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.

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)

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)

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)

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)

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...

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)

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- (...) class='Hi'>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)

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)

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)

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)

Lane et al. propose an integrative model for the reconsolidation of traces in their timely and impressive article. This commentary draws attention to tradeoffs between accuracy and self-narrative integrity in the model. The tradeoffs concern the sense of agency in memory and its role in both implicit and explicit memory reconsolidation, rather than balances concerning degrees of emotional arousal.

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)

Non-technical summary: -/- When developing and deploying negative emissions technologies (NETs), little attention has been paid to where. On the one hand, one might develop NETs where they are likely to contribute most to global mitigation targets, contributing to a global climate solution. On the other hand, one might develop NETs where they can help support development on a regional basis, justified by regional demands. I defend these arguments and suggest that they reflect the values of efficiency and responding to (...) need, respectively. To the extent that these values conflict, they introduce what I call the Need-Efficiency Trade-off Effect (‘NET Effect’). -/- Technical summary: -/- Unlike other geoengineering methods, the effectiveness of negative emissions technologies (NETs) tends to be sensitive to regional siting. This paper argues that this point raises morally and legally important implications by identifying a trade-off between ‘efficiency’ and ‘need’. First, it introduces two arguments justifying NETs: one focused on contributions to global mitigation and the other focused on contributions to regional development. Second, reflecting the two arguments, the paper discusses the moral values of efficiency and need, respectively. For instance, if the strategy is to try to use NETs to maximize expected mitigation contributions to reflect efficiency, then deployment should occur in regions with the best prospects for success (e.g. Western countries). However, if the strategy is to try to use NETs to improve the chances of simultaneous development and mitigation to respond to need, then deployment should occur in regions with limited development and expected growth of demand for NETs (e.g. Asian countries). When these values conflict, I call that a Need-Efficiency Trade-off Effect (‘NET Effect’). The paper concludes by considering the NET Effect in the context of bioenergy with carbon capture and storage as well as direct air carbon capture and storage. [Open access]. (shrink)

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an (...) example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised. (shrink)

Engineers fine-tune the design of robot bodies for control purposes, however, a methodology or set of tools is largely absent, and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging 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 ”morphological computation”. In this article, we briefly review different notions of computation by physical systems and (...) propose the dynamical systems framework as the most useful in the context of describing and eventually designing the interactions of controllers and bodies. Then, we 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 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. (shrink)

Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on ‘description’, ‘classification’, and ‘prediction’, we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing ‘things in the world’, these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research questions? (...) In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors. (shrink)

Minimum wages are usually assumed to be inefficient as they prevent the full exploitation of mutual gains from trade. Yet advocates of wage regulation policies have repeatedly claimed that this loss in market efficiency can be justified by the pursuit of ethical goals. Policy makers, it is argued, should not focus on efficiency alone. Rather, they should try to find an adequate balance between efficiency and equity targets. This idea is based on a two-worlds-paradigm that sees ethics and economics (...) as two inherently conflicting ways of thinking. We, however, believe that this view of the relationship between ethics and economics is fundamentally flawed and blurs our understanding of how an ethically responsible regulation of the labour market should be conducted. In drawing on an economic-ethical approach that resolves the antinomy between efficiency and equity, we show that ethics and economics are, in fact, two sides of the same coin and that minimum wage legislation can only be ethically responsible, if it is at the same time economically efficient. In other words, we can have our cake and eat it too. On the basis of our approach, we develop two simple game theoretical models for different types of labour markets and derive policy implications from an economic-ethical viewpoint. We suggest that under the assumption of perfectly competitive labour markets a tax-funded wage subsidy is preferable over minimum wages, as it makes everyone better off. If, however, employers have monopsony power in the wage setting process, the minimum wage is justifiable under certain conditions. (shrink)

