Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose (...) a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments. (shrink)

How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be deployed in people's ordinary conceptual practices? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social‐conceptual changes. Since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won't (...) be possible without corresponding world change. This tradition, however, tends to ignore that concepts don't only encode statistical, but also causal information. Paying attention to this feature of concepts, I argue, shows that conceptual engineering is not only possible. There is an imperative to conceptually‐engineer. (shrink)

Stephen Yablo’s notion of proportionality, despite controversies surrounding it, has played a significant role in philosophical discussions of mental causation and of high-level causation more generally. In particular, it is invoked in James Woodward’s interventionist account of high-level causation and explanation, and is implicit in a novel approach to constructing variables for causal modeling in the machine learning literature, known as causal feature learning (CFL). In this article, we articulate an account of proportionality inspired by (...) both Yablo’s account of proportionality and the CFL account of variable construction. The resulting account has at least three merits. First, it illuminates an important feature of the notion of proportionality, when it is adapted to a probabilistic and interventionist framework. The feature is that at the center of the notion of proportionality lies the concept of “determinate intervention effects.” Second, it makes manifest a virtue of (common types of) high-level causal/explanatory statements over low-level ones, when relevant intervention effects are determinate. Third, it overcomes a limitation of the CFL framework and thereby also addresses a challenge to interventionist accounts of high-level causation. (shrink)

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 less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the Unificationist model. It is argued that, on the Unificationist model, the features that makes something an explanation is sensitive to complexity. Therefore, on the Unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...) an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely (...) related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Causal understanding is a defining characteristic of human cognition. Like many animals, human children learn to control their bodily movements and act effectively in the environment. Like a smaller subset of animals, children intervene: they learn to change the environment in targeted ways. Unlike other animals, children grow into adults with the causal reasoning skills to develop abstract theories, invent sophisticated technologies and imagine alternate pasts, distant futures and fictional worlds. In this Review, we explore the development of (...) human-unique causal learning and reasoning from evolutionary and ontogenetic perspectives. We frame our discussion using an ‘interventionist’ approach. First, we situate causal understanding in relation to cognitive abilities shared with non-human animals. We argue that human causal understanding is distinguished by its depersonalized (objective) and decontextualized (general) representations. Using this framework, we next review empirical findings on early human causal learning and reasoning and consider the naturalistic contexts that support its development. Then we explore connections to related abilities. We conclude with suggestions for ongoing collaboration between developmental, cross-cultural, computational, neural and evolutionary approaches to causal understanding. (shrink)

Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes (...) sense of several aspects of actual explanatory practice, including the widespread use of equilibrium explanations, the formulation of distinct explanations for a single event, and the tight relationship between explanations of events and explanations of causal regularities. (shrink)

Any property has two sorts of causal features: “forward-looking” ones, having to do with what its instantiation can contribute to causing, and ldquo;backward-looking” ones, having to do with how its instantiation can be caused. Such features of a property are essential to it, and properties sharing all of their causal features are identical. Causal necessity is thus a special case of metaphysical necessity. Appeals to imaginability have no more force against this view than they do against the (...) Kripkean view that statements like “Gold is an element” are metaphysically necessary. (shrink)

Causal exclusion arguments are taken to threaten the autonomy of the special sciences, and the causal efficacy of mental properties. A recent line of response to these arguments has appealed to “independently plausible” and “well grounded” theories of causation to rebut key premises. In this paper I consider two papers which proceed in this vein and show that they share a common feature: they both require causes to be proportional to their effects. I argue that this (...) class='Hi'>feature is a bug, and one that generalises: any attempt to rescue the autonomy of the special sciences, or the efficacy of the mental, from exclusion worries had better not look to proportionality for help. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Causal essentialists hold that a property essentially bears its causal and nomic relations. Further, as many causal essentialists have noted, the main motivations for causal essentialism also motivate holding that properties are individuated in terms of their causal and nomic relations. This amounts to a kind of identity of indiscernibles thesis; properties that are indiscernible with respect to their causal and nomic relations are identical. This can be compared with the more well-known identity of (...) indiscernibles thesis, according to which particulars that are qualitatively indiscernible are identical. Robert Adams has developed a well-known objection to this thesis by considering a series of possibilities involving nearly qualitatively indiscernible particulars that naturally leads to a possibility involving qualitatively indiscernible particulars. I argue that we can construct parallel cases involving a series of possibilities involving properties that are nearly indiscernible with respect to their causal and nomic relations that naturally lead to possibilities involving properties that are indiscernible with respect to their causal and nomic relations. The same features that make Adams’ argument forceful also carry over to my cases, giving us a powerful objection to the causal essentialist identity of indiscernibles thesis. (shrink)

