Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is lag- ging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or "soft" bodies. These carry substantial potential regarding their exploitation for control – sometimes referred to as "mor- phological computation" in the sense of offloading computation (...) needed for control to the body. Here, we will argue in favor of a dynamical systems rather than com- putational perspective on the problem. Then, we will look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of “soft” bodies automatically taking over control tasks. We will address another key dimension of the design space – whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies. ----- This paper was also published in the 2014 AISB proceedings http://aisb50.org/representation-of-reality-humans-animals-and-machines/ - http://doc.gold.ac.uk/aisb50/. (shrink)

Critics have recently argued that reliabilists face trade-off problems, forcing them to condone intuitively unjustified beliefs when they generate lots of true belief further downstream. What these critics overlook is that reliabilism entails that there areside-constraintson belief-formation, on account of which there are some things you should not believe, even if doing so would have very good epistemic consequences. However, we argue that by embracing side-constraints the reliabilist faces a dilemma: she can either hold on to reliabilism, and (...) with it aforementioned side-constraints, but then needs to explain why we should allow the pursuit of justification to get in the way of the acquisition of true belief; or she can deny that there are side-constraints – and in effect give up on reliabilism. We'll suggest that anyone moved by the considerations that likely attract people to reliabilism in the first place – the idea the true belief is good, and as such should be promoted – should go for the second horn, and instead pursue a form ofepistemic utilitarianism. (shrink)

James Sterba uses the Pauline Principle to argue that the occurrence of significant, horrendous evils is logically incompatible with the existence of a good God. The Pauline Principle states that (as a rule) one must never do evil so that good may come from it, and according to Sterba, this principle implies that God may not permit significant evils even if that permission would be necessary to secure other, greater goods. By contrast, I argue that the occurrence of significant evils (...) is logically compatible with the existence of a good God because victims of significant evils may themselves reasonably consent to their suffering. In particular, I argue that they may be able to accept their suffering if it turns out that there was no way for God to secure relevant greater goods (or prevent other, greater evils) except by way of allowing their suffering, and God also provides them with other compensating, heavenly comforts. After using this consent-based argument to address Sterba’s logical problem from evil, I briefly consider how this argument may also help address a related evidential problem from evil, which suggests that while it is possible that victims of significant evils would consent to their suffering, it is unlikely that they would do so. While I do not provide a definitive solution to this evidential problem of evil, I highlight one important example of a trade-off that God may need to make that would—along with the provision of compensating, heavenly comforts—potentially persuade victims of significant evils to consent to their suffering. Specifically, I argue that there may be a necessary trade-off that God needs to make between permitting significant evils (on the one hand) and protecting a certain, morally significant form of free will (on the other hand). (shrink)

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. And (...) patience trades off against shutdownability: the more patient an agent, the greater the costs that agent is willing to incur to manipulate the shutdown button. I end by noting that these theorems can guide our search for solutions. (shrink)

There is a long-standing debate in philosophy about whether it is morally permissible to harm one person in order to prevent a greater harm to others and, if not, what is the moral principle underlying the prohibition. Hypothetical moral dilemmas are used in order to probe moral intuitions. Philosophers use them to achieve a reflective equilibrium between intuitions and principles, psychologists to investigate moral decision-making processes. In the dilemmas, the harms that are traded off are almost always deaths. However, the (...) moral principles and psychological processes are supposed to be broader than this, encompassing harms other than death. Further, if the standard pattern of intuitions is preserved in the domain of economic harm, then that would open up the possibility of studying behavior in trolley problems using the tools of experimental economics. We report the results of two studies designed to test whether the standard patterns of intuitions are preserved when the domain and severity of harm are varied. Our findings show that the difference in moral intuitions between bystander and footbridge scenarios is replicated across different domains and levels of physical and non-physical harm, including economic harms. (shrink)

This article introduces and defends the “pathological complexity thesis” as a hypothesis about the evolutionary origins of minimal consciousness, or sentience, that connects the study of animal consciousness closely with work in behavioral ecology and evolutionary biology. I argue that consciousness is an adaptive solution to a design problem that led to the extinction of complex multicellular animal life following the Avalon explosion and that was subsequently solved during the Cambrian explosion. This is the economic trade-off problem of having (...) to deal with a complex body with high degrees of freedom, what I call “pathological complexity.” By modeling the explosion of this computational complexity using the resources of state-based behavioral and life history theory we will be able to provide an evolutionary bottom-up framework to make sense of subjective experience and its function in nature by paying close attention to the ecological lifestyles of different animals. (shrink)

According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: i) to bring attention to this neglected issue, ii) to clarify what exactly this concern about dehumanization might amount to, and iii) to sketch an argument for why dehumanizing the hiring (...) process is ethically suspect. After distinguishing the use of the term “dehumanization” in this context (i.e., removing the human presence) from its more common meaning in the interdisciplinary field of dehumanization studies (i.e., conceiving of other humans as subhuman), we argue that the use of hiring algorithms may negatively impact the employee-employer relationship. We argue that there are good independent reasons to accept a substantive employee-employer relationship, as well as an applicant-employer relationship, both of which are consistent with a stakeholder theory of corporate obligations. We further argue that dehumanizing the hiring process may negatively impact these relationships because of the difference between the values of human recruiters and the values embedded in recruitment algorithms. Drawing on Nguyen’s (2021) critique of how Twitter “gamifies communication”, we argue that replacing human recruiters with algorithms imports artificial values into the hiring process. We close by briefly considering some ways to potentially mitigate the problems posed by recruitment algorithms, along with the possibility that some difficult trade-offs will need to be made. (shrink)

