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What does it mean to say that science is value-laden?
Get PDFThe literature on values in science contains countless claims to the effect that a particular type of scientific choice is or is not value-laden. This chapter exposes an ambiguity in the notion of a value-laden choice. In the first half, I distinguish four ways a choice can be said to be value-laden. In the second half, I illustrate the usefulness of this taxonomy by assessing arguments about whether the value-ladenness of science is inevitable. I focus on the “randomizer reply,” which claims that, in principle, scientists could always avoid value-laden choices by flipping a coin
Decoding Cognitive Neuroscience: A Defence of the Explanatory Role of Content
Get PDFCognitive neuroscientists typically posit representations which relate to various aspects of the world, which philosophers call representational content. Anti-realists about representational content argue that contents play no role in neuroscientific explanations of cognitive capacities. In this paper, I defend realism against an anti-realist argument due to Frances Egan, who argues that for content to be explanatory it must be both essential and naturalistic. I introduce a case study from cognitive neuroscience in which content is both essential and naturalistic, meeting Egan’s challenge. I then spell out some general principles for identifying studies in which content plays an explanatory role
Three Kinds of AI Ethics
Get PDFThere is an overwhelming abundance of works in AI Ethics. This growth is chaotic because of how sudden it is, its volume, and its multidisciplinary nature. This makes difficult to keep track of debates, and to systematically characterize goals, research questions, methods, and expertise required by AI ethicists. In this article, I show that the relation between ‘AI’ and ‘ethics’ can be characterized in at least three ways, which correspond to three well-represented kinds of AI ethics: ethics and AI; ethics in AI; ethics of AI. I elucidate the features of these three kinds of AI Ethics, characterize their research questions, and identify the kind of expertise that each kind needs. I also show how certain criticisms to AI ethics are misplaced, as being done from the point of view of one kind of AI ethics, to another kind with different goals. All in all, this work sheds light on the nature of AI ethics, and sets the groundwork for more informed discussions about the scope, methods, and training of AI ethicists
Can AI systems have free will?
Get PDFWhile there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been very little discussion of whether AI systems could have free will. I sketch a framework for thinking about this question, inspired by Daniel Dennett’s work. I argue that, to determine whether an AI system has free will, we should not look for some mysterious property, expect its underlying algorithms to be indeterministic, or ask whether the system is unpredictable. Rather, we should simply ask whether we have good explanatory reasons to view the system as an intentional agent, with the capacity for choice between alternative possibilities and control over the resulting actions. If the answer is “yes”, then the system counts as having free will in a pragmatic and diagnostically useful sense
A disputable assumption behind the empirical equivalence between pilot-wave theory and standard quantum mechanics
Get PDFThe de Broglie-Bohm pilot-wave theory asserts that a complete characterization of an N-particle system is given by its wave function together with the (at-all-times-defined) positions of the particles, with the wave function always satisfying the Schrödinger equation and the positions evolving according to the deterministic "guiding equation". A complete agreement with the predictive apparatus of standard quantum mechanics, including the uncertainty principle and the probabilistic Born rule, is then said to emerge from these equations, without having to confer any special status to measurements or observers. Two key elements behind the proof of this complete agreement are absolute uncertainty and the POVM theorem. The former involves an alleged "naturally emerging, irreducible limitation on the possibility of obtaining knowledge within pilot-wave theory" and the latter establishes that the outcome distributions of all measurements are described by POVMs. Here, we argue that the derivations of absolute uncertainty and the POVM theorem depend upon the questionable assumption that "information is always configurationally grounded". We explain in detail why the offered rationale behind such an assumption is deficient and explore the consequences of having to let go of it
Consciousness as the dissipation of information
Get PDFDespite its growing appeal for the study of consciousness, the notion of entropy has yet to lead to widely supported new insights about the nature of phenomenal experience. Typically, entropy measures of brain activity are found to correlate with cognitive functions that are assumed to index consciousness. Taking a very different approach, this theoretical framework does not conflate consciousness with any function. It presents a series of premises to argue that consciousness is fundamentally characterized as inactionable perception, i.e. that does not give rise to macrophysical action. This is then fitted in a framework of perception and action as informational changes in a dynamical neural state space. In this model, inactionable perception naturally arises as the prediction-driven increase of concept-related entropy. This entails an increase of (Shannon) information while its efficacy to produce macrophysical effects decreases, which is here referred to as information dissipation, analogously to energy dissipation in thermodynamic systems. It results from inefficient sensorimotor coupling with the environment, which occurs when behavior is not fixed relative to the stimulus. Despite the posited inefficacy of conscious perception, it consists of action-specific information and can therefore be interpreted as potential behavior.
Starting from fundamental properties, this framework may provide a new and coherent conceptual basis for a fuller understanding of what consciousness is and how it relates to the physical world. Although many of its implications remain to be explored, it appears consistent with empirical findings, and prompts subtle reinterpretations of some classical results in perception research
Understanding (and) Machine Learning's Black Box Explanation Problems
Get PDFMachine learning (ML) is a major scientific success. Yet, ML models are notoriously considered black boxes, where this black boxness may refer to details of the ML model itself or details concerning its outcomes. Hence, there is a flourishing field of "eXplainable Artificial Intelligence" (XAI), providing means for rendering several aspects of ML more transparent. However, given their tremendous success,
why would we even want to explain black boxed ML models with XAI? I here suggest that, in order to answer this question, we first need to distinguish between proximate and ultimate aims in using XAI: While the proximate aim may be uniformly
to provide instruments for explaining aspects of ML to relevant stakeholders, the ultimate aim varies with the context of deployment. Furthermore, I argue that in
science, the ultimate aim is the understanding of scientific phenomena. I then sketch three paths along which understanding of phenomena may be gained by means of
ML and XAI. In a coda, I address the possibility of gaining understanding from ML directly, without explanations and XAI
Jury Theorems for Peer Review
Get PDFPeer review is often taken to be the main form of quality control on academic research. Usually journals carry this out. However, parts of maths and physics appear to have a parallel, crowd-sourced model of peer review, where papers are posted on the arXiv to be publicly discussed. In this paper we argue that crowd-sourced peer review is likely to do better than journal-solicited peer review at sorting papers by quality. Our argument rests on two key claims. First, crowd-sourced peer review will lead on average to more reviewers per paper than journal-solicited peer review. Second, due to the wisdom of the crowds, more reviewers will tend to make better judgments than fewer. We make the second claim precise by looking at the Condorcet Jury Theorem as well as two related jury theorems developed specifically to apply to peer review
The many-worlds view of quantum mechanics
Get PDFA particular version of the many-worlds interpretation is presented. I argue that the only ontology of quantum mechanics is the universal wavefunction following unitary deterministic evolution. The other part of the theory are postulates connecting experiences of agents in multiple parallel worlds with branches of this wavefunction
Reply on `Comment on Aur\'elien Drezet's defense of relational quantum mechanics' by Jay Lawrence, Marcin Markiewicz and Marek \'{Z}ukowski.
Get PDFWe respond briefly to the recent comment by Jay Lawrence, Marcin Markiewicz and Marek Zukowski [arXiv:2210.09025 and Found. Phys. 54, 45 (2024)] regarding our work defending RQM against their previous assessment. We refute the analysis proposed by the authors and rephrase our previous study in order to clarify the remaining ambiguities in our rebuttal