David Danks

We argue that there is a large class of questions—specifically questions about how to epistemically evaluate environments that currently available epistemic theories are not well-suited for answering, precisely because these questions are not about the epistemic state of particular agents or groups. For example, if we critique Facebook for being conducive to the spread of misinformation, then we are not thereby critiquing Facebook for being irrational, or lacking knowledge, or failing to testify truthfully. Instead, we are saying something about the social media environment. In this paper, we first propose that a new branch of epistemology–Environmental Epistemology–is needed to address these questions. We argue that environments can be proper objects of epistemic evaluation, and that there are genuine epistemic norms that govern environments. We then provide a positive account of these norms and conclude by considering how recognition of these norms may require us to rethink longstanding epistemic debates.

Normative moral theories are frequently invoked to serve one of two distinct purposes: (1) explicate a criterion of rightness, or (2) provide an ethical decision-making procedure. Although a criterion of rightness provides a valuable theoretical ideal, proposed criteria rarely can be (nor are they intended to be) directly translated into a feasible decision-making procedure. This paper applies the computational framework of bounded rationality to moral decision-making to ask: how ought a bounded human agent make ethical decisions? We suggest agents ought to follow moral maxims: principles that approximate rightness in many situations, but that can be overridden in specific, precisely describable circumstances. While this intuitive idea has been proposed many times before, we provide a precise model of how maxim consequentialism functions as an approximation to an act-consequentialist criterion of rightness, while maintaining the flexibility and defeasibility that has eluded most forms of rule consequentialism. Furthermore, while our overarching aim is to propose a new normative standard of moral decision-making, we demonstrate how maxim consequentialism can also function as a descriptive account of human behavior. We conclude by noting that different criteria of rightness may lead to different maxim-based ethics.

Causal models provide a framework for precisely representing complex causal structures, where specific models can be used to efficiently predict, infer, and explain the world. At the same time, we often do not know the full causal structure a priori and so must learn it from data using a causal model search algorithm. This chapter provides a general overview of causal models and their uses, with a particular focus on causal graphical models (the most commonly used causal modeling framework) and methods for learning such models from observational and experimental data. The chapter concludes with two examples of productive causal search and modeling in experimental philosophy.

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns.

Artificial Intelligence (AI) offers numerous opportunities to improve military Intelligence, Surveillance, and Reconnaissance operations. And, modern militaries recognize the strategic value of reducing civilian harm. Grounded in these two assertions we focus on the transformative potential that AI ISR systems have for improving the respect for and protection of humanitarian relief operations. Specifically, we propose that establishing an interface for humanitarian organizations to military AI ISR systems can improve the current state of ad-hoc humanitarian notification systems, which are notoriously unreliable and ineffective for both parties to conflict and humanitarian organizations. We argue that such an interface can improve military awareness and understanding while also ensuring that states better satisfy their international humanitarian law obligations to respect and protect humanitarian relief personnel.

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation.

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed.

Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major thread of empirical research on our concept of causation has investigated the role that normative considerations play in causal judgments. These experimental results suggest that philosophical accounts of our concept of causation should take a broader view of what might be relevant. For both lines of research, we describe some of the significant experiments and outline key philosophical morals that have been drawn, all while pointing out various limitations. We conclude by considering other kinds of empirical research that should be philosophically interesting for those studying the nature of causation and our concept of causation. In particular, we point towards the need for philosophical research about causal perception, causal reasoning, and causal learning, as well as ways in which this research could play a role in prescriptive metaphysics.

Experimental philosophy is often presented as a new movement that avoids many of the difficulties that face traditional philosophy. This article distinguishes two views of experimental philosophy: a narrow view in which philosophers conduct empirical investigations of intuitions, and a broad view which says that experimental philosophy is just the colocation in the same body of (i) philosophical naturalism and (ii) the actual practice of cognitive science. These two positions are rarely clearly distinguished in the literature about experimental philosophy, both pro and con. The article argues, first, that the broader view is the only plausible one; discussions of experimental philosophy should recognize that the narrow view is a caricature of experimental philosophy as it is currently done. It then shows both how objections to experimental philosophy are transformed and how positive recommendations can be provided by adopting a broad conception of experimental philosophy

There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular type of causal cognition; the extant data do not warrant any wide-ranging conclusions about the pervasiveness of normative considerations in causal cognition. Of course, almost all empirical investigations involve some manner of ampliative inference, and so we provide novel empirical results demonstrating that there are types of causal cognition that do not seem to be influenced by moral considerations

This book explores new findings on the long-neglected topic of theory construction and discovery, and challenges the orthodox, current division of scientific development into discrete stages: the stage of generation of new hypotheses; the stage of collection of relevant data; the stage of justification of possible theories; and the final stage of selection from among equally confirmed theories. The chapters, written by leading researchers, offer an interdisciplinary perspective on various aspects of the processes by which theories rationally should, and descriptively are, built. They address issues such as the role of problem-solving and heuristic reasoning in theory-building; how inferences and models shape the pursuit of scientific knowledge; the relation between problem-solving and scientific discovery; the relative values of the syntactic, semantic, and pragmatic view of theories in understanding theory construction; and the relation between ampliative inferences, heuristic reasoning, and models as a means for building new theories and knowledge. Through detailed arguments and examinations, the volume collectively challenges the orthodox view’s main tenets by characterizing the ways in which the different “stages” are logically, temporally, and psychologically intertwined. As a group, the chapters provide several attempts to answer long-standing questions about the possibility of a unified conceptual framework for building theories and formulating hypotheses.

