According to high-levelism, one can perceptually be aware of high-level properties such as natural kind properties. Against high-levelism, the Gestalt proposal suggests that instead of high-level properties, one can have a perceptual experience as of Gestalt properties, i.e., determinables of determinate low-level properties. When one looks at a bird, the high-levelist argues that one can perceive the property of being a bird, and the proponent of the Gestalt proposal argues that one first perceives the property of having the (...) bird Gestalt (shared by all and only birds) and only post-perceptually recognizes that it is a bird. In the present study, to resolve the dispute between high-levelism and the Gestalt proposal, I aim to test their abilities to explain the attribution of multiple perceptible properties to the same object by making use of various empirical studies on high-level aftereffects. I conclude that the Gestalt proposal fails the test and hence shall not be a viable alternative to high-levelism. (shrink)

The use of “levels of abstraction” in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section “Some Definitions and Preliminary Examples” the nature and applicability of the epistemological method of levels of abstraction is clarified. In section “A Classic Application of the Method ofion”, the philosophical fruitfulness of (...) the new method is shown by using Kant’s classic discussion of the “antinomies of pure reason” as an example. In section “The Philosophy of the Method of Abstraction”, the method is further specified and supported by distinguishing it from three other forms of “levelism”: (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems. (shrink)

Act-utilitarianism and other theories in normative ethics confront the implementability problem: normal human agents, with normal human epistemic abilities, lack the information needed to use those theories directly for the selection of actions. Two Level Theories have been offered in reply. The theoretical level component states alleged necessary and sufficient conditions for moral rightness. That component is supposed to be true, but is not intended for practical use. It gives an account of objective obligation. The practical level component is offered (...) as an implementable system for the choice of actions by agents lacking some relevant information. It gives an account of subjective obligation. Several different ways of developing Two Levelism are explained and criticized. Five conditions that must be satisfied if the practical level principle is to be a good match for a given theoretical level principle are stated. A better form of Two Levelism is presented. (shrink)

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 (...) emphasizes the central role of context and agency in evaluating new and emerging technologies. We show how epistemological and ethical considerations are inextricably intertwined in contemporary debates on algorithmic bias and explainability. We trace the dialectical interplay between dogmatic and skeptical narratives across disciplines, merging insights from social theory and philosophy. We review a number of theories of explanation, ultimately endorsing a sociotechnical pragmatism that combines elements of Floridi’s levelism and Mayo’s reliabilism to place a special emphasis on notions of agency and trust. We conclude that this hybrid does more to promote fairness, accountability, and transparency in machine learning than dogmatic or skeptical alternatives. (shrink)