A central task of developmental psychology and philosophy of science is to show how humans learn radically new concepts. Famously, Fodor has argued that such learning is impossible if concepts have definitional structure and all learning is hypothesis testing. We present several learning processes that can generate novel concepts. They yield transformations of the fundamental feature space, generating new similarity structures which can underlie conceptual change. This framework provides a tractable, empiricist-friendly account that unifies and shores (...) up various strands of the neo-Quinean approach to conceptual development. (shrink)

Jaegwon Kim’s influential exclusion argument attempts to demonstrate the inconsistency of nonreductive materialism in the philosophy of mind. Kim’s argument begins by showing that the three main theses of nonreductive materialism, plus two additional considerations, lead to a specific and familiar picture of mental causation. The exclusion argument can succeed only if, as Kim claims, this picture is not one of genuine causal overdetermination. Accordingly, one can resist Kim’s conclusion by denying this claim, maintaining instead that the effects of (...) the mental are always causally overdetermined. I call this strategy the ‘ overdetermination challenge’. One of the main aims of this paper is to show that the overdetermination challenge is the most appropriate response to Kim’s exclusion argument, at least in its latest form. I argue that Kim fails to adequately respond to the overdetermination challenge, thus failing to prevent his opponents from reasonably maintaining that the effects of the mental are always causally overdetermined. Interestingly, this discussion reveals a curious dialectical feature of Kim’s latest response to the overdetermination challenge: if it succeeds, then a new, simpler and more compact version of the exclusion argument is available. While I argue against the consequent of this conditional, thereby also rejecting the antecedent, this dialectical feature should be of interest to philosophers on either side of this debate. (shrink)

This paper discusses von Wright's theory of causation from Explanation and Understanding and Causality and Determinism in contemporary context. I argue that there are two important common points that von Wright's view shares with the version of manipulability currently supported by Woodward: the analysis of causal relations in a system modelled on controlled experiments, and the explanation of manipulability through counterfactuals - with focus on the counterfactual account of unmanipulable causes. These points also mark von Wright's departure from previous (...) action-based theories of causation. Owing to these two features, I argue that, upon classifying different versions of manipulability theories, von Wright's view should be placed closer to the interventionist approach than to the agency theory, where it currently stands. Furthermore, given its relevance in contemporary context, which this paper aims to establish, I claim that von Wright's theory can be employed to solve present problems connected to manipulability approaches to causation. (shrink)

Perceptual learning is characterized by long-term changes in perception as a result of practice or experience. In this paper, I argue that through perceptual learning we can become newly sensitive to basic perceptual features. First, I provide a novel account of basic perceptual features. Then, I argue that evidence from experience-based plasticity suggests that basic perceptual features can be learned. Lastly, I discuss the common scientific and philosophical view that perceptual learning comes in at least four varieties: (...) differentiation, unitization, attentional weighting and stimulus imprinting (Goldstone 1998, Connolly 2019). Becoming newly sensitive to basic perceptual features, I argue, does not fit into any of these categories. This paper’s contribution to the literature is twofold. First, I present a novel view of basic perceptual features which can be used in subsequent theorizing. Second, I show that learning basic perceptual features, since it does not fit into this standardized taxonomy, constitutes an underappreciated form of perceptual learning. This result has important implications for recent discussions in the philosophy of perception and epistemology. (shrink)

