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)

In this wide-ranging conversation, Berry and Galloway explore the implications of undertaking media theoretical work for critiquing the digital in a time when networks proliferate and, as Galloway claims, we need to ‘forget Deleuze’. Through the lens of Galloway’s new book, Laruelle: Against the Digital, the potential of a ‘non-philosophy’ for media is probed. From the import of the allegorical method from excommunication to the question of networks, they discuss Galloway’s recent work and reflect on the (...) implications of computation for media theory, thinking about media objects, and critical theory. (shrink)

This volume explores the problems and prospects attending evangelical engagement with natural law as a key feature for political thought. Engaging theology, philosophy, political theory and biblical studies, many contributors are optimistic about the prospects of evangelical re-appropriation of natural law, but note ways in which evangelical commitments might lend distinctive shape to this engagement.

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that (...) are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

We examine children's cognitive skills and cultural representations in naturally occurring discourse, integrating theoretical perspectives from psychology, cognitive anthropology, and sociolinguistics. We focus on two interactional events recorded in our 10-year study of children's language socialization in Kwara'ae involving the same child at ages 2 and 4 years interacting with an older child and an adult, respectively, around routine tasks. In both cases a potentially serious cultural anomaly that challenges the children's own constructions of cultural models tests their strategic creativity (...) and agency in negotiating an outcome. The resulting discourse reveals how they are construing and modeling culture in a time of rapid social change. (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 article discusses Charles Parsons’ conception of mathematical intuition. Intuition, for Parsons, involves seeing-as: in seeing the sequences I I I and I I I as the same type, one intuits the type. The type is abstract, but intuiting the type is supposed to be epistemically analogous to ordinary perception of physical objects. And some non-trivial mathematical knowledge is supposed to be intuitable in this way, again in a way analogous to ordinary perceptual knowledge. In particular, the successor axioms are (...) supposed to be knowable intuitively.This conception has the resources to respond to some familiar objections to mathematical intuition. But the analogy to ordinary perception is weaker than it looks, and the warrant provided for non-trivial mathematical beliefs by intuition of this sort is weak too weak, perhaps, to yield any mathematical knowledge. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

This is a discussion of L. Jonathan Cohen’s argument against the possibility that empirical psychological research might show that lay deductive competence is inconsistent. I argue that, within the framework Cohen provides, the consistency of lay deductive practice is indeterminate.

Many have attempted to respond to arguments for the incompatibility of freedom with divine foreknowledge by claiming that God’s beliefs about the future are explained by what the world is like at that future time. We argue that this response adequately advances the discussion only if the theist is able to articulate a model of foreknowledge that is both clearly possible and compatible with freedom. We investigate various models the theist might articulate and argue that all of these models fail.

When The Absurd Hero in American Fiction was first released in 1966, Granville Hicks praised it in a lead article for the Saturday Review as a sensitive and definitive study of a new trend in postwar American literature. In the years that followed, David Galloway’s analysis of the writings of John Updike, William Styron, Saul Bellow, and J. D. Salinger became a standard critical work, an indispensable tool for readers concerned with contemporary American literature. The New York Times (...) described the book as “a seminal study of the modern literary imagination." David Galloway, himself an established novelist, later extensively revised The Absurd Hero to include authoritative discussions of more than a dozen novels which had appeared since the first revised edition was released in 1970. Among them are John Updike’s Couples, Rabbit Redux, and The Coup; William Styron’s The Confessions of Nat Turner and Sophie’s Choice; and Saul Bellow’s Mr. Sammler’s Planet and Humboldt’s Gift. Through detailed analyses of these works, Galloway demonstrates the continuing relevance of his own provocative concept of the absurd hero and provides important insights into the literary achievements of four of America’s most influential postwar novelists. (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)

In a previous article, we projected the future accumulation of profiles belonging to deceased users on Facebook. We concluded that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, on the other hand, this number will exceed 4.9 billion. Although these findings provided an important first step, one network alone remains insufficient to establish a quantitative foundation for further macro-level analysis (...) of the phenomenon of online death. Facebook is but one social media platform among many, and hardly the most representative in terms of their policy on deceased users. In this study, we use the same methodology to develop a complementary analysis of projected mortality on Instagram. Our models indicate that somewhere between 767 million and 4.2 billion Instagram users will die between 2019 and 2100, depending on the network’s future growth rate. Although the number of deceased Instagram profiles will likely be fewer than those on Facebook, we argue that they are nonetheless part of a shared digital cultural heritage, and should hence be curated with careful consideration. (shrink)

