Results for 'Machine Learning'

979 found
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  1. Machine Learning, Functions and Goals.Patrick Butlin - 2022 - Croatian Journal of Philosophy 22 (66):351-370.
    Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise (...)
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  2. 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 (...)
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  3. Explainable machine learning practices: opening another black box for reliable medical AI.Emanuele Ratti & Mark Graves - 2022 - AI and Ethics:1-14.
    In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine (...) tools to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)
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  4. 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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  5. (1 other version)Using Machine Learning for Non-Sentential Utterance Classification.Jonathan Ginzburg & Shalom Lappin - unknown
    In this paper we investigate the use of machine learning techniques to classify a wide range of non-sentential utterance types in dialogue, a necessary first step in the interpretation of such fragments. We train different learners on a set of contextual features that can be extracted from PoS information. Our results achieve an 87% weighted f-score—a 25% improvement over a simple rule-based algorithm baseline.
     
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  6. 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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  7.  29
    Machine Learning in Psychometrics and Psychological Research.Graziella Orrù, Merylin Monaro, Ciro Conversano, Angelo Gemignani & Giuseppe Sartori - 2020 - Frontiers in Psychology 10:492685.
    Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls (...)
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  8. (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, 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 (...)
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  9.  21
    Machine Learning.Paul Thagard - 1998 - In George Graham & William Bechtel, A Companion to Cognitive Science. Blackwell. pp. 245–249.
    Machine learning is the study of algorithms that enable computers to improve their performance and increase their knowledge base. Research in machine learning has taken place since the beginning of artificial intelligence in the mid‐1950s. The first notable success was Arthur Samuel's program that learned to play checkers well enough to beat skilled humans. The program estimated the best move in a situation by using a mathematical function whose sixteen parameters describe board positions, and it improved (...)
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  10.  62
    Testimonial injustice in medical machine learning.Giorgia Pozzi - 2023 - Journal of Medical Ethics 49 (8):536-540.
    Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby consider how ML systems may silence patients’ voices (...)
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  11. 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 (...)
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  12.  66
    On Machine Learning and the Replacement of Human Labour: Anti-Cartesianism versus Babbage’s path.Felipe Tobar & Rodrigo González - 2022 - AI and Society 37 (4):1459-1471.
    This paper addresses two methodological paths in Artificial Intelligence: the paths of Babbage and anti-Cartesianism. While those researchers who have followed the latter have attempted to reverse the Cartesian dictum according to which machines cannot think in principle, Babbage’s path, which has been partially neglected, implies that the replacement of humans—and not the creation of minds—should provide the foundation of AI. In view of the examined paths, the claim that we support here is this: in line with Babbage, AI researchers (...)
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  13. Introduction: Machine learning as philosophy of science.Kevin B. Korb - 2004 - Minds and Machines 14 (4):433-440.
    I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
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  14.  66
    Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data.Reuben Binns & Michael Veale - 2017 - Big Data and Society 4 (2):205395171774353.
    Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate (...)
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  15. Using machine learning to predict decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning (...)
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  16. 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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  17.  62
    Machine learning and essentialism.Kristina Šekrst & Sandro Skansi - 2022 - Philosophical Problems in Science 73:171-196.
    Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based (...)
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  18. Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.
    Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.
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  19.  99
    Machine learning by imitating human learning.Chang Kuo-Chin, Hong Tzung-Pei & Tseng Shian-Shyong - 1996 - Minds and Machines 6 (2):203-228.
    Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to (...)
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  20.  56
    Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?Chang Ho Yoon, Robert Torrance & Naomi Scheinerman - 2022 - Journal of Medical Ethics 48 (9):581-585.
    We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely addressed by current methods of appraising medical interventions like pharmacological therapies; second, a number of judicial precedents underpinning medical liability and negligence are compromised when ‘autonomous’ ML recommendations are considered to be en par with human instruction (...)
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  21. Machine learning, inductive reasoning, and reliability of generalisations.Petr Spelda - 2020 - AI and Society 35 (1):29-37.
