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When and why are motivational trade-offs evidence of sentience?
Get PDFMotivational trade-off behaviours, where an organism behaves as if flexibly weighing up an opportunity for reward against a risk of injury, are often regarded as evidence that the organism has valenced experiences like pain. This type of evidence has been influential in shifting opinion regarding crabs and insects. Critics note that (i) the precise links between trade-offs and consciousness are not fully known; (ii) simple trade-offs are evinced by the nematode worm Caenorhabditis elegans, mediated by a mechanism plausibly too simple to support conscious experience; (iii) pain can sometimes interfere with rather than support making trade-offs rationally. However, rather than undermining trade-off evidence in general, such cases show that the nature of the trade-off, and its underlying neural substrate, matter. We investigate precisely how
Beyond biological and social normativity: varieties of norm deviation and the justification for intervention
Get PDFThe most common theoretical approaches to defining mental disorder are naturalism, normativism, and hybridism. Naturalism and normativism are often portrayed as diametrically opposed, with naturalism grounded in objective science and normativism grounded in social convention and values. Hybridism is seen as a way of combining the two. However, all three approaches share a common feature in that they conceive of mental disorders as deviations from norms. Naturalism concerns biological norms; normativism concerns social norms; and hybridism, both biological and social norms. This raises the following two questions: (a) Are biological and social norms the only sorts of norms that are relevant to considerations of mental disorder? (b) Should addressing norm deviations continue to be a major focus of mental healthcare? This paper introduces several norms that are relevant to mental disorder beyond the biological and social. I argue that mental disorders often deviate from individual, well-being, and regulatory norms. I also consider approaches which question mental healthcare’s focus on addressing norm deviations in the first place, including the neurodiversity paradigm, social model of disability, and Mad discourse. Utilizing these critical approaches, I contend that whether mental health intervention is justified depends, in part, on the type of norm deviation being intervened upon
Beyond transparency: computational reliabilism as an externalist epistemology of algorithms
Get PDFThis chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms –such as functions and variables– and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm’s output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms
Notes on a future quantum event-ontology
Get PDFThis essay is a two-step reflection on the question 'Which events (can be said to) occur in quantum phenomena?' The first step regiments the ontological category of "statistical phenomena" and studies the adequacy of "probabilistic event models" as descriptions thereof. Guided by the conviction that quantum phenomena are to be circumscribed within this same ontological category, the second step highlights the peculiarities of probabilistic event models of some non-relativistic quantum phenomena, and thereby of what appear to be some plausible answers to our initial question. The reflection ends in an aporetic state, as it is by now usual in encounters between ontology and the quantum
Natural Kinds as Homeorhetic Dynamic Systems
Get PDFPhilosophers have become increasingly aware of the difficulties that plague accounts of kinds with objectively determined boundaries, and generally recognise that scientific taxonomies are shaped by human pragmatic interests and non-epistemic values. Against this trend, we propose an account of kinds conceived as dynamic entities, characterised by qualitatively distinct and robust trajectories originating from bifurcation events in the development of complex systems. We argue that the Homeorhetic Dynamic Kinds account (HDK) can be applied to systems investigated in a variety of disciplinary contexts, ranging from biology, medicine, and the social sciences. Shifting the focus from a synchronic (homeostatic) to a dynamic and processual (homeorhetic) perspective, we show that HDK allows a better characterisation of discontinuities among kinds. We then outline its implications for pluralism, particularly how HDK can help us understand how scientific categories are shaped both by ontological aspects of developmental trajectories and by pragmatic, value-laden considerations
Scientific Progress: Normative, but Aimless
Get PDFDoes science have any aim(s)? If not, does it follow that the debate about scientific progress is somehow misguided or problematically non-objective? These are two of the central questions posed in Rowbottom’s Scientific Progress. In this paper, I argue that we should answer both questions in the negative. Science probably has no aims, certainly not a single aim; but it does not follow from this that the debate about scientific progress is somehow misguided or problematically non-objective
Values, disagreement, and psychiatric classification
Get PDFIt has been argued that non-epistemic values have legitimate roles to play in the classification of psychiatric disorders. Such a value-laden view on psychiatric classification raises questions about the extent to which expert disagreements over psychiatric classification are fueled by disagreements over value judgments and the extent to which these disagreements could be resolved. This paper addresses these questions by arguing for two theses. First, a major source of disagreements about psychiatric classification is factual and concerns what social consequences a classification decision will have. This type of disagreement can be addressed by empirical research, although obtaining and evaluating relevant empirical evidence often requires interdisciplinary collaboration. Second, there is also a type of disagreement over value judgments; namely, disagreements over which aims of psychiatric classification should be prioritized. To address this type of value disagreement, it is helpful to develop a plurality of different psychiatric classification systems, each targeted toward satisfying a different subset of stakeholder aims
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
Shaking up the dogma: Solving trade-offs without (moral) values in machine learning
Get PDFThe field of machine learning intricately links ethical and epistemological considerations in many contexts which raises the question as to their precise relation. This paper tries to provide a partial answer by focusing on one particular context, namely, the trade-off between accuracy and interpretability, which can be considered a prime example for the entanglement of ethics and epistemology in machine learning. At its core, the trade-off states that any choice of a machine learning model needs to balance the conflicting desiderata of achieving accurate predictions and an interpretable functionality. On a widely shared view inspired by the argument from inductive risk, this balancing of conflicting desiderata can only be resolved by appeal to non-epistemic values. By contrast, we argue that, in certain settings, the accuracy-interpretability trade-off can be resolved on purely epistemic grounds. To that end, we closely analyze the general nature of trade-offs as well as the notions of accuracy and interpretability. This allows us to derive strategies for resolving the accuracy-interpretability trade-off that center around choosing the right epistemic frame for a given machine learning application and, thus, do not require non-epistemic considerations. We conclude by sketching the implications of this result for the general relation of ethical and epistemological considerations in ML