We explore the implications of a hierarchical structure, consisting of (a) the higher order dimensions of nonspecific Positive Activation and Negative Activation and (b) multiple specific negative affects (e.g., fear, sadness, and anger) and positive affects (e.g., joviality, self-assurance, and attentiveness) at the lower level. Emotional blends of the same valence (e.g., simultaneously experiencing both fear and sadness) are an essential part of this structure and form the basis of the higher order Negative and Positive Activation dimensions. Mixed cross-valence emotions (...) (e.g., feeling both nervous and alert) are not central to this hierarchical scheme but are compatible with it. We examine the frequency of pure emotional states, same-valence emotional blends, and cross-valence mixed emotions in a large momentary mood sample. (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence, a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence, we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of (...) necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks. (shrink)

The Approved Mental Health Professional (AMHP) has a pivotal role in a decision to detain an individual under the Mental Health Act 1983. This article is a reflective account demonstrating how a solution-focused approach can enable an AMHP to engage constructively with the person being assessed and apply the values of anti-oppressive practice. Using a solution-focused approach enables a creative and empowering discussion of risk and may lead to a less restrictive outcome. These techniques should be part of the training (...) and ongoing education of AMHPs. (shrink)

Living organisms are the ultimate survivalists, having evolved phenotypes with unprecedented adaptability, ingenuity, resourcefulness, and versatility compared to human technology. To harness these properties, functional descriptions and design principles from all sources of biodiversity information must be collated − including the hundreds of thousands of possible survival features manifest in natural history museum collections, which represent 12% of total global biodiversity. This requires a consortium of expert biologists from a range of disciplines to convert the observations, data, and hypotheses into (...) the language of engineering. We hope to unite multidisciplinary biologists and natural history museum scientists to maximize the coverage of observations, descriptions, and hypotheses relating to adaptation and function across biodiversity, to make it technologically useful. This is to be achieved by developments in meta‐ taxonomic classification, phylogenetics, systematics, biological materials research, structure and morphological characterizations, and ecological data gathering from the collections − the aim being to identify and catalogue features essential for good biomimetic design. (shrink)

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...) challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: _the Switch_, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; _the Ladder_, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and _the Matrix_, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks. (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors (...) with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks. (shrink)

On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).

Several competing narratives drive the contemporary AI ethics discourse. At the two extremes are sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better technology; and sociotechnical skepticism, which highlights the unacceptable risks AI systems pose. While both narratives have their merits, they are ultimately reductive and limiting. As a constructive synthesis, we introduce and defend sociotechnical pragmatism—a narrative that emphasizes the central role of context and human agency in designing and (...) evaluating emerging technologies. In doing so, we offer two novel contributions. First, we demonstrate how ethical and epistemological considerations are intertwined in the AI ethics discourse by tracing the dialectical interplay between dogmatic and skeptical narratives across disciplines. Second, we show through examples how sociotechnical pragmatism does more to promote fair and transparent AI than dogmatic or skeptical alternatives. By spelling out the assumptions that underpin sociotechnical pragmatism, we articulate a robust stance for policymakers and scholars who seek to enable societies to reap the benefits of AI while managing the associated risks through feasible, effective, and proportionate governance. (shrink)

Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on (...) animating graphical head models. Here, we present a principal component analysis -based active appearance model for capturing patterns of spatiotemporal variation in videos of natural dynamic facial behaviours. We demonstrate how this technique can be applied to generate dynamic anti-caricatures of biological motion patterns in facial behaviours. This technique could be extended to caricaturing other facial dimensions, or to more general analyses of spatiotemporal variations in dynamic faces. (shrink)

Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we show how information (...) and communication technologies enhance the reliability, scalability, and connectivity of crowdsourced e-research, giving online citizen science projects powerful epistemic advantages over more traditional modes of scientific investigation. These results highlight the essential role played by technologically mediated social interaction in contemporary knowledge production. We conclude by calling for an explicitly sociotechnical turn in the philosophy of science that combines insights from statistics and logic to analyse the latest developments in scientific research. (shrink)