John Symons

Ethically significant consequences of artificially intelligent artifacts will stem from their effects on existing social relations. Artifacts will serve in a variety of socially important roles—as personal companions, in the service of elderly and infirm people, in commercial, educational, and other socially sensitive contexts. The inevitable disruptions that these technologies will cause to social norms, institutions, and communities warrant careful consideration. As we begin to assess these effects, reflection on degrees and kinds of social agency will be required to make properly informed decisions concerning the deployment of artificially intelligent artifacts in important settings. The social agency of these systems is unlike a human social agency, and this paper provides a methodological framework that is more suited for inquiry into artificial social agents than conventional philosophical treatments of the concept of agency. Separate aspects and dimensions of agency can be studied without assuming that agency must always look like adult human agency. This revised approach to the agency of artifacts is conducive to progress in the topics studied by AI ethics.

This paper introduces the _Global Philosophy_ symposium on Giuseppe Primiero’s book _On the Foundations of Computing_ (2020). The collection gathers commentaries and responses of the author with the aim of engaging with some open questions in the philosophy of computer science. Firstly, this paper introduces the central themes addressed in Primiero’s book; secondly, it highlights some of the main critiques from commentators in order to, finally, pinpoint some conceptual challenges indicating future directions for the philosophy of computer science.

Procedural justifications of democracy emphasize inclusiveness and respect and by doing so come into conflict with instrumental justifications that depend on voters’ competence. This conflict raises questions about jury theorems and makes their standing in democratic theory contested. We show that a type of no-regret learning called meta-induction can help to satisfy the competence assumption without excluding voters or diverse opinion leaders on an a priori basis. Meta-induction assigns weights to opinion leaders based on their past predictive performance to determine the level of their inclusion in recommendations for voters. The weighting minimizes the difference between the performance of meta-induction and the best opinion leader in hindsight. The difference represents the regret of meta-induction whose minimization ensures that the recommendations are optimal in supporting voters’ competence. Meta-induction has optimal truth-tracking properties that support voters’ competence even if it is targeted by mis/disinformation and should be considered a tool for supporting democracy in hyper-plurality.

This paper argues that open-mindedness is a corrective virtue. It serves as a corrective to the epistemic vice of confirmation bias. Specifically, open-mindedness is the epistemically virtuous disposition to resist the negative effects of confirmation bias on our ability to reason well and to evaluate evidence and arguments. As part of the defense and presentation of our account, we explore four discussions of open-mindedness in the recent literature. All four approaches have strengths and shed light on aspects of the virtue of open-mindedness. Each mentions various symptoms of confirmation bias and some explore aspects of the corrective role of open-mindedness. However, ours is the first to explicitly identify open-mindedness as a corrective virtue to the specific epistemic vice of confirmation bias. We show how the corrective account also permits a response to the concern that open-mindedness might not actually count as a virtue.

What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.

Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data science technologies. However, we argue that epistemic injustices can be distinguished conceptually from more familiar kinds of harm. We argue that epistemic harms are morally relevant even in cases where those who suffer them are unharmed in other ways. Via a series of examples from the criminal justice system, workplace hierarchies, and educational contexts we explain the kinds of epistemic injustice that can result from common uses of data science technologies.

While ontic structural realism (OSR) has been a central topic in contemporary philosophy of science, the relation between OSR and the concept of emergence has received little attention. We will argue that OSR is fully compatible with emergentism. The denial of ontological emergence requires additional assumptions that, strictly speaking, go beyond OSR. We call these _physicalist closure assumptions._ We will explain these assumptions and show that they are independent of the central commitments of OSR and inconsistent with its core goals. Recognizing the compatibility of OSR and ontological emergence may contribute to the solution of ontological puzzles in physics while offering new ways to achieve the goals that advocates of OSR set for their view.

Scientific modelling is a value-laden process: the decisions involved can seldom be made using 'scientific' criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making them.

ABSTRACT In Philosophy Within its Proper Bounds, Édouard Machery argues that the results of experimental philosophy should lead us to abandon much of traditional philosophical practice. In its place Machery defends naturalized conceptual analysis as a more modest and pragmatic alternative to standard analytic philosophy. This paper argues that Machery overstates the metaphilosophical significance of x-phi’s results. We can and should keep many of the insights and good methodological habits that come with x-phi. However, if one is not already convinced of pragmatism or naturalism, the discoveries of x-phi are unlikely to make too much difference to one’s metaphilosophical position.

Meaningfulness is the dimension of importance that exists for beings capable of adjudicating between competing kinds of normative reasons. The way an agent decides to rank competing values in terms of importance reflects that agent’s understanding of what counts as meaningful. We can imagine agents who do not engage in this kind of deliberation. Agents who fail to adjudicate between kinds of normative reasons can still act in ways that are prudentially valuable, aesthetically pleasing, and morally praiseworthy. While the actions of such agents can be good in a variety of ways such actions can also be meaningless. This paper explains how meaningfulness is connected to deliberation, how one can be mistaken in one’s judgments of meaningfulness, and how some lives and practices can be more meaningful than others.

We address some of the epistemological challenges highlighted by the Critical Data Studies literature by reference to some of the key debates in the philosophy of science concerning computational modeling and simulation. We provide a brief overview of these debates focusing particularly on what Paul Humphreys calls epistemic opacity. We argue that debates in Critical Data Studies and philosophy of science have neglected the problem of error management and error detection. This is an especially important feature of the epistemology of Big Data. In “Error” section we explain the main characteristics of error detection and correction along with the relationship between error and path complexity in software. In this section we provide an overview of conventional statistical methods for error detection and review their limitations when faced with the high degree of conditionality inherent to modern software systems.

Scientific modelling is a value-laden process: the decisions involved can seldom be made using ‘scientific’ criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making them.

Software-intensive systems are ubiquitous in the industrialized world. The reliability of software has implications for how we understand scientific knowledge produced using software-intensive systems and for our understanding of the ethical and political status of technology. The reliability of a software system is largely determined by the distribution of errors and by the consequences of those errors in the usage of that system. We select a taxonomy of software error types from the literature on empirically observed software errors and compare that taxonomy to Giuseppe Primiero’s Minds and Machines 24: 249–273, (2014) taxonomy of error in information systems. Because Primiero’s taxonomy is articulated in terms of a coherent, explicit model of computation and is more fine-grained than the empirical taxonomy we select, we might expect Primiero’s taxonomy to provide insights into how to reduce the frequency of software error better than the empirical taxonomy. Whether using one software error taxonomy can help to reduce the frequency of software errors better than another taxonomy is ultimately an empirical question.

Science is a dynamic process in which the assimilation of new phenomena, perspectives, and hypotheses into the scientific corpus takes place slowly. The apparent disunity of the sciences is the unavoidable consequence of this gradual integration process. Some thinkers label this dynamical circumstance a ‘crisis’. However, a retrospective view of the practical results of the scientific enterprise and of science itself, grants us a clear view of the unity of the human knowledge seeking enterprise. This book provides many arguments, case studies and examples in favor of the unity of science. These contributions touch upon various scientific perspectives and disciplines such as: Physics, Computer Science, Biology, Neuroscience, Cognitive Psychology, and Economics.