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David Watson [46]David S. Watson [12]David Lindsay Watson [7]David L. Watson [2]
David M. Watson [1]
  1. The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  2. 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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  3. The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David Watson - 2019 - Minds and Machines 29 (3):417-440.
    Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...)
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  4.  44
    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 (IML) 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 conceptual challenges that (...)
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  5.  32
    The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealisedexplanation gamein which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of (...)
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  6.  22
    The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2021 - In Josh Cowls & Jessica Morley (eds.), The 2020 Yearbook of the Digital Ethics Lab. Springer Verlag. pp. 109-143.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  7.  25
    Health complaints, stress, and distress: Exploring the central role of negative affectivity.David Watson & James W. Pennebaker - 1989 - Psychological Review 96 (2):234-254.
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  8.  68
    The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?Jakob Mökander, Prathm Juneja, David S. Watson & Luciano Floridi - 2022 - Minds and Machines 32 (4):751-758.
    On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).
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  9.  29
    On the Philosophy of Unsupervised Learning.David S. Watson - 2023 - Philosophy and Technology 36 (2):1-26.
    Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...)
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  10.  33
    Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.David S. Watson, Limor Gultchin, Ankur Taly & Luciano Floridi - 2022 - Minds and Machines 32 (1):185-218.
    Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence, a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence, we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of (...)
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  11. The epistemological foundations of data science: a critical review.Luciano Floridi, Mariarosaria Taddeo, Vincent Wang, David Watson & Jules Desai - 2022 - Synthese 200 (6):1-27.
    The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...)
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  12.  68
    The Ethics of Online Controlled Experiments (A/B Testing).Andrea Polonioli, Riccardo Ghioni, Ciro Greco, Prathm Juneja, Jacopo Tagliabue, David Watson & Luciano Floridi - 2023 - Minds and Machines 33 (4):667-693.
    Online controlled experiments, also known as A/B tests, have become ubiquitous. While many practical challenges in running experiments at scale have been thoroughly discussed, the ethical dimension of A/B testing has been neglected. This article fills this gap in the literature by introducing a new, soft ethics and governance framework that explicitly recognizes how the rise of an experimentation culture in industry settings brings not only unprecedented opportunities to businesses but also significant responsibilities. More precisely, the article (a) introduces a (...)
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  13. Crowdsourced science: sociotechnical epistemology in the e-research paradigm.David Watson & Luciano Floridi - 2018 - Synthese 195 (2):741-764.
    Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we show how information (...)
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  14.  34
    Emotion Blends and Mixed Emotions in the Hierarchical Structure of Affect.David Watson & Kasey Stanton - 2017 - Emotion Review 9 (2):99-104.
    We explore the implications of a hierarchical structure, consisting of the higher order dimensions of nonspecific Positive Activation and Negative Activation and multiple specific negative affects and positive affects at the lower level. Emotional blends of the same valence are an essential part of this structure and form the basis of the higher order Negative and Positive Activation dimensions. Mixed cross-valence emotions are not central to this hierarchical scheme but are compatible with it. We examine the frequency of pure emotional (...)
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  15. Causal feature learning for utility-maximizing agents.David Kinney & David Watson - 2020 - In David Kinney & David Watson (eds.), International Conference on Probabilistic Graphical Models. pp. 257–268.
    Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, (...)
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  16. Local explanations via necessity and sufficiency: unifying theory and practice.David Watson, Limor Gultchin, Taly Ankur & Luciano Floridi - 2022 - Minds and Machines 32:185-218.
    Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors (...)
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  17.  41
    A Genealogical Approach to Algorithmic Bias.Marta Ziosi, David Watson & Luciano Floridi - 2024 - Minds and Machines 34 (2):1-17.
    The Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness metrics), rather than addressing the underlying social and technical conditions that (re)produce it. In this article, we propose a complementary strategy that uses genealogy as a constructive, epistemic critique to explain algorithmic bias in terms of the conditions that enable it. We focus on XAI feature attributions (Shapley values) and counterfactual approaches as potential tools to gauge these conditions (...)