Among the factors necessary for the occurrence of some event, which of these are selectively highlighted in its explanation and labeled as causes-and which are explanatorily omitted, or relegated to the status of background conditions? Following J. S. Mill, most have thought that only a pragmatic answer to this question was possible. In this paper I suggest we understand this ‘causal selection problem’ in causal-explanatory terms, and propose that explanatory trade-offs between abstraction and stability can provide a principled solution (...) to it. After sketching that solution, it is applied to a few biological examples. (shrink)

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers (...) and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot. (shrink)

It is common practice in intergenerational justice to set fixed thresholds determining what qualifies as justice. Static definitions of how much and what to save for future generations, however, overestimate human epistemological limits and predictive capacity in regard to uncertainty in social- and ecosystems. Long-term predictions cannot account for the inherent range of contingent variables at play, especially according to contemporary theories of punctuated equilibrium. It is argued that policies deliberately testing ecological limits as currently conceived must be excluded from (...) political calculations to minimize current actions from foreclosing future life quality and options. To better reduce harms to present and future generations and support the stability of just institutions (and the stable environments they depend on) as Rawls and others demand, polities can overcompensate resource allocation to environmental sustainability. Focus on reducing environmental harms benefits both current and future generations, reconciling distributive versus intergenerational justice trade-offs. (shrink)

There is a trade-off between specificity and accuracy in existing models of belief. Descriptions of agents in the tripartite model, which recognizes only three doxastic attitudes—belief, disbelief, and suspension of judgment—are typically accurate, but not sufficiently specific. The orthodox Bayesian model, which requires real-valued credences, is perfectly specific, but often inaccurate: we often lack precise credences. I argue, first, that a popular attempt to fix the Bayesian model by using sets of functions is also inaccurate, since it requires us (...) to have interval-valued credences with perfectly precise endpoints. We can see this problem as analogous to the problem of higher order vagueness. Ultimately, I argue, the only way to avoid these problems is to endorse Insurmountable Unclassifiability. This principle has some surprising and radical consequences. For example, it entails that the trade-off between accuracy and specificity is in-principle unavoidable: sometimes it is simply impossible to characterize an agent’s doxastic state in a way that is both fully accurate and maximally specific. What we can do, however, is improve on both the tripartite and existing Bayesian models. I construct a new model of belief—the minimal model—that allows us to characterize agents with much greater specificity than the tripartite model, and yet which remains, unlike existing Bayesian models, perfectly accurate. (shrink)

Which is better: a guarantee of a modest amount of moral value, or a tiny probability of arbitrarily large value? To prefer the latter seems fanatical. But, as I argue, avoiding such fanaticism brings severe problems. To do so, we must decline intuitively attractive trade-offs; rank structurally identical pairs of lotteries inconsistently, or else admit absurd sensitivity to tiny probability differences; have rankings depend on remote, unaffected events ; and often neglect to rank lotteries as we already know (...) we would if we learned more. Compared to these implications, fanaticism is highly plausible. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. (...) Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements. (shrink)

This paper outlines an account of political realism as a form of ideology critique. Our focus is a defence of the normative edge of this critical-theoretic project against the common charge that there is a problematic trade-off between a theory’s groundedness in facts about the political status quo and its ability to consistently envisage radical departures from the status quo. To overcome that problem we combine insights from three distant corners of the philosophical landscape: theories of legitimacy by Bernard (...) Williams and other realists, Frankfurt School-inspired Critical Theory, and recent analytic epistemological and metaphysical theories of cognitive bias, ideology, and social construction. The upshot is a novel account of realism as empirically-informed diagnosis- critique of social and political phenomena. This view rejects a sharp divide between descriptive and normative theory, and so is an alternative to the anti- empiricism of some approaches to Critical Theory as well as to the complacency towards existing power structures found within liberal realism, let alone mainstream normative political philosophy, liberal or otherwise. (shrink)

We argue that deliberative decision-making that is inclusive, transparent and accountable can contribute to more trustworthy and legitimate decisions on difficult ethical questions and political trade-offs during the pandemic and beyond.

In 2012, the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth’s history, had one-quarter of its boundaries moved in a widespread revision of radiometric dates. The philosophy of metrology helps us understand this episode, and it, in turn, elucidates the notions of calibration, coherence, and consilience. I argue that coherence testing is a distinct activity preceding calibration and consilience, and I highlight the value of (...) discordant evidence and trade-offs scientists face in calibration. The iterative nature of calibration, moreover, raises the problem of legacy data. (shrink)

Among the factors necessary for the occurrence of some event, which of these are selectively highlighted in its explanation and labeled as causes — and which are explanatorily omitted, or relegated to the status of background conditions? Following J. S. Mill, most have thought that only a pragmatic answer to this question was possible. In this paper I suggest we understand this ‘causal selection problem’ in causal-explanatory terms, and propose that explanatory trade-offs between abstraction and stability can provide a (...) principled solution to it. After sketching that solution, it is applied to a few biological examples, including to a debate concerning the ‘causal democracy’ of organismal development, with an anti-democratic (though not a gene-centric) moral. (shrink)

