Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic or epistemic norms. Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation. Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic (...) accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Why is it rational for scientists to pursue multiple models of a phenomenon at the same time? The literatures on mechanistic inquiry and scientific pursuit each develop answers to a version of this question which is rarely discussed by the other. The mechanistic literature suggests that scientists pursue different complementary models because each model provides detailed insights into different aspects of the phenomenon under investigation. The pursuit literature suggests that scientists pursue competing models because alternative models promise to solve outstanding (...) empirical and conceptual problems. Looking into research on visual processing as a case study, we suggest an integrated account of why it is rational for scientists to pursue both complementary and competing models of the same mechanism in scientific practice. (shrink)

In contemporary philosophy of science, the consensus view seems to be that scientific explanations describe mechanisms responsible for the phenomena to be explained. Two kinds of explanatory relevance figure in mechanistic accounts of explanation: causal and constitutive. Following prominent accounts, it seems natural to analyze both these relations in terms of systematic interventions into some factor X with respect to another factor Y. However, such interventions are tailored to uncover causal relations only. Construing the constitutive relationship between parts and wholes (...) in terms of interventions thus raises metaphysical, conceptual, and epistemological questions. We here review the barriers that intervention-based inquiry into mechanisms encounters and consider some solutions. (shrink)

Abstract In this article, we investigate the merits of an enactive view of cognition for the contemporary debate about social cognition. If enactivism is to be a genuine alternative to classic cognitivism, it should be able to bridge the “cognitive gap”, i.e. provide us with a convincing account of those higher forms of cognition that have traditionally been the focus of its cognitivist opponents. We show that, when it comes to social cognition, current articulations of enactivism are—despite their celebrated successes (...) in explaining some cases of social interaction—not yet up to the task. This is because they (1) do not pay sufficient attention to the role of offline processing or “decoupling”, and (2) obscure the cognitive gap by overemphasizing the role of phenomenology. We argue that the main challenge for the enactive view will be to acknowledge the importance of both coupled (online) and decoupled (offline) processes for basic and advanced forms of (social) cognition. To meet this challenge, we articulate a dynamic embodied view of cognition. We illustrate the fruitfulness of this approach by recourse to recent findings on false belief understanding. Content Type Journal Article Pages 1-23 DOI 10.1007/s11097-011-9223-1 Authors Leon C. de Bruin, Department of Philosophy II, Ruhr-University Bochum, Universitätsstr. 150, 44801 Bochum, Germany Lena Kästner, Department of Philosophy II, Ruhr-University Bochum, Universitätsstr. 150, 44801 Bochum, Germany Journal Phenomenology and the Cognitive Sciences Online ISSN 1572-8676 Print ISSN 1568-7759. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic (Craver, in: Kaiser, Scholz, Plenge, Hüttemann (eds) Explanation in the special sciences: the case of biology and history, Springer, Dordrecht, pp 27–52, 2014) or epistemic norms (Bechtel in Mental mechanisms: philosophical perspectives on cognitive neuroscience, Routledge, London, 2008). Still, mechanistic philosophers on both (...) sides agree that there is no sharp distinction between the processes of discovery and explanation (Bechtel and Richardson in Discovering complexity. Decomposition and localization as strategies in scientific research, MIT Press, Cambridge, 2010; Craver and Darden in In search of mechanisms: discoveries across the life sciences, University of Chicago Press, Chicago, 2013). Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition (Haugeland, in: Having thought. Essays in the metaphysics of mind, Harvard University Press, Cambridge, pp 267–290, 1998) we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

The background for this special issue is the multidisciplinary workshop "Minds, Models and Mechanisms: Current Trends in Philosophy of Psychiatry" which was held at Saarland University in April 2021. Though we had to switch to an online format due to the pandemic, the discussions at the event have been extremely inspiring. It brought together experts from diverse disciplines, like clinical psychiatry, neuroscience, computational modelling, philosophy of mind and cognition, and philosophy of science. Joining forces, researchers from these disciplines presented their (...) ideas on the subject. In the aftermath of the workshop, the organizers issued an open call for contributions. After a rigorous review process five papers made it into the final issue, each making an original contribution to the field. (shrink)

The contributions in this part of the present issue mainly originate from the Carnap Lectures 2011 in Bochum where Prof. Tim Crane (Cambridge, UK) and Prof. Katalin Farkas (Budapest) presented keynote lectures under the heading “The Boundaries of the Mental”. The full workshop program is available on our website: http://www.ruhr-uni-bochum.de/philosophy/carnap2011/index.html.

Talk of mechanisms is ubiquitous in the natural sciences. Interdisciplinary fields such as biochemistry and pharmacy frequently discuss mechanisms with the assistance of diagrams. Such diagrams usually depict entities as structures or boxes and activities or interactions as arrows. While some of these arrows may indicate causal or componential relations, others may represent temporal or operational orders. Importantly, what kind of relation an arrow represents may not only vary with context but also be underdetermined by empirical data. In this manuscript, (...) we investigate how an analysis of pharmacological mechanisms in terms of producing and underlying mechanisms—as discussed in the contemporary philosophy of science—may shed light on these issues. Specifically, we shall argue that while pharmacokinetic mechanisms usually describe causal chains of production, pharmacodynamics tends to focus on mechanisms of action underlying the in vivo effects of a drug. Considering the action of thyroid gland hormones in the human body as a case study, we further demonstrate that pharmacodynamic schemes tend to incorporate entities and interactions on multiple levels. Yet, traditional pharmacodynamic schemes are sketched “flat”, i.e., non-hierarchically. We suggest that transforming flat pharmacodynamic schemes into mechanistic multi-level representations may assist in disentangling the different kinds of mechanisms and relations depicted by arrows in flat schemes. The resulting Baumkuchen model provides a powerful and practical alternative to traditional flat schemes, as it explicates the relevant mechanisms and relations more clearly. On a more general note, our discussion demonstrates how pharmacology and related disciplines may benefit from applying concepts from the new mechanist philosophy to guide the interpretation of scientific diagrams. (shrink)

Explanations in psychiatry often integrate various factors relevant to psychopathology. Identifying genuine causes among them is theoretically and clinically important, but epistemically challenging. Woodward’s interventionism appears to provide a promising tool to achieve this. However, Woodward’s interventionism is too demanding to be applied to psychiatry. I thus introduce difference-making interventionism, which detects relevance in general rather than causation, to make interventionist reasoning viable in clinical practice. DMI mirrors the empirical reality of psychiatry even more closely than interventionism, but it needs (...) to be supplied with additional heuristics to disambiguate between causes and other difference-makers. To achieve this, I suggest employing heuristics based on multiple experiments, temporal order and scientific domain. (shrink)

Accounts of mental disorders focusing either on the brain as neurophysiological substrate or on systematic connections between symptoms are insufficient to account for the multifactorial nature of mental illnesses. Recently, multiplexes have been suggested to provide a holistic view of psychopathology that integrates data from different factors, at different scales, or across time. Intuitively, these multi-layered network structures present quite appealing models of mental disorders that can be constructed by powerful computational machinery based on increasing amounts of real-world data. In (...) this paper, I systematically examine what challenges psychopathology models face and to what extent different species of psychopathology models can address them. My analysis highlights that while multiplexes, as they are usually conceived, appear promising, they suffer from the same problems as other approaches. To remedy this, I suggest, we must go a step further and combine different kinds of multiplexes into 4D models. Once we embrace 4D multiplexes and identify appropriate ways to constrain them, we might unlock the true potential of multiplexes for making headway in psychopathology research. (shrink)

Volume 32, Issue 1, March 2019, Page 69-72.