applying race categories to humans is biologically illegitimate, this consensus has been erased over the past decade. This is largely due to advances in population genetics that allow biologists to pick out genetic population clusters that approximate some of our common sense racial categories. In this paper, I argue that this new ability really ought not undermine our confidence in the biological illegitimacy of the human races. Unfortunately, the claim (...)
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  42. Clusters: On the structure of lexical concepts.Agustín Vicente - 2010 - Dialectica 64 (1):79-106.
    The paper argues for a decompositionalist account of lexical concepts. In particular, it presents and argues for a cluster decompositionalism, a view that claims that the complexes a token of a word corresponds to on a given occasion are typically built out of a determinate set of basic concepts, most of which are present on most other occasions of use of the word. The first part of the paper discusses some explanatory virtues of decompositionalism in general. The second singles out (...)
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  43. Ameliorating Algorithmic Bias, or Why Explainable AI Needs Feminist Philosophy.Linus Ta-Lun Huang, Hsiang-Yun Chen, Ying-Tung Lin, Tsung-Ren Huang & Tzu-Wei Hung - 2022 - Feminist Philosophy Quarterly 8 (3).
    Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can be handled (...)
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  44. Algorithmic Political Bias Can Reduce Political Polarization.Uwe Peters - 2022 - Philosophy and Technology 35 (3):1-7.
    Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...)
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  45. Algorithm and Parameters: Solving the Generality Problem for Reliabilism.Jack C. Lyons - 2019 - Philosophical Review 128 (4):463-509.
    The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of (...)
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  46. (1 other version)A property cluster theory of cognition.Cameron Buckner - 2013 - Philosophical Psychology (3):1-30.
    Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical tests that comparative (...)
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  47. (1 other version)Algorithmic correspondence and completeness in modal logic. IV. Semantic extensions of SQEMA.Willem Conradie & Valentin Goranko - 2008 - Journal of Applied Non-Classical Logics 18 (2):175-211.
    In a previous work we introduced the algorithm \SQEMA\ for computing first-order equivalents and proving canonicity of modal formulae, and thus established a very general correspondence and canonical completeness result. \SQEMA\ is based on transformation rules, the most important of which employs a modal version of a result by Ackermann that enables elimination of an existentially quantified predicate variable in a formula, provided a certain negative polarity condition on that variable is satisfied. In this paper we develop several extensions of (...)
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  48.  55
    Protecting the Hadoop Cluster on the Basis of Big Data Security.Pamarthi Kartheek - 2023 - Journal of Artificial Intelligence, Machine Learning and Data Science 1 (3):831-837.
    Gathering and analyzing enormous volumes of data is known as "big data," and it includes information from users, sensors, healthcare providers, and companies. Using the Hadoop framework, large amounts of data are stored, managed, and dispersed across multiple server nodes. Big Data issues, including security holes in the Hadoop Distributed File System (HDFS), the architecture's core layer, are highlighted in this article. The methodology includes setting up a Hadoop environment, integrating Kerberos for authentication, enabling HDFS encryption zones, implementing SSL/TLS for (...)
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  49. Informationally-connected property clusters, and polymorphism.Manolo Martínez - 2015 - Biology and Philosophy 30 (1):99-117.
    I present and defend a novel version of the homeostatic property cluster account of natural kinds. The core of the proposal is a development of the notion of co-occurrence, central to the HPC account, along information-theoretic lines. The resulting theory retains all the appealing features of the original formulation, while increasing its explanatory power, and formal perspicuity. I showcase the theory by applying it to the problem of reconciling the thesis that biological species are natural kinds with the fact that (...)
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  50. Algorithms and Arguments: The Foundational Role of the ATAI-question.Paola Cantu' & Italo Testa - 2011 - In Frans H. van Eemeren, Bart Garssen, David Godden & Gordon Mitchell, Proceedings of the Seventh International Conference of the International Society for the Study of Argumentation. Rozenberg / Sic Sat.
    Argumentation theory underwent a significant development in the Fifties and Sixties: its revival is usually connected to Perelman's criticism of formal logic and the development of informal logic. Interestingly enough it was during this period that Artificial Intelligence was developed, which defended the following thesis (from now on referred to as the AI-thesis): human reasoning can be emulated by machines. The paper suggests a reconstruction of the opposition between formal and informal logic as a move against a premise of an (...)
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