The Market Failures Approach (MFA) is one of the leading theories in contemporary business ethics. It generates a list of ethical obligations for the managers of private firms that states that they should not create or exploit market failures because doing so reduces the efficiency of the economy. Recently the MFA has been criticised by Abraham Singer on the basis that it unjustifiably does not assign private managers obligations based on egalitarian values. Singer proposes an extension to the MFA, the (...) Justice Failures Approach (JFA), in which managers have duties to alleviate political, social, and distributive inequalities in addition to having obligations to not exploit market failures. In this paper I describe the MFA and JFA and situate them relative to each other. I then highlight a threefold distinction between different types of obligations that can be given to private managers in order to argue that a hybrid theory of business ethics, which I call the MFA +, can be generated by arguing that managers have obligations based on efficiency and duties based on equality to the extent that these latter obligations do not lead to efficiency losses. This argument suggests a novel theoretical option in business ethics, elucidates the issues that are at stake between the MFA and the JFA, and clarifies the costs and benefits of each theory. (shrink)

This paper disputes the notion, endorsed by much of narrative theory, that the reading of literary narrative is functionally analogous to an act of communication, where communication stands for the transfer of thought and conceptual information. The paper offers a basic typology of the sensorimotor effects of reading, which fall outside such a narrowly communication-based model of literary narrative. A main typological distinction is drawn between those sensorimotor effects pertaining to the narrative qua verbal utterance (verbal presence) and those sensorimotor (...) effects pertaining to the imaginary physical world(s) of the story (direct presence). While verbal presence refers to the reader's vicarious perception of the voices of narrators and characters, direct presence refers to the emulated sensorimotor experience of the imaginary worlds that the narrators' and characters' utterances refer to. The paper further elaborates on how, by which kinds of narrative content and structure, direct presence may be prompted. The final section addresses some of the observational and historical caveats that must be attached to any theoretical inquiry made into the sensorimotor effects of reading. As a preliminary for further research, a few ideas about the model's potential for empirical validation are put forward. A brief, tentative history of the sensorimotor benefits of literary narrative reading is then outlined. (shrink)

This article argues that capital flight of real investment presents governments with a quadrilemma. First, governments can tailor their policies to attract investors – but this is incompatible with a whole range of alternative policy choices. Second, they can simply accept capital flight – but this is incompatible with a robust capital stock and tax base. Third, they can harmonize its taxes and regulations with other states – but this is incompatible with international independence. Fourth, they can impose capital controls (...) – but this is incompatible with international capital mobility. These incompatibilities make up four different goals, the value of which are described. Strategies may be mixed, but the pursuit of any three goals must always come at the expense of the fourth. (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)

Internet trolling involves making assertions with the aim of provoking emotionally heated responses, all while pretending to be a sincere interlocutor. In this paper, I give an account of some of the epistemic and psychological dimensions of trolling, with the goal of better understanding why certain kinds of trolling can be dangerous. I first analyze how trolls eschew the epistemic norms of assertion, thus covertly violating their conversation partners’ normative expectations. Then, drawing on literature on the “explore/exploit trade-off,” I (...) argue that trolling is a kind of exploratory behaviour. Specifically, it involves exploring the consequences of violating an interlocutor’s expectation that one will follow the epistemic norms of assertion. To defend this account, I argue that it explains various facts about trolls and the kind of pleasure they get from their activities. My account provides a deeper understanding of why trolling can be dangerous: namely, it explains why certain trolling behaviours contribute to radicalizing people into extremist or hateful ideologies, as well as why online platforms where trolls run amok gradually become polluted with extreme, hateful speech. (shrink)

There is a general presumption against arming outlaw states. But can that presumption sometimes be overturned? The argument considered here maintains that outlaw states can have legitimate security interests and that transferring weapons to these states can be an appropriate way of promoting those interests. Weapons enable governments to engage in wrongful oppression and aggression, but they also enable them to fend off predators in a manner that can be beneficial to their citizens. It clearly does not follow from the (...) fact that a state is oppressive or aggressive that it will never be a victim of wrongful aggression itself, and while an outlaw state’s primary aim in repelling such aggression will often be the preservation of its own power, its defensive manoeuvres will sometimes also serve its citizens’ interests. In short, supplying weapons to outlaw states may sometimes contribute to the protection of innocents. (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)