I discuss an important feature of the notion of cause in Post. An. 1. 13, 78b13–28, which has been either neglected or misunderstood. Some have treated it as if Aristotle were introducing a false principle about explanation; others have understood the point in terms of coextensiveness of cause and effect. However, none offers a full exegesis of Aristotle's tangled argument or accounts for all of the text's peculiarities. My aim is to disentangle Aristotle's steps to show that he is (...) arguing in favour of a logical requirement for a middle term's being the appropriate cause of its explanandum. Coextensiveness between the middle term and the attribute it explains is advanced as a sine qua non condition of a middle term's being an appropriate or primary cause. This condition is not restricted either to negative causes or to middle terms in second‐figure syllogisms, but ranges over all primary causes qua primary. (shrink)

Abstract In this paper I consider an easier-to-read and improved to a certain extent version of the causal chance-based analysis of counterfactuals that I proposed and argued for in my A Theory of Counterfactuals. Sections 2, 3 and 4 form Part I: In it, I survey the analysis of the core counterfactuals (in which, very roughly, the antecedent is compatible with history prior to it). In section 2 I go through the three main aspects of this analysis, which are (...) the following. First, it is a causal analysis, in that it requires that intermediate events to which the antecedent event is not a cause be preserved in the main truth-condition schema. Second, it highlights the central notion to the semantics of counterfactuals on the account presented here -- the notion of the counterfactual probability of a given counterfactual, which is the probability of the consequent given the following: the antecedent, the prior history, and the preserved intermediate events. Third, it considers the truth conditions for counterfactuals of this sort as consisting in this counterfactual probability being higher than a threshold. In section 3, I re-formulate the analysis of preservational counterfactuals in terms of the notion of being a cause, which ends up being quite compact. In section 4 I illustrate this analysis by showing how it handles two examples that have been considered puzzling – Morgenbesser's counterfactual and Edgington's counterfactual. Sections 5 and on constitute Part II: Its main initial thrust is provided in section 5, where I present the main lines of the extension of the theory from the core counterfactuals (analyzed in part I) to counterfactuals (roughly) whose antecedents are not compatible with their prior history. In this part II, I elaborate on counterfactuals that don't belong to the core, and more specifically on so-called reconstructional counterfactuals (as opposed to the preservational counterfactuals, which constitute the core counterfactual-type). The heart of the analysis is formulated in terms of processes leading to the antecedent (event/state), and more specifically in terms of processes likely to have led to the antecedent, a notion which is analyzed entirely in terms of chance. It covers so-called reconstructional counterfactuals as opposed to the core, so-called preservational counterfactuals, which are analyzed in sections 2 and 3 of part I. The counterfactual probability of such reconstructional counterfactuals is determined via the probability of possible processes leading to the antecedent weighed, primarily and roughly, by the conditional probability of the antecedent given such process: The counterfactual probability is thus, very roughly, a weighted sum for all processes most likely to have led to the antecedent, diverging at a fixed time. In section 6 I explain and elaborate further on the main points in section 5. In section 7 I illustrate the reconstructional analysis. I specify counterfactuals which are so-called process-pointers, since their consequent specifies stages in processes likely to have led to their antecedent. I argue that so-called backtracking counterfactuals are process-pointers counterfactuals, which fit into the reconstructional analysis, and do not call for a separate reading. I then illustrate cases where a speaker unwittingly employs a certain counterfactual while charitably construable as intending to assert (or ‘having in mind’) another. Here I also cover the issue of how to construe what one can take as back-tracking counterfactuals, or counterfactuals of the reconstructional sort, and more specifically, which divergence point they should be taken as alluding to (prior to which the history is held fixed). Some such cases also give rise to what one can take as a dual reading of a counterfactual between preservational and reconstructional readings. Such cases may yield an ambiguity, where in many cases one construal is dominant. In section 8 I illustrate the analysis by applying it to the famous Bizet-Verdi counterfactuals. This detailed analysis of counterfactuals (designed for the indeterministic case) has three main distinctive elements: its being chance-based, its causal aspect, and the use it makes of processes most likely to have led to the antecedent-event. This analysis is couched in a very different conceptual base from, and is an alternative account to, analyses in terms of the standard notion of closeness or distance of possible worlds, which is the main feature of the Stalnaker-Lewis-type analyses of counterfactuals. This notion of closeness or distance plays no role whatsoever in the analysis presented here. (This notion of closeness has been left open by Stalnaker, and to significant extent also by Lewis's second account.) . (shrink)