ABSTRACTAutonomous weapons systems pose many challenges in complex battlefield environments. Previous discussions of them have largely focused on technological or policy issues. In contrast, we focus here on the challenge of trust in an AWS. One type of human trust depends only on judgments about the predictability or reliability of the trustee, and so are suitable for all manner of artifacts. However, AWSs that are worthy of the descriptor “autonomous” will not exhibit the required strong predictability in the complex, changing contexts of war. Instead, warfighters need to develop deeper, interpersonal trust that is grounded in understanding the values, beliefs, and dispositions of the AWS. Current acquisition, training, and deployment processes preclude the development of such trust, and so there are currently no routes for a warfighter to develop trust in an AWS. We thus survey three possible changes to current practices in order to facilitate the type of deep trust that is required for appropri...

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments.

Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger “pipeline” within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure.

Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system’s behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system’s causal timescale. Second, missing variables have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of “raw” evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight.

We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions . The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of research on the normative question: the framework of Bayesian networks, and algorithms that provably extract maximal causal information from observations and interventions in the environment. I then show that we can use the framework of Bayesian networks to represent the three major descriptive theories of causal judgment. The three theories are not directly comparable, however, since one of them is algorithmic, while the other two are computational. Therefore, I provide a fully general characterization of the Rescorla-Wagner model. ;With this computational description of the Rescorla-Wagner model in hand, we can compare all three of the theories against the known experimental data. Unfortunately, the experimental data has all been obtained using one particular experimental methodology, which contains serious problems. In addition to critiquing the currently dominant methodology, I provide a "proof of concept" of a different methodology that does not face these problems. ;More importantly, the psychological theories all focus on parameter estimation in a fixed causal structure, rather than learning the actual structure. Therefore, I consider the experimental question: can people learn complex causal structures? The data from those experiments reveal that people appear to use a wide array of learning strategies. The epistemology of our causal beliefs is substantially more complex than has previously been thought. ;Finally, at the end of this dissertation, I step back and examine the implications of these experimental results for descriptive theories of causal judgment. I also point towards several open research questions, both descriptive and normative, raised by the work in this dissertation

In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation and causation,1 and accordingly make different inferences from passive observation than from interventions. A few statisticians aside,” so do most of us. To square these views with the demands of computability, increasing numbers of psychologists and others have embraced a particular formalization, causal Bayes nets, as an account of human reasoning about and to causal connections.111 Such structures can be used by rational agents, including humans in so far as they are rational, to have degrees of belief in various conceptual contents, which they use to reason to expectations, which are realized or defeated by sensory inputs, which cause them to change their degrees of belief in other contents in accord with Bayes Rule, or some generalization of it. How is all of this supposed to be carried out? l. Representing Causal Structures The causal Bayes net framework adopted by a growing number of psychologists goes like this: Our representations of causal relations are captured in a graphical causal.

Debates about the moral permissibility of mass electronic surveillance often turn on whether consequentialist considerations legitimately trump relevant deontological rights and principles. In order to establish such overriding consequences, many proponents of mass surveillance employ a modern analogue of Pascal’s wager: they contend that the consequences of no surveillance are so severe that any probability of such outcomes legitimates the abrogation of the relevant rights. In this paper, I briefly review Pascal’s original wager about whether to live a pious life, including two classes of objections that were almost immediately leveled against his argument. I then show that analogues of those objections apply straightforwardly to the modern versions of Pascal’s wager. Mass electronic surveillance might be ethically permissible or even obligatory in some circumstances, but the details matter in ways that are systematically ignored or assumed away in a Pascal’s wager-type argument. Careful consideration of the complexities of our modern situation is required to decide whether consequentialist considerations override deontological principles in this domain

Models based on causal capacities, or independent causal influences/mechanisms, are widespread in the sciences. This paper develops a natural mathematical framework for representing such capacities by extending and generalizing previous results in cognitive psychology and machine learning, based on observations and arguments from prior philosophical debates. In addition to its substantial generality, the resulting framework provides a theoretical unification of the widely-used noisy-OR/AND and linear models, thereby showing how they are complementary rather than competing. This unification helps to explain many of the shared cognitive and mathematical properties of those models.

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist and rational paradigms in causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment