David Danks

Many social media platforms enable (nearly) anyone to post (nearly) anything. One clear downside of this permissiveness is that many people appear bad at determining who to trust online. Hacks, quacks, climate change deniers, vaccine skeptics, and election deniers have all gained massive followings in these free markets of ideas, and many of their followers seem to genuinely trust them. At the same time, there are many cases in which people seem to reliably determine who to trust online. Consider, for example, Do It Yourself (DIY) content about how to play guitar, bake, fix one’s plumbing, or repair one’s car. For these topics, those who have the largest accounts and the most popular content typically possess significant expertise. That is, social media users seem to reliably pick out DIY experts. We thus have a puzzle: why are social media users seemingly competent at identifying DIY experts, but not climate science or vaccine experts? In what follows, we solve this puzzle. We begin by identifying a novel wisdom of the crowds phenomenon: specifically we argue that the crowd (in combination with social media search and recommendation algorithms) reliably picks out DIY experts and serves as a credentialing institution for DIY content. Next, we argue that (a) there are five epistemic factors that determine whether the crowd can succeed at recognizing experts on social media platforms, and (b) while many of those factors are satisfied to a sufficiently high degree by DIY content, they are mostly lacking for content about climate change or vaccines.

A standard platitude about the function of causal knowledge or theories is that they are valuable because they support prediction, explanation, and control. Knowledge of predator-prey relations enables us to predict future animal populations, as well as design policies or interventions that help influence those populations. If we understand the underlying biochemical mechanisms of some disease, then we can predict who is at risk for it, explain why it produces particular symptoms, and develop interventions to try to reduce its prevalence or the symptom severity. Of course, there are many situations in which one has, for practical reasons, only some of these desiderata; for example, control might be infeasible for technical or ethical reasons. But these remain, for many researchers, the ideal for why causal knowledge is a valuable end of scientific inquiry, including biological inquiry. There are, however, certain types of systems—in particular, chaotic systems—in which it appears that these ends are unattainable, and these systems appear to be widespread in the biological domain, broadly construed. In this paper, we will show why it is natural to think that causal models of chaotic systems cannot satisfy any of the three functions. But we will also show why this natural thought is wrong: we can have usable causal knowledge about even chaotic systems. Moreover, the ways in which we can have such knowledge lead us naturally to rethink a standard understanding of how causal learning and modeling proceed. In particular, just as we often must find the appropriate variables for a causal system, we also must determine the proper level or granularity of description for the dynamics of that system.

Pragmatist accounts of science are often thought to necessarily imply pluralism about science. This chapter examines the common belief that a pragmatist approach must lead to the conclusion that scientific theories cannot be unified in substantive ways. I first examine the grounds for this belief, and argue that the prima facie connection between pragmatism and (dis)unification holds only if we adopt a narrow view of the conditions under which scientific theories are actually unifiable. In particular, I contend that this apparent connection presupposes that unification requires intertheoretic reduction, but there are many other intertheoretic relations. If we broaden our understanding to allow for unifications based on mutual satisfaction of intertheoretic constraints, then the connection between pragmatism and disunification no longer holds. As a result, the pragmatist about science can have all of the scientific unification that they need, even if that unification falls short of the realist’s demands.

Kinetic warfare involves tanks, bullets, and other hardware, but everyone recognizes that understandings of it must involve warfighters from general staff to boots on the ground. In contrast, most discussions of cyberwarfare—its strategy, impacts, ethics—focus on the machines, systems, and data, while largely ignoring the human element. This chapter explores a range of conceptual and ethical issues that arise when we focus on the cognitive constraints, biases, and heuristics of human agents in four different roles: developers of a cyberaction; target of that cyberaction; defender against some cyberaction; and third-party observers, whether neutral nations, or even the public within a nation engaged in cyberactions. Cyberwarfare is conducted with machines, but humans are the developers, targets, and defenders of cyberactions. Thus, a full understanding of the ethical dimensions of cyberwarfare must incorporate the human actors with all of their cognitive, conceptual, and cultural biases, tendencies, and foibles.

Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is considered at the associative (probabilistic) level. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rational choice using associative information: maximize expected utility. There is an ongoing debate around the origin of probabilities involved in such calculation. In this work, we will show how the probabilities for decision-making can be grounded in causal models by considering decision problems in which the available actions and consequences are causally connected. In this setting, actions are regarded as an intervention over a causal model. Then, we extend a previous causal decision-making result, which relies on a known causal model, to the case in which the causal mechanism that controls some environment is unknown to a rational decision-maker. In this way, action-outcome probabilities can be grounded in causal models both in known and unknown cases. Finally, as an application, we extend the well-known concept of Nash Equilibrium to the case in which the players of a strategic game consider causal information.

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.

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.

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data.

When thinking about designing social media platforms, we often focus on factors such as usability, functionality, aesthetics, ethics, and so forth. Epistemic considerations have rarely been given the same level of attention in design discussions. This paper aims to rectify this neglect. We begin by arguing that there are epistemic norms that govern environments, including social media environments. Next, we provide a framework for applying these norms to the question of platform design. We then apply this framework to the real-world case of long-form informational content platforms. We argue that many current long-form informational content platforms are epistemically unhealthy. The good news? We provide concrete advice on how to take steps toward improving their health! Specifically, we argue that they should change how they verify and authenticate content creators and how this information is displayed to content consumers. We conclude by connecting this guidance to broader issues about the epistemic health of platforms.

Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 18 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy.

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.

There are numerous routes for scientific discovery, many of which involve the use of information from other scientific theories. In particular, searching for possible reductions is widely recognized as one guiding principle for scientific discovery or innovation. However, reduction is only one kind of intertheoretic relation; scientific theories, claims, and proposals can be related in more, and more complex, ways. This chapter proposes that much scientific discovery proceeds through the use of constraints implied by those intertheoretic relationships. The resulting framework is significantly more general than the common reduction-centric focus. As a result, it can explain more prosaic, everyday cases of scientific discovery, as well as scientists’ opportunistic use of many different kinds of scientific information. I illustrate the framework using three case studies from cognitive science, and conclude by exploring the potential limits of analyses of scientific discovery via constraints.

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...