This paper introduces the Monstrous Conclusion, according to which, for any population, there is a better population consisting of just one individual (the Monster). The Monstrous Conclusion is deeply counterintuitive. I defend a version of Prioritarianism as a particularly promising population axiology that does not imply the Monstrous Conclusion. According to this version of Prioritarianism, which I call Asymptotic Prioritarianism, there is diminishing marginal moral importance of individual welfare that can get close to, but never quite reach, some upper limit. (...) I argue that Asymptotic Prioritarianism faces a theoretical cost, that I call the Absolute Priority Principle. However, the Absolute Priority Principle is an extreme version of what I call the Trade-off Condition, an already noteworthy problem facing other (more widely endorsed) versions of Prioritarianism. I conclude that it is better for a theory to imply the Absolute Priority Principle and avoid the Monstrous Conclusion than to imply the Monstrous Conclusion and the Trade-off Condition. The potential for Asymptotic Prioritarianism is substantial. (shrink)

The main thesis of this paper is that when we trust the results of scientific research, that trust is inevitably directed at least in part at collective bodies rather than at single researchers, and that accordingly, reasonable assessments of epistemic trustworthiness in science must attend to these collective bodies. In order to support this claim, I start by invoking the collaborative nature of most of today’s scientific research. I argue that the trustworthiness of a collaborative research group does not supervene (...) on the trustworthiness of its individual members and point out some specific problems for the assessment of epistemic trustworthiness that arise from the specific nature of today’s collaborative research. Next, I argue that the social diffusion of trustworthiness goes even further; we always also need an assessment of the trustworthiness of the respective research community as a whole. Communities, I claim, play an essential role in the epistemic quality management of science. To see why this role is indispensible, we have to appreciate the full complexity of determining what is desirable in a method of inquiry. The relevant features of a method include three different dimensions: the reliability of positive results, the reliability of negative results, and the method’s power. Every methodological choice involves a trade-off between these three dimensions. The right balance between them (the “distribution of inductive risks”, or DIR) depends on value judgments about the costs of false results and the benefits of correct ones. Conventional methodological standards of research communities impose constraints on admissible DIR and thereby harmonize the implicit value judgments. Trusting that the research community has set the limitations on DIR in a suitable way is thus always part of placing our trust in a scientific result. (shrink)

Our aim in this chapter is to draw attention to what we see as a disturbing feature of conciliationist views of disagreement. Roughly put, the trouble is that conciliatory responses to in-group disagreement can lead to the frustration of a group's epistemic priorities: that is, the group's favoured trade-off between the "Jamesian goals" of truth-seeking and error-avoidance. We show how this problem can arise within a simple belief aggregation framework, and draw some general lessons about when the problem is (...) most pronounced. We close with a tentative proposal for how to solve the problem raised without rejecting conciliationism. (shrink)

This research is concerned with understanding the factors behind the trade-off between child labor and child schooling, given the well-documented links between the two. It examines parents' behavior in their decision-making on their children's schooling or practicing child labor. Depending on qualitative research methods including 28 semi-structured interviews and two focus group discussions conducted in the rural areas of Bangladesh in 2020, this study reveals the following: subsistence needs compel households, particularly the ultra-poor and the female-headed, to trade (...) off child labor with schooling; due to higher demand of labor, parents engage their children into work instead of schooling; parents of labor-intensive occupations tend to trade off child labor with schooling; sexual division of labor remains obvious; finally, credit constraints and cultural beliefs have negative impacts on parental decision-making on child schooling. Interventions aiming to reduce child labor and increase schooling in these rural areas must remain mindful of the socio-economic and cultural needs. (shrink)

The paper analyses six ethical challenges posed by cloud computing, concerning ownership, safety, fairness, responsibility, accountability and privacy. The first part defines cloud computing on the basis of a resource-oriented approach, and outlines the main features that characterise such technology. Following these clarifications, the second part argues that cloud computing reshapes some classic problems often debated in information and computer ethics. To begin with, cloud computing makes possible a complete decoupling of ownership, possession and use of data and this (...) helps to explain the problems occurring when different providers of cloud computing retain or relinquish the right to use or own users‘ data. The problem of safety in cloud computing is coupled to that of reliability, insofar as users have to trust providers to preserve their data, applications and content in a reliable manner. It is argued that, in this context, data insurance could play an important role. Regarding fairness, the paper argues that cloud computing is already reshaping the nature of the Digital. Responsibility, accountability and privacy close the ethical analysis of cloud computing. In this case, the thesis is that the necessity to account for the actions of cloud computing users imposes delicate trade-offs between users‘ privacy and the traceability of their operations. (shrink)

This paper presents two challenges faced by many initiatives that try to diversify undergraduate philosophy curricula, both intellectually and demographically. Trade-offs involve making difficult decisions to prioritise some values over others (like gender diversity over cultural diversity). Backfires involve unintended consequences contrary to the aims and values of diversity initiatives, including ones that compromise more general philosophical values. I discuss two specific backfire risks, involving the critical and political dimensions of teaching philosophy. Some general practical advice is offered along (...) the way. (shrink)