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  18.  10
    Reply to Tom Sterkenburg’s Commentary.David S. Watson - 2023 - Philosophy and Technology 36 (4):1-4.
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  19.  23
    Are the Dead Taking Over Instagram? A Follow-up to Öhman & Watson.Carl Öhman & David Watson - 2021 - In Josh Cowls & Jessica Morley (eds.), The 2020 Yearbook of the Digital Ethics Lab. Springer Verlag. pp. 5-21.
    In a previous article, we projected the future accumulation of profiles belonging to deceased users on Facebook. We concluded that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, on the other hand, this number will exceed 4.9 billion. Although these findings provided an important first step, one network alone remains insufficient to establish a quantitative foundation for further macro-level analysis (...)
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  20.  11
    Scientists are human.David Lindsay Watson - 1938 - New York: Arno Press.
  21. The epistemological foundations of data science: a critical analysis.Jules Desai, David Watson, Vincent Wang, Mariarosaria Taddeo & Luciano Floridi - manuscript
    The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...)
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  22.  72
    The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.Jakob Mökander, Margi Sheth, David S. Watson & Luciano Floridi - 2023 - Minds and Machines 33 (1):221-248.
    Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...)
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  23. Scientists Are Human.David Lindsay Watson & John Dewey - 1939 - Ethics 49 (3):374-375.
     
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  24.  29
    Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.Jakob Mökander, Margi Sheth, David S. Watson & Luciano Floridi - 2023 - Minds and Machines 33 (1):249-249.
  25.  36
    Preliminary Scales for ICD-11 Personality Disorder: Self and Interpersonal Dysfunction Plus Five Personality Disorder Trait Domains.Lee Anna Clark, Alejandro Corona-Espinosa, Shereen Khoo, Yuliya Kotelnikova, Holly F. Levin-Aspenson, Greg Serapio-García & David Watson - 2021 - Frontiers in Psychology 12.
    The ICD-11 personality disorder model is the first fully dimensional assessment of personality pathology. It consists of a personality disorder dysfunction-severity dimension, which encompasses both self- and interpersonal dysfunction, and six optional qualifiers for five prominent personality traits—Negative Affectivity, Detachment, Dissociality, Disinhibition, and Anankastia —plus a borderline pattern that is defined by the criteria of DSM-IV borderline PD. This article reports on the development of a new self-report measure to assess self- and interpersonal dysfunction and the five trait qualifiers. It (...)
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  26.  8
    Who runs our universities?David Watson - 2012 - Perspectives: Policy and Practice in Higher Education 16 (2):41-45.
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  27.  27
    Caring and Curing, a Philosophy of Medicine and Social Work.David Watson - 1982 - Philosophical Quarterly 32 (127):186-187.
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  28.  12
    The 2018 Yearbook of the Digital Ethics Lab.Carl Öhman & David Watson (eds.) - 2019 - Springer Verlag.
    This book explores a wide range of topics in digital ethics. It features 11 chapters that analyze the opportunities and the ethical challenges posed by digital innovation, delineate new approaches to solve them, and offer concrete guidance to harness the potential for good of digital technologies. The contributors are all members of the Digital Ethics Lab, a research environment that draws on a wide range of academic traditions. The chapters highlight the inherently multidisciplinary nature of the subject, which cannot be (...)
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  29. International Conference on Probabilistic Graphical Models.David Kinney & David Watson (eds.) - 2020
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  30.  3
    Slower Growth in the Western World.Neil Leighton, R. F. Stalley & David Watson - 1982 - William Heinemann.
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  31.  22
    The effects of social interaction, exercise, and test stress on positive and negative affect.Curtis W. McIntyre, David Watson & Anne C. Cunningham - 1990 - Bulletin of the Psychonomic Society 28 (2):141-143.
  32.  31
    The effect of induced social interaction on positive and negative affect.Curtis W. McIntyre, David Watson, Lee Anna Clark & Stephen A. Cross - 1991 - Bulletin of the Psychonomic Society 29 (1):67-70.
