Results for 'machine learning'

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  1.  22
    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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  2. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen 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 (...)
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  3.  37
    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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  4. 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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  5.  13
    Fairer Machine Learning in the Real World: Mitigating Discrimination Without Collecting Sensitive Data.Reuben Binns & Michael Veale - 2017 - Big Data and Society 4 (2).
    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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  6.  76
    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.  7
    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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  8. Fair Machine Learning Under Partial Compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In 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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  9.  42
    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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  10.  2
    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.
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  11.  11
    Machine Learning for Geophysical Characterization of Brittleness: Tuscaloosa Marine Shale Case Study.Mark Mlella, Ming Ma, Rui Zhang & Mehdi Mokhtari - 2020 - Interpretation 8 (3):T589-T597.
    Brittleness is one of the most important reservoir properties for unconventional reservoir exploration and production. Better knowledge about the brittleness distribution can help to optimize the hydraulic fracturing operation and lower costs. However, there are very few reliable and effective physical models to predict the spatial distribution of brittleness. We have developed a machine learning-based method to predict subsurface brittleness by using multidiscipline data sets, such as seismic attributes, rock physics, and petrophysics information, which allows us to implement (...)
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  12. 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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  13. Machine-Learning-Based Automatic Well-Log Completion and Generation: Examples From the Ordos Basin, China.Ziheng Guan, Xuan Tang, Bo Ran, Shaobin Guo, Jinchuan Zhang, Kefeng Du & Ting Jia - 2022 - Interpretation 10 (3):SJ91-SJ99.
    Oilfields have large amounts of old well-logging data, some of which were possibly lost or distorted for borehole situation, limiting the use of well-logging in formation evaluation. Machine-learning algorithms provide possibility to complete or correct bad quality logging, even to generate new loggings. We took 50 wells in the Ordos Basin, a prolific hydrocarbon production basin, as an example to complete and generate well- loggings. We applied three algorithms, such as random forest, extreme gradient boosting, and deep neural (...)
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  14.  6
    Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - forthcoming - Res Publica:1-28.
    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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  15.  38
    Machine Learning’s Limitations in Avoiding Automation of Bias.Daniel Varona, Yadira Lizama-Mue & Juan Luis Suárez - 2021 - AI and Society 36 (1):197-203.
    The use of predictive systems has become wider with the development of related computational methods, and the evolution of the sciences in which these methods are applied Solon and Selbst and Pedreschi et al.. The referred methods include machine learning techniques, face and/or voice recognition, temperature mapping, and other, within the artificial intelligence domain. These techniques are being applied to solve problems in socially and politically sensitive areas such as crime prevention and justice management, crowd management, and emotion (...)
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  16.  42
    Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics.Florian J. Boge & Paul Grünke - forthcoming - In Andreas Kaminski, Michael Resch & Petra Gehring (eds.), The Science and Art of Simulation II.
    In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of (...)
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  17.  8
    Machine Learning in Medicine: Should the Pursuit of Enhanced Interpretability Be Abandoned?Chang Ho Yoon, Robert Torrance & Naomi Scheinerman - forthcoming - Journal of Medical Ethics:medethics-2020-107102.
    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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  18.  11
    A Machine-Learning Benchmark for Facies Classification.Yazeed Alaudah, Patrycja Michałowicz, Motaz Alfarraj & Ghassan AlRegib - 2019 - Interpretation 7 (3):SE175-SE187.
    The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes or use different train and test splits. In addition, it is common for papers that apply machine learning for facies (...)
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  19.  5
    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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  20.  59
    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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  21.  18
    Identifying Ethical Considerations for Machine Learning Healthcare Applications.Danton S. Char, Michael D. Abràmoff & Chris Feudtner - 2020 - American Journal of Bioethics 20 (11):7-17.
    Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...
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  22.  3
    Machine Learning in Psychometrics and Psychological Research.Graziella Orrù, Merylin Monaro, Ciro Conversano, Angelo Gemignani & Giuseppe Sartori - 2020 - Frontiers in Psychology 10.
  23. Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data.Giambattista Albora & Andrea Zaccaria - 2022 - Complexity 2022:1-12.
    The relatedness between a country or a firm and a product is a measure of the feasibility of that economic activity. As such, it is a driver for investments at a private and institutional level. Traditionally, relatedness is measured using networks derived by country-level co-occurrences of product pairs, that is counting how many countries export both. In this work, we compare networks and machine learning algorithms trained not only on country-level data, but also on firms, which is something (...)
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  24.  13
    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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  25.  16
    Explaining Machine Learning Decisions.John Zerilli - 2022 - Philosophy of Science 89 (1):1-19.
    The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a (...)
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  26.  8
    Using Machine Learning to Assess Covariate Balance in Matching Studies.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):848-854.
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  27. Machines Learning Values.Steve Petersen - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, USA: Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: (...)
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  28.  20
    Machine Learning in Healthcare: Exceptional Technologies Require Exceptional Ethics.Kristine Bærøe, Maarten Jansen & Angeliki Kerasidou - 2020 - American Journal of Bioethics 20 (11):48-51.
    Char et al. describe an interesting and useful approach in their paper, “Identifying ethical considerations for machine learning healthcare applications.” Their proposed framework, which see...