Aristotelian causal theories incorporate some philosophically important features of the concept of cause, including necessity and essential character. The proposed formalization is restricted to one-place predicates and a finite domain of attributes (without individuals). Semantics is based on a labeled tree structure, with truth defined by means of tree paths. A relatively simple causal prefixing mechanism is defined, by means of which causes of propositions and reasoning with causes are made explicit. The distinction of causal and factual (...) explanation are elaborated, and examples of cyclic and convergent causation are given. Soundness and completeness proofs are sketched. (shrink)

Engineering failure investigations seek to reconstruct the actual causes of major engineering failures. The investigators need to establish the existence of certain past events and the actual causal relationships that these events bear to the failures in question. In this paper, I examine one method for reconstructing the actual causes of failure events, which I call "feature dependence". The basic idea of feature dependence is that some features of an event are informative about the features of its (...) causes; therefore, the investigators can use the features of a known failure event to reconstruct details of its causes. I make explicit the structure of feature dependence and the evidential basis of its key premises, and show how feature dependence works in the investigation of the American Airlines Flight 191 accident. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture (...) tackle the possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (shrink)

Aesthetic non-cognitivists deny that aesthetic statements express genuinely aesthetic beliefs and instead hold that they work primarily to express something non-cognitive, such as attitudes of approval or disapproval, or desire. Non-cognitivists deny that aesthetic statements express aesthetic beliefs because they deny that there are aesthetic features in the world for aesthetic beliefs to represent. Their assumption, shared by scientists and theorists of mind alike, was that language-users possess cognitive mechanisms with which to objectively grasp abstract rules fixed independently of human (...) responses, and that cognizers are thereby capable of grasping rules for the correct application of aesthetic concepts without relying on evaluation or enculturation. However, in this article I use Wittgenstein’s rule-following considerations to argue that psychological theories grounded upon this so-called objective model of rule-following fail to adequately account for concept acquisition and mastery. I argue that this is because linguistic enculturation, and the perceptual learning that’s often involved, influences and enables the mastery of aesthetic concepts. I argue that part of what’s involved in speaking aesthetically is to belong to a cultural practice of making sense of things aesthetically, and that it’s within a socio-linguistic community, and that community’s practices, that such aesthetic sense can be made intelligible. (shrink)

Non-reductive physicalists hold that mental properties are realized by physical properties. The realization relation is typically taken to be a metaphysical necessitation relation. Here, I explore how the metaphysical necessitation feature of realization can be explained by what is known as ‘the subset view’ of realization. The subset view holds that the causal powers that are associated with a realized property are a proper subset of the causal powers that are associated with the realizer property. I argue (...) that the said explanation of the metaphysical necessitation feature requires a careful treatment of the relationship between properties and causal powers. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the (...) proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I (...) argue that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and (...) compared with others. (shrink)

Proponents of cognitive penetration often argue for the thesis on the basis of combined intuitions about categorical perception and perceptual learning. The claim is that beliefs penetrate perceptions in the course of learning to perceive categories. I argue that this "diachronic" penetration thesis is false. In order to substantiate a robust notion of penetration, the beliefs that enable learning must describe the particular ability that subjects learn. However, they cannot do so, since in order to help with (...) learning they must instruct learners to employ previously existing abilities. I argue that a better approach recognizes that we can have sophisticated causal precursors to perceptual learning, but that the learning process itself must operate outside of cognitive influence. (shrink)