The field of public policy is dominated by the social sciences. Schools and departments of public policy and public administration are largely populated by economists, political scientists, and sociologists, and the vast majority of work in prestigious public policy journals employs empirical methods. This is unsurprising, in one respect, for collecting data, predicting and identifying the causal impacts of policies, and understanding political institutions and processes are massive, important tasks that require the tools of the social sciences. It is surprising, (...) in another respect however, since much policy scholarship and analysis aspires to offer recommendations to policymakers, and recommendations necessarily involve evaluative judgments, for example, that the status quo is deficient and that a certain course of action will make things better. To defend such judgments, policy scholars and analysts cannot appeal to the social sciences alone for these judgments concern the realm of values, not facts. Instead, careful ethical reflection and analysis is required. This need for ethical reflection in public policy is particularly evident in the practice of policy analysis, a principal analytic tool of the field. Policy analyses involve the identification of policy problems, the specification of criteria for evaluating possible solutions, and the recommendation that a policy option offers the best balance of trade offs. This identification of bads, the specification of goods policies should realize, and the weighing of these goods with the judgment that policymakers should or ought to pursue this course of action or that, are all deeply ethical activities. Ethical reflection and analysis is thus a constituent feature of policy analysis, and like the amassing of evidence and projection of outcomes, this reflection and analysis may be performed well or badly. The overall aim of this book is to provide students, scholars, and practitioners of public policy with guidance regarding the ethical dimensions of policy analysis. My aim in this chapter is to set the stage, providing an overview of these dimensions. After providing a brief overview of the practice of policy analysis, I identify the steps of an analysis where analysts must make ethical judgments in order to move forward, and fully articulate the ethical questions they must confront and take a position on. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze (...) normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

We develop and apply a multi-dimensional conception of explanatory depth towards a comparative analysis of inflationary and bouncing paradigms in primordial cosmology. Our analysis builds on earlier work due to Azhar and Loeb (2021) that establishes initial condition fine-tuning as a dimension of explanatory depth relevant to debates in contemporary cosmology. We propose dynamical fine-tuning and autonomy as two further dimensions of depth in the context of problems with instability and trans-Planckian modes that afflict bouncing and inflationary approaches respectively. (...) In the context of the latter issue, we argue that the recently formulated trans-Planckian censorship conjecture leads to a trade-off for inflationary models between dynamical fine-tuning and autonomy. We conclude with the suggestion that explanatory preference with regard to the different dimensions of depth is best understood in terms of differing attitudes towards heuristics for future model building. (shrink)

This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...

Teleological accounts of sensory normativity treat normal functioning for a species as a standard: sensory error involves departure from normal functioning for the species, i.e. sensory malfunction. Straightforward reflection on sensory trade-offs reveals that normal functioning for a species can exhibit failures of accuracy. Acknowledging these failures of accuracy is central to understanding the adaptations of a species. To make room for these errors we have to go beyond the teleological framework and invoke the notion of an ideal observer (...) from vision science. The notion of an ideal observer also sheds light on the important distinction between sensory malfunction and sensory limitation. (shrink)

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...) are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values. (shrink)

The task of Philosophy in Science (PinS) is to use philosophical tools to help solve scientific problems. This article describes how I stumbled into this line of work and then addressed several topics in philosophy of biology—units of selection, cladistic parsimony, robustness and trade-offs in model building, adaptationism, and evidence for common ancestry—often in collaboration with scientists. I conclude by offering advice for would-be PinS practitioners.

ABSTRACT A new ‘political philosophy’ is indispensable to the ‘post-Corona world,’ and this paper tries to analyze the future of ‘liberal democracy’ in it. It shows that ‘liberal democracy’ faces a ‘global crisis’ that has begun before, but the ‘novel Coronavirus pandemic,’ as a setback for it, strongly encourages that crisis. ‘Liberalism’ and ‘democracy,’ which had long been assumed by ‘political philosophers’ to go together, are now becoming decoupled, and the ‘liberal values’ of ‘democracy’ are eroding. To find why and (...) how, this paper analyzes ‘authoritarianism,’ ‘totalitarianism,’ and the evils and propensities of ‘democracy’ that bring about further erosions of ‘liberal values.’ There may be difficult trade-offs to be made between ‘liberal’ and ‘authoritarian’ ‘values’ - and, after the experience of ‘Coronavirus,’ this paper shows the ‘illiberal or authoritarian democracy’ may become stronger. -/- KEYWORDS: democracy, liberalism, liberal democracy, illiberal democracy, the novel Coronavirus pandemic, Covid-19, authoritarianism, totalitarianism. -/- EXTENDED ABSTRACT For almost a century in West, ‘democracy’ has meant ‘liberal democracy’-a political system marked not only by ‘free and fair elections,’ but also by ‘liberal values.’ ‘Liberalism’ applied to the problem of the limits of the criminal law would require commitment to the presumption in favor of ‘liberty.’ If the word ‘liberal’ is to have any utility in this context, it should refer to one who has so powerful a commitment to ‘liberty’ that she is motivated to limit the number of acknowledged liberty-limiting or coercion-legitimizing principles as narrowly as possible. So, she only believes in ‘harm-principle’ as the morally relevant reason for criminal prohibitions. ‘Offense principle’ and ‘paternalistic’ and ‘moralistic’ considerations, when introduced as support for penal legislation, have no weight at all. So, it means ‘autonomy’ and ‘self-regarding vs. other-regarding actions distinction’ based on which the human is the owner of her mind and body and everything consenting adults do is beyond the realm of morality and law. It leads to ‘individual’ ‘basic rights and liberties’ such as ‘basic rights and liberties’ of ‘speech,’ ‘religion,’ and ‘property’ and ‘collective’ ‘basic rights and liberties’ such as ‘basic rights and liberties of assembly,’ ‘civil society,’ ‘political pluralism,’ ‘democratic institutions,’ and ‘non-governmental organizations.’ ‘Liberalism’ also believes in ‘equality of conditions, ‘equal and free participatory rights in political decision making,’ and ‘collective self-governance.’ It also believes in the ‘rule of law,’ a ‘separation of powers,’ and ‘checks and balances.’ However, even under fair and free elections, the elected leaders can be ‘populists,’ ‘ultra-nationalists,’ ‘racists,’ ‘fascists,’ and ‘authoritarians’ who do not respect ‘inviolable basic rights and liberties,’ and suppress ‘minorities.’ Democratically elected leaders can routinely ignore constitutional limits on their power and deprive their citizens of ‘basic rights and liberties.’ The two strands of ‘liberal democracy’ have been coming apart in the world, and the ‘liberal’ elements of ‘democracy’ have been fraying and eroding even before the ‘novel Coronavirus pandemic.’ Even before the Coronavirus hit, there was already much discussion of a crisis of ‘liberal democracy.’ In particular, there has been a debate about whether ‘liberalism’ and ‘democracy,’ which had long been assumed to go together, were becoming decoupled. This paper shows ‘liberal democracy’ faces a ‘global crisis’ that had begun before the ‘novel Coronavirus pandemic.’ It also analyzes ‘authoritarianism,’ ‘totalitarianism,’ ‘liberalism,’ and ‘democracy’ and shows that Covid-19 is a setback for ‘liberal democracy.’ What is striking about the current moment is that many of the ‘liberal’ elements of ‘democracy’ are so far holding up under immense pressure. ‘Illiberal democracies’ seemed to be emerging in many countries. This model of ‘illiberal democracy,’ in which elections continue to be held but some individual rights and liberties are curtailed, may emerge stronger from this new crisis. In that sense, the ‘pandemic’ may become a challenge not only to ‘democracy’ as such but also to ‘liberal democracy’ in particular – in other words, a system of popular sovereignty together with guaranteed basic rights, such as freedom of association and expression and checks and balances on executive power. ‘Authoritarian’ procedures may succeed in mitigating the spread of the ‘Coronavirus,’ but the world now faces another problem: that when the virus recedes, many ‘liberal democracies’ will be far less ‘liberal’ or ‘democratic’ than they were before. In times of crisis, ‘liberal values’ have been ignored temporarily in the name of executive power. However, the ‘temporary’ can become ‘permanent.’ In addition, if citizens lose their faith in the legitimacy of ‘liberal democracy’ as the best form of government, and think ‘liberal democracy’ cannot function effectively during a crisis, and ‘authoritarian regimes’ manage the crisis more decisively, the world will slide towards ‘illiberal or authoritarian democracy,’ and many ‘liberal democracies’ will be at grave risk of failure. There may now be difficult trade-offs to be made between those basic rights and security - and, after the experience of Covid-19, many citizens may choose security. (shrink)

