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  1. Scientific Exploration and Explainable Artificial Intelligence.Carlos Zednik & Hannes Boelsen - 2022 - Minds and Machines 32 (1):219-239.
    Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future (...)
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  • Transparency as Manipulation? Uncovering the Disciplinary Power of Algorithmic Transparency.Hao Wang - 2022 - Philosophy and Technology 35 (3):1-25.
    Automated algorithms are silently making crucial decisions about our lives, but most of the time we have little understanding of how they work. To counter this hidden influence, there have been increasing calls for algorithmic transparency. Much ink has been spilled over the informational account of algorithmic transparency—about how much information should be revealed about the inner workings of an algorithm. But few studies question the power structure beneath the informational disclosure of the algorithm. As a result, the information disclosure (...)
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  • Technological Answerability and the Severance Problem: Staying Connected by Demanding Answers.Daniel W. Tigard - 2021 - Science and Engineering Ethics 27 (5):1-20.
    Artificial intelligence and robotic technologies have become nearly ubiquitous. In some ways, the developments have likely helped us, but in other ways sophisticated technologies set back our interests. Among the latter sort is what has been dubbed the ‘severance problem’—the idea that technologies sever our connection to the world, a connection which is necessary for us to flourish and live meaningful lives. I grant that the severance problem is a threat we should mitigate and I ask: how can we stave (...)
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  • Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).
    Explainable artificial intelligence is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek (...)
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  • ANNs and Unifying Explanations: Reply to Erasmus, Brunet, and Fisher.Yunus Prasetya - 2022 - Philosophy and Technology 35 (2):1-9.
    In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not make them (...)
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  • Values and inductive risk in machine learning modelling: the case of binary classification models.Koray Karaca - 2021 - European Journal for Philosophy of Science 11 (4):1-27.
    I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is (...)
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  • Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.
    Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...)
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  • A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge.Darren J. Edwards, Ciara McEnteggart & Yvonne Barnes-Holmes - 2022 - Frontiers in Psychology 13.
    Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining (...)
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  • AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...)
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  • Philosophy of science at sea: Clarifying the interpretability of machine learning.Claus Beisbart & Tim Räz - 2022 - Philosophy Compass 17 (6):e12830.
    Philosophy Compass, Volume 17, Issue 6, June 2022.
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