In this paper, I motivate skepticism about the causal efficacy of moral properties in two ways. First, I highlight a tension that arises between two claims that moral realists may want to accept. The first claim is that physically indistinguishable things do not differ in any causally efficacious respect. The second claim is that physically indistinguishable things that differ in certain historical respects have different moral properties. The tension arises to the extent to which these different moral properties are (...) supposed to have different effects on people. I will introduce a class of cases in which this tension arises and suggest that the moral properties in these cases have no causal power. I will also question whether there are differences between the moral properties in these cases and moral properties in other cases that do not involve physically indistinguishable things that could make the latter moral properties causally efficacious. The second way that I motivate skepticism consists in pointing out a unique feature of cases in which moral properties are supposed both to supervene on historical properties and to be causally efficacious. These cases allow us to change moral properties with alleged effects while we hold constant the nonmoral candidates for causal contribution to those effects. This feature of these cases is unique because in most other cases the moral properties supervene on the physical properties in the nonmoral candidates, such that we cannot change the former while holding constant the latter. This way of changing moral properties provides empirical grounds for testing their causal efficacy. (shrink)

This study explores a liberatory epistemic virtue that is suitable for good learning as a form of liberating socially situated epistemic agents toward ideal virtuousness. First, I demonstrate that the weak neutralization of epistemically bad stereotypes is an end of good learning. Second, I argue that weak neutralization represents a liberatory epistemic virtue, the value of which derives from liberating us as socially situated learners from epistemic blindness to epistemic freedom. Third, I explicate two distinct forms of epistemic (...) transformation: constitutive and causal epistemic transformation. I argue that compared with the ideal conception of epistemic virtue, constitutive epistemic transformation that involves good learning has a transcendent value in light of agents constantly renewing their default epistemic status toward ideal virtuousness. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to (...) explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

This paper responds to critical commentaries on my book, Perceiving Reality (OUP, 2012), by Laura Guerrero, Matthew MacKenzie, and Anand Vaidya. Guerrero focuses on the metaphysics of causation, and its role in the broader question of whether the ‘two truths’ framework of Buddhist philosophy can be reconciled with the claim that science provides the best account of our experienced world. MacKenzie pursues two related questions: (i) Is reflexive awareness (svasaṃvedana) identical with the subjective pole of a dual-aspect cognition or are (...) there alternative, perhaps better, ways of understanding this self-intimating character of mental states? (ii) Is perception constitutively intentional or is it representational? Vaidya argues that, in so far as Husserlian phenomenology and Buddhism differ in terms of their fundamental ontological commit- ments, they must be incompatible, thus rendering any cross-cultural philosophical project that seeks their rapprochement tenuous. One of my aims in Perceiving Reality is to show how accounts of perception informed by metaphysical realism can be problematic on both metaphysical and epistemological grounds, especially when relying on conceptions of consciousness that ignore its properly phenomenological features. (shrink)

This paper examines Dharmakīrti's arguments against Cārvāka physicalism in the Pramāṇasiddhi chapter of his magnum opus, the Pramāṇavārttika, with a focus on classical Indian philosophical attempts to address the mind-body problem. The key issue concerns the relation between cognition and the body, and the role this relation plays in causal-explanatory accounts of consciousness and cognition. Drawing on contemporary debates in philosophy of mind about embodiment and the significance of borderline states of consciousness, the paper proposes a philosophical reconstruction that (...) builds on two important features of the Buddhist account: an expanded conception of causality and a robust account of phenomenal content. (shrink)

Suppose that you are at a live jazz show. The drummer begins a solo. You see the cymbal jolt and you hear the clang. But in addition seeing the cymbal jolt and hearing the clang, you are also aware that the jolt and the clang are part of the same event. Casey O’Callaghan (forthcoming) calls this awareness “intermodal feature binding awareness.” Psychologists have long assumed that multimodal perceptions such as this one are the result of a subpersonal feature (...) binding mechanism (see Vatakis and Spence, 2007, Kubovy and Schutz, 2010, Pourtois et al., 2000, and Navarra et al., 2012). I present new evidence against this. I argue that there is no automatic feature binding mechanism that couples features like the jolt and the clang together. Instead, when you experience the jolt and the clang as part of the same event, this is the result of an associative learning process. The cymbal’s jolt and the clang are best understood as a single learned perceptual unit, rather than as automatically bound. I outline the specific learning process in perception called “unitization,” whereby we come to “chunk” the world into multimodal units. Unitization has never before been applied to multimodal cases. Yet I argue that this learning process can do the same work that intermodal binding would do, and that this issue has important philosophical implications. Specifically, whether we take multimodal cases to involve a binding mechanism or an associative process will have impact on philosophical issues from Molyneux’s question to the question of how active or passive we consider perception to be. (shrink)