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  29.  5
    Unsupervised Machine Learning Using 3D Seismic Data Applied to Reservoir Evaluation and Rock Type Identification.Marwa Hussein, Robert R. Stewart, Deborah Sacrey, Jonny Wu & Rajas Athale - 2021 - Interpretation 9 (2):T549-T568.
    Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations (...)
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  30.  11
    Using Machine Learning as an Aid to Seismic Geomorphology, Which Attributes Are the Best Input?Lennon Infante-Paez & Kurt J. Marfurt - 2019 - Interpretation 7 (3):SE1-SE18.
    Volcanic rocks with intermediate magma composition indicate distinctive patterns in seismic amplitude data. Depending on the processes by which they were extruded to the surface, these patterns may be chaotic, moderate-amplitude reflectors or continuous high-amplitude reflectors. We have identified appropriate seismic attributes that highlight the characteristics of such patterns and use them as input to self-organizing maps to isolate these volcanic facies from their clastic counterpart. Our analysis indicates that such clustering is possible when the patterns are approximately self-similar, such (...)
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  31. Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar. StuartmShieber - 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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  32.  7
    Machine Learning's Feet of Clay.Levente Kriston - 2020 - Journal of Evaluation in Clinical Practice 26 (1):373-375.
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  33.  68
    Machine Learning: A Structuralist Discipline?Christophe Bruchansky - 2019 - AI and Society 34 (4):931-938.
    Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. (...)
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  34.  12
    Using Machine Learning to Identify Structural Breaks in Single-Group Interrupted Time Series Designs.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):855-859.
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  35. 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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  36.  28
    Machine Learning and the Future of Realism.Giles Hooker & Cliff Hooker - 2018 - Spontaneous Generations 9 (1):174-182.
  37.  2
    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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  38.  15
    Combining Machine Learning and Matching Techniques to Improve Causal Inference in Program Evaluation.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):868-874.
  39.  7
    Combining Machine Learning and Propensity Score Weighting to Estimate Causal Effects in Multivalued Treatments.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):875-885.
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  40.  3
    Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science.Suzanne Kawamleh - 2021 - Philosophy of Science 88 (5):1008-1020.
    Scientists and decision makers rely on climate models for predictions concerning future climate change. Traditionally, physical processes that are key to predicting extreme events are either directly represented or indirectly represented. Scientists are now replacing physically based parameterizations with neural networks that do not represent physical processes directly or indirectly. I analyze the epistemic implications of this method and argue that it undermines the reliability of model predictions. I attribute the widespread failure in neural network generalizability to the lack of (...)
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  41. Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning.Maya Krishnan - 2020 - Philosophy and Technology 33 (3):487-502.
    The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a “black box problem.” That is, machine learning algorithms are “opaque” to human users, failing to be “interpretable” or “explicable” in terms that would render categorization procedures “understandable.” The purpose of this paper is to challenge (...)
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  42.  15
    On Machine Learning and the Replacement of Human Labour: Anti-Cartesianism Versus Babbage’s Path.Felipe Tobar & Rodrigo González - forthcoming - AI and Society:1-13.
    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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  43. Fast Machine-Learning Online Optimization of Ultra-Cold-Atom Experiments.P. B. Wigley, P. J. Everitt, A. van den Hengel, J. W. Bastian, M. A. Sooriyabandara, G. D. McDonald, K. S. Hardman, C. D. Quinlivan, P. Manju, C. C. N. Kuhn, I. R. Petersen, A. N. Luiten, J. J. Hope, N. P. Robins & M. R. Hush - 2016 - Sci. Rep 6:25890.
    We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical (...)
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  44.  6
    Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach is Needed.Helene Gerhards, Karsten Weber, Uta Bittner & Heiner Fangerau - 2020 - American Journal of Bioethics 20 (11):46-48.
    ML-HCAs have the potential to significantly change an entire healthcare system. It is not even necessary to presume that this will be disruptive but sufficient to assume that the mere adaptation of...
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  45.  7
    Using Machine Learning to Model Dose-Response Relationships.Ariel Linden, Paul R. Yarnold & Brahmajee K. Nallamothu - 2016 - Journal of Evaluation in Clinical Practice 22 (6):860-867.
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  46. 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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  47.  19
    Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness.Robert Long - 2021 - Journal of Moral Philosophy 19 (1):49-78.
    As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a “fairness tradeoff” between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, (...)
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  48. Machine Learning and the Cognitive Basis of Natural Language.Shalom Lappin - unknown
    Machine learning and statistical methods have yielded impressive results in a wide variety of natural language processing tasks. These advances have generally been regarded as engineering achievements. In fact it is possible to argue that the success of machine learning methods is significant for our understanding of the cognitive basis of language acquisition and processing. Recent work in unsupervised grammar induction is particularly relevant to this issue. It suggests that knowledge of language can be achieved through (...)
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  49.  8
    Conceptual Challenges for Interpretable Machine Learning.David S. Watson - 2022 - Synthese 200 (2):1-33.
    As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three (...)
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    Can Machine Learning Provide Understanding? How Cosmologists Use Machine Learning to Understand Observations of the Universe.Helen Meskhidze - forthcoming - Erkenntnis:1-15.
    The increasing precision of observations of the large-scale structure of the universe has created a problem for simulators: running the simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as black-boxes deliver scientific understanding? In this paper, I investigate how cosmologists employ ML, arguing that (...)
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