I investigate whether heuristics similar to those studied by Gigerenzer and his co-authors can apply to the problem of finding a suitable heuristic for a given problem. I argue that not only can heuristics of a very similar kind apply but they have the added advantage that they need not incorporate specific trade-off parameters for balancing the different desiderata of a good decision-procedure.

Most mental disorders affect only a small segment of the population. On the reasonable assumption that minds or brains are prone to occasional malfunction, these disorders do not seem to pose distinctive explanatory problems. Depression, however, because it is so prevalent and costly, poses a conundrum that some try to explain by characterizing it as an adaptation—a trait that exists because it performed fitness-enhancing functions in ancestral populations. Heretofore, proposed evolutionary explanations of depression did not focus on thought processes; (...) instead, they emphasized that it facilitates navigation of adverse social circumstances or promotes immune response to infectious agents. According to a new hypothesis, the “analytical rumination hypothesis” (ARH), however, depression’s crucial adaptive trait is rumination—negative, intrusive thought. ARH holds that, (i) social dilemmas trigger depressed mood; (ii) depressed mood induces changes in body systems that facilitate ruminative analysis aimed at solving dilemmas; and, (iii) depressive rumination is a fitness-enhancing trait that was selected for in evolutionary time. Jointly, (i)~(iii) imply that we should not think of rumination as a disorder; instead, it is a trade-off, an eminently rational one. In the same way that fever solves a problem—coordination of the immune system in response to infection—so too does depressive rumination solve a problem, a social dilemma, albeit at the cost of inducing anhedonia and other maladies. But they argue that the cost is worthwhile, something that should be endured “until the problem is solved.” First, we argue that there are two distinct types of rumination, brooding and pondering; the former is associated with a disposition for depression, not the latter. But only the latter has the problem-solving capabilities that ARH requires. Second, recent brain imaging studies of depression reveal resting state hypoactivity in lateral regions and hyperactivity in paralimbic regions; this asymmetric pattern correlates with heightened levels of brooding, self-focused rumination. In other words, on the personal level, patients are trapped within self, isolated from the external world and suffused with negative affect; on the subpersonal level, this pattern is reflected by an asymmetric pattern of lateral vs. paralimbic resting state activity. Third, we proceed to conjecture that rational responses (e.g., pondering) to social dilemmas are those that strike a balance between internal and external considerations in the process of belief formation. Fourth, because the asymmetric resting state activity blocks those who suffer with depression from accessing and processing potentially positive stimuli from the external world, the capacity for rational, analytic response—hence, problem-solving—is constrained. Fifth, it follows that, although there might be conditions for which suffering should be endured rather than pharmacologically alleviated, depression is not one of those. Indeed, in view of the effects of the asymmetric resting state pattern, it is unlikely that depressive rumination would have been useful even for ancestral populations. (shrink)

This paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this (...) prevalent interpretation by drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values. I show how neglecting these considerations can distort our normative deliberations, and result in costly and misaligned interventions and justifications. Taken together, these considerations form a sociotechnical framework that could guide those involved in AI governance to assess how, in many cases, we can and should have higher aspirations than the prevalent interpretation of the trade-offs would suggest. I end by drawing out the normative opportunities and challenges that emerge out of these considerations, and highlighting the imperative of interdisciplinary collaboration in fostering responsible AI. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific (...) theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