  33.  5
    Philosophy and the Concepts of Modern Science.David Lindsay Watson - 1936 - Philosophy of Science 3 (3):364-365.
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  34.  21
    Recognizing individual differences in predictive structure.Auke Tellegen, John Kamp & David Watson - 1982 - Psychological Review 89 (1):95-105.
  35.  39
    Philosophy in social work.Noel Timms & David Watson (eds.) - 1978 - Boston: Routledge and Kegan Paul.
    Introduction Most of the papers gathered here were contributions to a series of joint meetings of the Department of Social Administration and Social Work ...
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  36.  8
    Talking About Welfare: Readings in Philosophy and Social Policy.Noel W. Timms & David Watson - 1976 - Routledge.
    Originally published in 1976 Talking About Welfare is a collection of essays providing a general survey of the problems facing social welfare. The book introduces a number of philosophers, social workers and social administrators, concentrating on problems in describing a general philosophical orientation to social work, what it means to understand another person, and to problems in describing and justifying social work and social welfare activity. The essays collected contribute to discussion of a wide range of welfare issues, principally that (...)
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  37.  5
    Human Values.David Watson - 1980 - Philosophical Quarterly 30 (120):269-271.
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  38.  11
    Human Values: Royal Institute of Philosophy Lectures 1976/7.David Watson - 1980 - Mind 89 (354):307-310.
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  39. Arendt.David Watson - 1994 - Transactions of the Charles S. Peirce Society 30 (2):433-444.
     
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  40.  6
    A PCA-Based Active Appearance Model for Characterising Modes of Spatiotemporal Variation in Dynamic Facial Behaviours.David M. Watson & Alan Johnston - 2022 - Frontiers in Psychology 13.
    Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on (...)
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  41.  35
    Are the dead taking over Facebook? A Big Data approach to the future of death online.David S. Watson & Carl J. Öhman - 2019 - Big Data and Society 6 (1).
    We project the future accumulation of profiles belonging to deceased Facebook users. Our analysis suggests that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, however, this number will exceed 4.9 billion. In both cases, a majority of the profiles will belong to non-Western users. In discussing our findings, we draw on the emerging scholarship on digital preservation and stress the (...)
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  42. Against the megamachine: empire and the Earth.David Watson - forthcoming - Environmental Philosophy: From Animal Rights to Radical Ecology. Pearson Prentice Hall, Upper Saddle River.
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  43.  4
    Caring for Strangers: An Introduction to Practical Philosophy for Students of Social Administration.David Watson - 1980 - Routledge.
  44.  19
    Hannah Arendt and the American Republic.David Watson - 1992 - Transactions of the Charles S. Peirce Society 28 (3):423 - 465.
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  45.  15
    Hegelianism in the United States.David Watson - 1982 - Hegel Bulletin 3 (2):18-28.
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  46.  3
    Is anyone there?: (and does it really matter?).David Watson - 1979 - Wheaton, Ill.: H. Shaw Publishers.
  47.  9
    In Defense of Sociotechnical Pragmatism.David Watson & Jakob Mökander - 2023 - In Francesca Mazzi (ed.), The 2022 Yearbook of the Digital Governance Research Group. Springer Nature Switzerland. pp. 131-164.
    The current discourse on fairness, accountability, and transparency in machine learning is driven by two competing narratives: sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better algorithms; and sociotechnical skepticism, which opposes many instances of automation on principle. Both perspectives, we argue, are reductive and unhelpful. In this chapter, we review a large, diverse body of literature in an attempt to move beyond this restrictive duality, toward a pragmatic synthesis that (...)
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  48. In Life's School: A Book for Young Men.David Watson - 1902
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  49.  9
    Is UK HE fit for purpose?David Watson - 2002 - Perspectives: Policy and Practice in Higher Education 6 (3):63-72.
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  50.  11
    New Labour and higher education.David Watson - 2006 - Perspectives: Policy and Practice in Higher Education 10 (3):63-68.
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