This paper addresses a relatively new mode of ecological research: data synthesis studies. Data synthesis studies involve reusing data to create a general model as well as a reusable, aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I (...) also identify an additional trade-off, the synthesis trade-off, between making data easy to synthesise for a particular project, on the one hand, and facilitating data reuse for other projects, on the other. In response to this synthesis trade-off, researchers create flexible datasets that are relatively easy to use for particular projects and can be adjusted to suit some other purposes. The flexibility compromise is also found in broader open data efforts, making it a significant element in the future of data-intensive ecology. (shrink)

Davis called for “extreme caution” in the use of non-invasive brain stimulation to treat neurological disorders in children, due to gaps in scientific knowledge. We are sympathetic to his position. However, we must also address the ethical implications of applying this technology to minors. Compensatory trade-offs associated with NIBS present a challenge to its use in children, insofar as these trade-offs have the effect of limiting the child’s future options. The distinction between treatment and enhancement has (...) some normative force here. As the intervention moves away from being a treatment toward being an enhancement—and thus toward a more uncertain weighing of the benefits, risks, and costs—considerations of the child’s best interests diminish, and the need to protect the child’s autonomy looms larger. NIBS for enhancement involving trade-offs should therefore be delayed, if possible, until the child reaches a state of maturity and can make an informed, personal decision. NIBS for treatment, by contrast, is permissible insofar as it can be shown to be at least as safe and effective as currently approved treatments, which are themselves justified on a best interests standard. (shrink)

Today, a major challenge for climate science is to overcome what is called the “usability gap” between the projections derived from climate models and the needs of the end-users. Regional Climate Models (RCMs) are expected to provide usable information concerning a variety of impacts and for a wide range of end-users. It is often assumed that the development of more accurate, more complex RCMs with higher spatial resolution should bring process understanding and better local projections, thus overcoming the usability gap. (...) In this paper, I rather assume that the credibility of climate information should be pursued together with two other criteria of usability, which are salience and legitimacy. Based on the Swiss climate change scenarios, I study the attempts at meeting the needs of end-users and outline the trade-off modellers and users have to face with respect to the cascade of uncertainty. A conclusion of this paper is that the trade-off between salience and credibility sets the conditions under which RCMs can be deemed adequate for the purposes of addressing the needs of end-users and gearing the communication of the projections toward direct use and action. (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)

Codes of ethics currently offer no guidance to scientists acting in capacity of expert. Yet communicating their expertise is one of the most important activities of scientists. Here I argue that expert communication has a specifically ethical dimension, and that experts must face a fundamental trade-off between "actionability" and "transparency" when communicating. Some recommendations for expert communication are suggested.

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)

A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory predicts (...) that, all else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role. (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)

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)

When it comes to epistemic normativity, should we take the good to be prior to the right? That is, should we ground facts about what we ought and ought not believe on a given occasion in facts about the value of being in certain cognitive states (such as, for example, the value of having true beliefs)? The overwhelming answer among contemporary epistemologists is “Yes, we should.” This essay argues to the contrary. Just as taking the good to be prior to (...) the right in ethics often leads one to sanction implausible trade-offs when determining what an agent should do, so too, this essay argues, taking the good to be prior to the right in epistemology leads one to sanction implausible trade-offs when determining what a subject should believe. Epistemic value—and, by extension, epistemic goals—are not the explanatory foundation upon which all other normative notions in epistemology rest. (shrink)