In the debate on how we ought to define political freedom, some definitions are criticized for implying that no one can ever be free to perform any action. In this paper, I show how the possibility of freedom depends on a definition that finds an appropriate balance between absence of interference and protection against interference. To assess the possibility of different conceptions of freedom, I consider the trade-offs they make between these two dimensions. I find that pure negative freedom (...) is clearly possible. Republican freedom might also be possible, though its protection requirement is too vague for a definitive verdict. Finally, the recently proposed ‘freedom as independence’ is impossible since it is an attempt to avoid the unavoidable trade-off. (shrink)

Public and research libraries have long provided resources in electronic formats, and the tension between providing electronic resources and patron privacy is widely recognized. But assessing trade-offs between privacy and access to electronic resources remains difficult. One reason is a conceptual problem regarding intellectual freedom. Traditionally, the LIS literature has plausibly understood privacy as a facet of intellectual freedom. However, while certain types of electronic resource use may diminish patron privacy, thereby diminishing intellectual freedom, the opportunities created by such (...) resources also appear liberty-enhancing. Adjudicating between privacy loss and enhanced opportunities on intellectual freedom grounds must therefore provide an account of intellectual freedom capable of addressing both privacy and opportunity. I will argue that intellectual freedom is a form of positive freedom, where a person’s freedom is a function of the quality of her agency. Using this view as the lodestar, I articulate several principles for assessing adoption of electronic resources and privacy protections. (shrink)

Abstract: Recently, the experimental philosopher Joshua Knobe has shown that the folk are more inclined to describe side effects as intentional actions when they bring about bad results. Edouard Machery has offered an intriguing new explanation of Knobe's work—the 'trade-off hypothesis'—which denies that moral considerations explain folk applications of the concept of intentional action. We critique Machery's hypothesis and offer empirical evidence against it. We also evaluate the current state of the debate concerning the concept of intentionality, and argue (...) that, given the number of variables at play, any parsimonious account of the relevant data is implausible. (shrink)

In the context of a global pandemic, there is good health-based reason for governments to impose various social distancing measures. However, such measures also cause economic and other harms to people at low risk from the virus. In this paper, I examine how to make such trade-offs in a way that is respectfully justifiable to their losers. I argue that existing proposals like using standard QALY (quality-adjusted life-year) valuations or WELLBYs (wellbeing-adjusted life-years) as the currency for trade-offs do (...) not allow such justification, because they give weight to utilities that are irrelevant in a life-and-death context. Drawing on work on restricted aggregation in ethics, I articulate an alternative framework for balancing claims arising from prospects of different kinds of harm and benefit, and show how it can be applied to reasoning about trade-offs in a pandemic context. (shrink)

Given a subject so imbued with contention and conflicting theoretical stances, it is remarkable that automated instruments ever came to replace the human eye as sensitive arbiters of color specification. Yet, dramatic shifts in assumptions and practice did occur in the first half of the twentieth century. How and why was confidence transferred from careful observers to mechanized devices when the property being measured – color – had become so closely identified with human physiology and psychology? A fertile perspective on (...) the problem is via the history of science and technology, paying particular attention to social groups and disciplinary identity to determine how those factors affected their communities’ cognitive territory. There were both common and discordant threads motivating the various technical groups that took on the problems of measuring light and color from the late nineteenth century onwards, and leading them towards the development of appropriate instruments for themselves. The transition from visual to photoelectric methods could be portrayed as a natural evolution, replacing the eye by an alternative roviding more sensitivity and convenience – indeed, this is the conventional positivist view propounded by technical histories. However, the adoption of new measurement technologies seldom is simple, and frequently has a significant cultural component. Beneath this slide towards automation lay a raft of implicit assumptions about objectivity, the nature of the observer, the role of instruments, and the trade-offs between standardization and descriptive power. While espousing rational arguments for a physical detector of color, its proponents weighted their views with tacit considerations. The reassignment of trust from the eye to automated instruments was influenced as much by the historical context as by intellectual factors. I will argue that several distinct aspects were involved, which include the reductive view of color provided by the trichromatic theory; the impetus provided by its association with photometry; the expanding mood for a quantitative and objective approach to scientific observation; and, the pressures for commercial standardization. As suggested by these factors, there was another shift of authority at play: from one technical specialism to another. The regularization of color involved appropriation of the subject by a particular set of social interests: communities of physicists and engineers espousing a ‘physicalist’ interpretation, rather than psychologists and physiologists for whom color was conceived as a more complex phenomenon. Moreover, the sources for automated color measurement, and instrumentation for measuring color, were primarily from the industrial sphere rather than from academic science. To understand these shifts, then, this chapter explores differing views of the importance of observers, machines and automation. (shrink)

This paper explores the possibility that Hobbesian jurisprudence is best understood as a ‘third way’ in legal theory, irreducible to classical natural law or legal positivism. I sketch two potential ‘third theories’ of law -- legal pragmatism and legal dualism -- and argue that, when considered in its broadest sense, Leviathan is best viewed as an example of legal pragmatism. I consider whether this legal pragmatist interpretation can be sustained in the examination of Leviathan’s treatment of civil law, and argue (...) that the pragmatic interpretation can only be successful if we can resolve two textual issues in that chapter. First, while Hobbes argues that law entails the existence of public (sharable) reasons, he does not adequately defend the view that the sovereign is the unique authority over such reasons in all cases, especially as far as they concern known collective emergencies. Second, Hobbes both affirms and denies that a sovereign can fail to do justice, which is paradoxical. Both problems are best resolved by legal pragmatism, though the second problem resists a fully satisfying resolution. The upshot is that, although Leviathan ought to be regarded as an episode of legal pragmatism, there are trade-offs on every reading. (shrink)