This chapter begins with a discussion of the significance of studies of aspects of tool use in understanding causal cognition. It argues that tool use studies reveal the most basic type or causal understanding being put to use, in a way that studies that focus on learning statistical relationships between cause and effect or studies of perceptual causation do not. An overview of the subsequent chapters is also presented.

This paper derives from a broader project dealing with the notion of causal order. I use this term to signify two kinds of parts-whole dependence: Orderly systems have rich, decomposable, internal structure; specifically, parts play differential roles, and interactions are primarily local. Disorderly systems, in contrast, have a homogeneous internal structure, such that differences among parts and organizational features are less important. Orderliness, I suggest, marks one key difference between individuals and collectives. My focus here will be the connection (...) between order and robustness, i.e. functional resilience in the face of internal or environmental perturbations. I distinguish three varieties of robustness. Ordered robustness is grounded in the system’s specific organizational pattern. In contrast, disorderly robustness stems from the aggregate outcome of many similar parts. In between, we find semi-ordered robustness, wherein a messy ensemble of elements is subjected to a selection or stabilization mechanism. I give brief characterizations of each category, discuss examples and remark on the connection between the order/disorder axis and the notions of individual versus collective. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert (...) the impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to classify lemons (...) based on their quality, evaluate the model's performance, compare it to traditional machine learning approaches, and identify key factors that affect the model's performance. The dataset used in the study consists of approximately 2533 images of lemons and an empty surface, divided into training, test, and validation data, and includes images of both good and bad quality lemons under different lighting conditions. The deep learning model achieved an accuracy of 99.74% on the test dataset and outperformed traditional machine learning approaches. The developed model has the potential to improve efficiency and accuracy in lemon quality control in the agriculture and food industry. (shrink)

In quantum mechanics, quantum nonseparability is at the core of philosophical debates regarding its meaning. In the context of the process matrix formalism, causal nonseparability characterises quantum processes (connecting the inputs and outputs of different local quantum operations) that are incompatible with any definite causal structure among interacting parties. One talks about indefinite causal orders. A famous example of causally nonseparable processes is called the quantum switch (QS). It is extensively studied in the literature in virtue of (...) its simple architecture and its various implementations in laboratories.The present work will discuss the possible interpretations of the QS's causal nonseparability under the assumption that causal nonseparability is seen as pointing towards novel objective features of nature.It was first defended that a scientific realist approach towards the process matrix formalism, which is an operational theory generalising quantum mechanics, was as much legitimate as any possible antirealist reading, contrary to certain views found in the literature. The reason is that operational formalisms are ontologically and epistemically neutral.From there, the theoretical concepts of interest, namely causal nonseparability and its model-independent counterpart called noncausality, were analysed in more details, in order to highlight in what sense they are distinct from the standard notions of quantum nonseparability and nonlocality in quantum mechanics.The discussion then focused on noncausality. It was argued that noncausality has a strong connection with a notion of temporal nonlocality. In the same way that Bell nonlocality is given different underlying explanations depending on the details of the chosen quantum mechanics' account, noncausality pointing towards temporal nonlocality can be given a variety of underlying descriptions depending on the exact way to interpret the process matrix formalism.The last chapter focused precisely on this particular point, namely on the various ways to understand process matrices and causal nonseparability in a realist context. In order to explore the potential impact of causal nonseparability on spacetime,we shifted from the notion of (indefinite) causal structure to (indefinite) spatiotemporal ones. This shift was allowed under a set of reasonable assumptions regarding the properties of a physical spacetime manifold and the connection between an operational and relativistic notions of causal relations.While different readings were suggested for indefiniteness of spatiotemporal relations, we insisted in particular on an objective understanding appealing to the concept of metaphysical indeterminacy. It was argued that such an approach could prove useful in a more general theoretical context such as quantum gravity, while being already partly supported in standard quantum mechanics. (shrink)