Drawing on the puzzling behavior of ordinary knowledge ascriptions that embed an epistemic (im)possibility claim, we tentatively conclude that it is untenable to jointly endorse (i) an unfettered classical logic for epistemic language, (ii) the general veridicality of knowledge ascription, and (iii) an intuitive ‘negative transparency’ thesis that reduces knowledge of a simple negated ‘might’ claim to an epistemic claim without modal content. We motivate a strategic trade-off: preserve veridicality and (generalized) negative transparency, while abandoning the general validity of (...) contraposition. We criticize various approaches to incorporating veridicality into domain semantics, a paradigmatic ‘information-sensitive’ framework for capturing negative transparency and, more generally, the non-classical behavior of sentences with epistemic modals. We then present a novel information-sensitive semantics that successfully executes our favored strategy: stable acceptance semantics, extending a vanilla bilateral state-based semantics for epistemic modals with a knowledge operator loosely inspired by the defeasibility theory of knowledge. (shrink)

Categorization behavior can be fruitfully analyzed in terms of the trade‐off between as high as possible faithfulness in the transmission of information about samples of the classes to be categorized, and as low as possible transmission costs for that same information. The kinds of categorization behaviors we associate with conceptual atoms, prototypes, and exemplars emerge naturally as a result of this trade‐off, in the presence of certain natural constraints on the probabilistic distribution of samples, and the ways in (...) which we measure faithfulness. Beyond the general structure of categorization in these circumstances, the same information‐centered perspective can shed light on other, more concrete properties of human categorization performance, such as the results of certain prominent experiments on supervised categorization. (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)

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)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More (...) specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

Often philosophers, logicians, and mathematicians employ a notion of intended structure when talking about a branch of mathematics. In addition, we know that there are foundational mathematical theories that can find representatives for the objects of informal mathematics. In this paper, we examine how faithfully foundational theories can represent intended structures, and show that this question is closely linked to the decidability of the theory of the intended structure. We argue that this sheds light on the trade-off between expressive (...) power and meta-theoretic properties when comparing first-order and second-order logic. (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)

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)

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- (...) class='Hi'>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)

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his (...) local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin’s behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin’s own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin’s consumption more exploratory than the culture’s production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior. (shrink)

Political theorists have offered many accounts of collective decision-making under pluralism. I discuss a key dimension on which such accounts differ: the importance assigned not only to the choices made but also to the reasons underlying those choices. On that dimension, different accounts lie in between two extremes. The ‘minimal liberal account’ holds that collective decisions should be made only on practical actions or policies and that underlying reasons should be kept private. The ‘comprehensive deliberative account’ stresses the importance of (...) giving reasons for collective decisions, where such reasons should also be collectively decided. I compare these two accounts on the basis of a formal model developed in the growing literature on the ‘discursive dilemma’ and ‘judgment aggregation’ and address several questions: What is the trade-off between the (minimal liberal) demand for reaching agreement on outcomes and the (comprehensive deliberative) demand for reason-giving? How large should the ‘sphere of public reason’ be? When do the decision procedures suggested by the two accounts agree and when not? How good are these procedures at truthtracking on factual matters? What strategic incentives do they generate for decision-makers? My discussion identifies what is at stake in the choice between minimal liberal and comprehensive deliberative accounts of collective decisionmaking, and sheds light not only on these two ideal-typical accounts themselves, but also on many characteristics that intermediate accounts share with them. (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)

-/- For all that digital methods—including network visualization, text analysis, and others—have begun to show extensive promise in philosophical contexts, a tension remains between two uses of those tools that have often been taken to be incompatible, or at least to engage in a kind of trade-off: the discovery of new hypotheses and the testing of already-formulated positions. This paper presents this basic distinction, then explores ways to resolve this tension with the help of two interdisciplinary case studies, taken (...) from preregistration in contemporary science and the debate over whig history in the history of science. These case studies, the paper argues, refocus our attention from a mutually exclusive testing/discovery binary to the relationship between our background data or philosophical views and the empirical generalizations that we might draw from the data. Finally, it develops a set of three challenges for philosophers and corresponding avenues for future work that will, it is hoped, allow us to better justify our use of these methods. (shrink)