Accelerating global climate change drives new climate risks. People around the world are researching, designing, and implementing strategies to manage these risks. Identifying and implementing sound climate risk management strategies poses nontrivial challenges including (a) linking the required disciplines, (b) identifying relevant values and objectives, (c) identifying and quantifying important uncertainties, (d) resolving interactions between decision levers and the system dynamics, (e) quantifying the trade-offs between diverse values under deep and dynamic uncertainties, (f) communicating to inform decisions, and (g) (...) learning from the decision-making needs to inform research design. Here we review these challenges and avenues to overcome them. ▪ People and institutions are confronted with emerging and dynamic climate risks. ▪ Stakeholder values are central to defining the decision problem. ▪ Mission-oriented basic research helps to improve the design of climate risk management strategies. (shrink)

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space, make inferences, as well as query for information under a limited budget. Re-exploration of the solution space takes place by an agent when an older solution expires and is thus able to adapt to dynamic changes in the environment. We investigate the effects of task dependencies, with highly-dependent graph G_40 (a well-known program graph that (...) contains 40 highly interlinked nodes, each representing a task) and less-dependent graphs G_18 (a program graph that contains 18 tasks with fewer links), increasing the speed of the agents and the complexity of the problem space and the query budgets available to agents. Specifically, we evaluate trade-offs between the agent's speed and query budget. During the experiments, we observed that increasing the speed of a single agent improves the system performance to a certain point only, and increasing the number of faster agents may not improve the system performance due to task dependencies. Favoring faster agents during budget allocation enhances the system performance, in line with the "Matthew effect." We also observe that allocating more budget to a faster agent gives better performance for a less-dependent system, but increasing the number of faster agents gives a better performance for a highly-dependent system. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...) the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

This chapter argues that rather than viewing transparency as a right, we should regard it as a finite resource whose allocation involves tradeoffs. It then argues that those tradeoffs should be resolved by using a multi-principle approach to distributive justice. The relevant principles include maximizing welfare, maximizing autonomy, and giving priority to the worst off. Finally, it examines some of the implications for law of recognizing the tradeoffs presented by transparency proposals.

Autonomy is often considered a core value of Western society that is deeply entrenched in moral, legal, and political practices. The development and deployment of artificial intelligence (AI) systems to perform a wide variety of tasks has raised new questions about how AI may affect human autonomy. Numerous guidelines on the responsible development of AI now emphasise the need for human autonomy to be protected. In some cases, this need is linked to the emergence of increasingly ‘autonomous’ AI systems that (...) can perform tasks without human control or supervision. Do such ‘autonomous’ systems pose a risk to our own human autonomy? In this article, I address the question of a trade-off between human autonomy and system ‘autonomy’. (shrink)

Moral error theorists often claim to be strongly anti‑metaphysical in their moral scepticism and atheistic naturalists. This paper argues that pre‑ cisely this becomes a problem for them, when their metaethical and ontologi‑ cal commitments clash. I first outline how the known arguments against error theory face a problematic, yet rarely considered trade‑off : either they are very strong, then they are also very demanding in their assumptions or they are less demanding in their assumptions but rather weak in (...) their conclusions. In re‑ sponse to this challenge I then develop a new argument against error theory that exploits an overlooked inconsistency in the error theorists’ standard line of argumentation. I conclude that the implications of this inconsistency are less of a problem for fictionalist error theorists, but will render any eliminativism based on error theory circular. (shrink)