In her book Specifications Grading, Linda B. Nilson advocates for a grading regimen she claims will save faculty time, increase student motivation, and improve the quality and rigor of student work. If she is right, there is a strong case for many faculty to adopt some version of the system she recommends. In this paper, we argue that she is mostly right and recommend that faculty move away from traditional grading. We begin by rehearsing the central features of specifications grading (...) and providing two examples of how to implement it in philosophy classes. In light of the examples, we argue that specifications grading fulfills two of Nilson’s central desiderata but not the third. Since specifications grading generates two benefits that when combined increase student learning, without adding or increasing burdens, we conclude that student learning increases when courses are revised to include aspects of specifications grading. (shrink)

The goal of this paper is to argue that there is indeterminacy in causation. I present three types of cases in which it is indeterminate whether an event c caused another event e: (1) cases of absence causation recently discussed by Bernstein and by Swanson, (2) cases leading to Sorites paradoxes for causation, and (3) cases where c and e occur in certain indeterministic causal structures and it is therefore indeterminate whether there is a causal relation between them. (...) These cases, I argue, provide strong evidence that indeterminacy is an important general feature of the causal relation and that philosophical theorizing about causation should take this observation into account. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do (...) something? And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (...) (ASD). This problem has to do with the individual’s assessment of the reliability of messages that are passed between people as common knowledge emerges. The proposal is developed on the background of our own empirical studies and outlines different ways common knowledge might be comprised. We discuss what these issues may tell us about ASD, about the relation between social and non-social cognition, about social objects, and about the dynamics of social networks. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of (...) the proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

Jacques Monod (1971) argued that certain molecular processes rely critically on the property of chemical arbitrariness, which he claimed allows those processes to “transcend the laws of chemistry”. It seems natural, as some philosophers have done, to interpret this in modal terms: a biological relationship is chemically arbitrary if it is possible, within the constraints of chemical “law”, for that relationship to have been otherwise than it is. But while modality is certainly important for understanding chemical arbitrariness, understanding its biological (...) role also requires an account of the concrete causal-functional features that distinguish arbitrary from non-arbitrary phenomena. In this paper I elaborate on this under-emphasised aspect by offering a general account of these features: arbitrary relations are instantiated by mechanisms that involve molecular adapters, which causally couple two properties or processes which would otherwise be uncorrelated. Additionally, adapters work by acting as intermediate rather than cooperating causes. (shrink)

The review first discusses componential explanations of automaticity, which specify non/automaticity features (e.g., un/controlled, un/conscious, non/efficient, fast/slow) and their interrelations. Reframing these features as factors that influence processes (e.g., goals, attention, and time) broadens the range of factors that can be considered (e.g., adding stimulus intensity and representational quality). The evidence reviewed challenges the view of a perfect coherence among goals, attention, and consciousness, and supports the alternative view that (a) these and other factors influence the quality of representations in (...) an additive way (e.g., little time can be compensated by extra attention or extra stimulus intensity) and that (b) a first threshold of this quality is required for unconscious processing and a second threshold for conscious processing. The review closes with a discussion of causal explanations of automaticity, which specify factors involved in automatization such as repetition and complexity, and a discussion of mechanistic explanations, which specify the low-level processes underlying automatization. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

The article suggests an unsupervised model for featuring student’s learning patterns in an open-ended learning scenario. The article proceeds by generating powerful metrics to characterize the learner’s behavior and efficacy through Coherence investigation. Then, the selected features are combined through a Gaussian Hybrid Fuzzy Clustering (GHFC) that categorizes students based on their learning patterns. The proposed system features the essential behaviors of every group and associate the behaviors with ability to develop right models to gauge the (...) class='Hi'>learning gains between pre- and post-test scores. Also, this article explains the deployment of behavior characterization to be developed as a adaptive framework of learning behaviors. (shrink)