    The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper (...)
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  22.  47
    Machine learning and the quest for objectivity in climate model parameterization.Julie Jebeile, Vincent Lam, Mason Majszak & Tim Räz - 2023 - Climatic Change 176 (101).
    Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help to improve climate model parameterization in several ways, they still require (...)
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  23.  7
    Machine learning, healthcare resource allocation, and patient consent.Jamie Webb - 2024 - The New Bioethics 30 (3):206-227.
    The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of (...)
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  24. Machine Learning and the Future of Scientific Explanation.Florian J. Boge & Michael Poznic - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (1):171-176.
    The workshop “Machine Learning: Prediction Without Explanation?” brought together philosophers of science and scholars from various fields who study and employ Machine Learning (ML) techniques, in order to discuss the changing face of science in the light of ML's constantly growing use. One major focus of the workshop was on the impact of ML on the concept and value of scientific explanation. One may speculate whether ML’s increased use in science exemplifies a paradigmatic turn towards mere (...)
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  25.  49
    Machine learning and power relations.Jonne Maas - forthcoming - AI and Society.
    There has been an increased focus within the AI ethics literature on questions of power, reflected in the ideal of accountability supported by many Responsible AI guidelines. While this recent debate points towards the power asymmetry between those who shape AI systems and those affected by them, the literature lacks normative grounding and misses conceptual clarity on how these power dynamics take shape. In this paper, I develop a workable conceptualization of said power dynamics according to Cristiano Castelfranchi’s conceptual framework (...)
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  26. Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice.Dan Li - 2023 - Minds and Machines 33 (3):429-450.
    As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help (...)
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  27. Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...)
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  28.  64
    Machine learning in human creativity: status and perspectives.Mirko Farina, Andrea Lavazza, Giuseppe Sartori & Witold Pedrycz - 2024 - AI and Society 39 (6):3017-3029.
    As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These (...)
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  29.  90
    Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation.Dunia J. Mahboobeh, Sofia B. Dias, Ahsan H. Khandoker & Leontios J. Hadjileontiadis - 2022 - Frontiers in Psychology 13:857249.
    Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (...)
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  30.  56
    Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter.Areej Babiker, Ibrahima Faye, Kristin Prehn & Aamir Malik - 2015 - Frontiers in Psychology 6.
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  31.  45
    Predicting and explaining with machine learning models: Social science as a touchstone.Oliver Buchholz & Thomas Grote - 2023 - Studies in History and Philosophy of Science Part A 102 (C):60-69.
    Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we (...)
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  32.  7
    Machine learning, healthcare resource allocation, and patient consent.Jamie Webb - 2024 - The New Bioethics 30 (3):206-227.
    The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of (...)
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  33.  9
    Machine Learning, Synthetic Data, and the Politics of Difference.Benjamin N. Jacobsen - 2025 - Theory, Culture and Society 42 (3):41-57.
    What is the relationship between ideas of sameness and difference for machine learning and AI? Algorithms are often understood to participate in the continual displacement of the different and heterogeneous in society in favour of sameness, of that which is socio-politically similar and proximate. In contrast to this prevalent emphasis on sameness, however, this paper argues that there is a nascent heterophilic logic underpinning the intersection of synthetic data and machine learning, a move towards actively generating (...)
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  34. Machine learning and suicide prevention: considering context as a guide to ethical design.Phoebe Friesen & Katie O'Leary - 2019 - In Kelso Cratsley & Jennifer Radden, Mental Health as Public Health: Interdisciplinary Perspectives on the Ethics of Prevention. San Diego, CA: Elsevier.
  35.  17
    A Machine-Learning Approach to Autonomous Music Composition.R. N. Lichtenwalter, K. Lichtenwalter & N. V. Chawla - 2010 - Journal of Intelligent Systems 19 (2):95-124.
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  36.  92
    Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based.Liam G. McCoy, Connor T. A. Brenna, Stacy S. Chen, Karina Vold & Sunit Das - 2022 - Journal of Clinical Epidemiology 142:252-257.
    Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding (...)
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  37. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Jessica Dai, Sina Fazelpour & Zachary Lipton, Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the (...)
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  38. Machine learning in healthcare and the methodological priority of epistemology over ethics.Thomas Grote - 2025 - Inquiry: An Interdisciplinary Journal of Philosophy 68 (4):1218-1247.
    This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...)
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  39.  56
    Machine learning applications in healthcare and the role of informed consent: Ethical and practical considerations.Giorgia Lorenzini, David Martin Shaw, Laura Arbelaez Ossa & Bernice Simone Elger - 2023 - Clinical Ethics 18 (4):451-456.
    Informed consent is at the core of the clinical relationship. With the introduction of machine learning (ML) in healthcare, the role of informed consent is challenged. This paper addresses the issue of whether patients must be informed about medical ML applications and asked for consent. It aims to expose the discrepancy between ethical and practical considerations, while arguing that this polarization is a false dichotomy: in reality, ethics is applied to specific contexts and situations. Bridging this gap and (...)
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  40.  20
    What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks.Lina Abed Ibrahim & István Fekete - 2019 - Frontiers in Psychology 9.
    This study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German-LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and nonword-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed according to the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across (...)
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  41.  46
    A machine learning approach to recognize bias and discrimination in job advertisements.Richard Frissen, Kolawole John Adebayo & Rohan Nanda - 2023 - AI and Society 38 (2):1025-1038.
    In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...)
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  42.  27
    Machine learning in tutorials – Universal applicability, underinformed application, and other misconceptions.Andreas Breiter, Juliane Jarke & Hendrik Heuer - 2021 - Big Data and Society 8 (1).
    Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine (...)
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  43. Machine learning theory and practice as a source of insight into universal grammar.Stuartm Shieber - unknown
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments (...)
     
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  44.  19
    Weighted Classification of Machine Learning to Recognize Human Activities.Guorong Wu, Zichen Liu & Xuhui Chen - 2021 - Complexity 2021:1-10.
    This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking (...)
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  45. Argument based machine learning applied to law.Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko - 2005 - Artificial Intelligence and Law 13 (1):53-73.
    In this paper we discuss the application of a new machine learning approach – Argument Based Machine Learning – to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as (...)
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  46. Machine learning theory and practice as a source of insight into universal grammar.Shalom Lappin - unknown
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments (...)
     
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  47.  29
    Bias, machine learning, and conceptual engineering.Rachel Etta Rudolph, Elay Shech & Michael Tamir - forthcoming - Philosophical Studies:1-29.
    Large language models (LLMs) such as OpenAI’s ChatGPT reflect, and can potentially perpetuate, social biases in language use. Conceptual engineering aims to revise our concepts to eliminate such bias. We show how machine learning and conceptual engineering can be fruitfully brought together to offer new insights to both conceptual engineers and LLM designers. Specifically, we suggest that LLMs can be used to detect and expose bias in the prototypes associated with concepts, and that LLM de-biasing can serve conceptual (...)
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  48.  24
    Machine Learning in Society: Prospects, Risks, and Benefits.Mirko Farina & Witold Pedrycz - 2024 - Philosophy and Technology 37 (3):1-8.
    Machine Learning (ML) is revolutionizing the functioning of our societies and reshaping much of the economic tissue underlying them. The deep integration of ML into the fabric of our lives has changed to way we work and communicate and how we relate to each other. In this Topical Collection we reflect on the reach and impact of this AI (ML-driven) revolution in our society, critically analyzing some of the most important ethical, epistemological, scientific, and sociological issues underlying it.
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  49.  6
    Machine Learning-Generated Clinical Data as Collateral Research: A Global Neuroethical Analysis.Michael O. S. Afolabi & Stephen Sodeke - 2024 - American Journal of Bioethics 24 (10):103-106.
    Volume 24, Issue 10, October 2024, Page 103-106.
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  50. Building machine learning pipelines.H. Hapke & L. Nelson - 2020 - O’Reilly Media.
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