All societies have to balance privacy claims with other moral concerns. However, while some concern for privacy appears to be a common feature of social life, the definition, extent and moral justifications for privacy differ widely. Are there better and worse ways of conceptualising, justifying, and managing privacy? These are the questions that lie in the background of this thesis. -/- My particular concern is with the ethical issues around privacy that are tied to the rise of new information and (...) communication technologies (henceforth “ICT”). The focus here is on technologies involved in processing personal data in its broadest sense, including “any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” The issue of moral concern is commonly designated as that of 'data privacy'. -/- The first step is to consider why privacy matters, or ought to matter, in the first place. And here we run into our first difficulty, since both the conceptualisation of privacy and the justification for the right to privacy are matters for contention. On the one hand, we are animated by a moral concern for privacy in some sense; on the other, we lack the conceptual and moral resources for making sense of this concern or for promoting particular social policies and criticising others. This often-cited dichotomy and its ensuing confusion have been the central motivation for most of the philosophical literature on privacy – including this thesis. -/- In what follows, I will suggest a way out of the confusion that pervades the current debate on data privacy. I will essentially argue two claims: one, that the concept of (data) privacy is uniquely characterised by a particular context within which we exchange information; two, that because (data) privacy constitutes an intrinsic value, its moral concerns and justifications ought to be deliberated as such. -/- The first chapter sketches the current data privacy landscape by introducing a number of concepts and arguing the unique nature of the landscape. I argue that the traditional distinction between four kinds of privacy (physical, mental, decisional and informational) is no longer accurate, given the technological context that reduces, unifies and processes all aspects of people’s lives by means of digital data. In fact, to hold on to the distinction leads to a fundamental misconception of data privacy. I further argue that today’s distinction between data protection and data privacy runs the risk of ignoring the fact that the former is an inherent aspect of the latter. These arguments allow me to settle on a (provisional) definition of data privacy: controlling how data about identifiable people is processed by ICT. -/- Next, I draw a line between descriptive and normative accounts of data privacy, and explain why this book belongs to the latter category. After a brief discussion of the coherence and distinctiveness theses, and of the difference between instrumental and intrinsic theories of privacy, I introduce the problem of data privacy as a distinct, coherent, and intrinsic moral concern. Three arguments support this position: one, data privacy is a coherent moral concern because it is motivated by a singular moral concept; two, data privacy is a distinct moral concern because the privacy challenges we face today are of a fundamentally new nature – distinct from any moral concerns we’ve faced in the past; and three, data privacy is an intrinsic moral concern that is predominantly justified, not by what it instrumentally protects us from, but by what it intrinsically constitutes. -/- Chapter Two makes the case against a harm-based, consequentialist approach to data privacy. After a brief introduction to the basic tenets of consequentialism I discuss harm-based arguments in favour as well as against privacy. I explain how privacy may protect us against psychological distress, but at the same time constitute the source of that distress; how privacy may be necessary for the proper functioning of democratic societies, but at the same time provide a cloak for anti-social and immoral behaviour; and how privacy protects us against the improper use of our personal data by others, but at the same enables us to conceal information or spread false information about ourselves in ways that are harmful to others. Next, I argue that consequentialism essentially relies on two flawed presumptions – that we know what harm is, and that we know how to predict it – and discuss the controversies surrounding definitions and predictions of harm. This argument becomes especially pertinent in today’s rapidly changing technological context: we may guess, and make assumptions, perhaps even find correlations, but don’t really know or understand (yet) with any degree of certainty the harmful consequences today’s data privacy violations are causing or will eventually cause – if any. -/- I illustrate this with examples of recent data privacy violations, and with a brief overview of consequentialist arguments for and against data privacy. Those who invoke consequentialist arguments against data privacy claim that personal data is nothing more than a raw material that can be turned into something useful and valuable without harming anyone. Choosing to move fast and break things – a popular mantra that promotes the benefits of technological “permissionless innovation” over and above its risks and costs – they draw our attention to the many benefits free data flows generate. Those who rely on consequentialist arguments in favour of data privacy, on the other hand, insist on actual and potential misuse of personal data. Rather careful than sorry, they draw our attention to the urgent need to take back control and protect ourselves against data privacy violations – precisely because we don’t know which harm they may cause in the future. Others go one step further and argue that these violations are part of a broader ideology of power. -/- What Chapter Two demonstrates is that vague definitions, unreliable predictions and inconclusive allegations of harm from both sides of the debate perpetuate a stalemate that fails to produce morally defensible answers to our growing data privacy concerns. Deliberating about data privacy from a consequentialist perspective is a dead end. -/- Chapter Three proposes an alternative to the harm-based approach, namely that privacy is an intrinsic value that deserves moral protection for its own sake. After a brief account of what I take values to be, and why we have a need to protect them, I introduce the foundational value of respect for persons, and lay out my main argument in two steps. The conceptual component of my argument returns to the definition of data privacy I had temporarily settled on in Chapter One, but now with the added consideration of the particular context, and particular circumstances and attitudes, within which information is exchanged. This addition is crucial, as it reveals the fundamental value that is at stake in our data privacy concerns, namely the inherent value of each and every person, which we protect by upholding an ethical principle commonly known as the principle of respect for persons. It is for this reason that the violation of privacy cannot (only) be expressed in terms of harm. I further argue that the principle of respect for persons is consistent with, and in fact unthinkable without, some degree of privacy. -/- I reply to potential challenges to a value-based approach to data privacy by showing that (i) values and value trade-offs are fundamentally different from consequential benefits and cost-benefit analyses; (ii) the suspension of one value in favour of another is not inherently contradictory; what matters is morally defensible deliberation about values and valid individual consent; (iii) data privacy concerns cannot be reduced to other moral concerns and therefore deserve a specific, distinct protection. -/- Finally, because this is a thesis in applied ethics, I attempt an answer to the question as to how we ought to apply a value-based approach to practical data privacy concerns. I argue that a revised, three-pronged version of John Rawls’ reflective equilibrium, founded on the non-negotiable principle of respect for persons, is the best possible procedure to deliberate data privacy concerns in relation to other moral values. (shrink)

At the height of the Covid-19 pandemic, frontline medical professionals at intensive care units around the world faced gruesome decisions about how to ration life-saving medical resources. These events provided a unique lens through which to understand how the public reasons about real-world dilemmas involving trade-offs between human lives. In three studies (total N = 2298), we examined people’s moral attitudes toward triage of acute coronavirus patients, and found elevated support for utilitarian triage policies. These utilitarian tendencies did not (...) stem from period change in moral attitudes relative to pre-pandemic levels--but rather, from the heightened realism of triage dilemmas. Participants favored utilitarian resolutions of critical care dilemmas when compared to structurally analogous, non-medical dilemmas—and such support was rooted in prosocial dispositions, including empathy and impartial beneficence. Finally, despite abundant evidence of political polarization surrounding Covid-19, moral views about critical care triage differed modestly, if at all, between liberals and conservatives. Taken together, our findings highlight people’s robust support for utilitarian measures in the face of a global public health threat, and illustrate how hypothetical scenarios in moral psychology (e.g. trolley cases) should strive for more experiential and psychological realism, otherwise their results might not generalize to real-world moral dilemmas. (shrink)