In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML (...) may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)

Though the revised edition of A Theory of Justice, published in 1999, is the definitive statement of Rawls's view, so much of the extensive literature on Rawls's theory refers to the first edition. This reissue makes the first edition once again available for scholars and serious students of Rawls's work.

S. 39: "My project in this paper is to develop the initial distinction which I have drawn between recognition and appraisal respect into a more detailed and specific account of each. These accounts will not merely be of intrinsic interest. Ultimately I will use them to illuminate the puzzles with which this paper began and to understand the idea of self-respect." 42 " Thus, insofar as respect within such a pursuit will depend on an appraisal of the participant from the (...) perspective of whatever standards are held to be appropriate to the pursuit, such respect will depend on a judgement to which excellences of character are thought relevant. Note that exactly what aspect of the person's character is thought to be relevant in this instance is his recognition respect (or lack of it) for the standards of the pursuit." 44 "This attitude fails of being appraisal respect in that my having it toward the person is conditional on those traits being such as to make him server a particular purpose that I happen to have" 44 - 45 "To sum up: Appraisal respect is an attitude of positibe appraisal of a person either judged as a person or as engaged in some more specific pursuit. In the first case, the appropriate grounds are features of the person's character: dispositions to act for particular reasons or a higher-level disposition to act for the best reasons. In the second case, though features of character do not exhaust the appropriate grounds for appraisal respect, some such character traits will be relevant (recognition respect for the standards of a particular pursuit). Also, the other features which constitute the appoprate excellences of the pursuit must be related to traits of character in the way specified. In both cases, the positive appraisal of the person, and of his traits, must be categorical. It cannot depend on the fact that the person, because of his traits, serves an interest or purpose of one's own." 45 "To have recognition respect for someone as a person is to give appropriate weight to the fact that he or she is a person by being willing to constrain one's behaviour in ways required by that fact." 45 "This is rather different from having an attitude of appraisal respect for someone as a person. The latter is a positive appraisal oif an individual made with regard ti those features which are excellences of persons. As such, it is not owed to everone, for it may or may not be merited. When it is, what is merited is just the positive appraisal itself." 47 "Second, the only beings who are approriate objects of appraisal respect are those who are capable of acting for reasons and hence capable of conceiving of various facts as meriting more or less consideration in deliberation.". (shrink)

We live in the Age of Big Data. In medicine, artificial intelligence and machine learning algorithms, fueled by big data, promise to change how physicians make diagnoses, determine prognoses, and develop new treatments. An exponential rise in articles on these topics is seen in the medical literature. Recent applications range from the use of deep learning neural networks to diagnose diabetic retinopathy and skin cancer from image databases, to the use of various machine learning algorithms for prognostication in cancer and (...) cardiovascular disease. Many factors are driving the adoption of AI in health care, from... (shrink)

In what sense are persons equal, such that it is appropriate to treat them as equals? This difficult question has been strangely neglected by political philosophers. A plausible answer can be found by adopting a particular interpretation of the idea of respect. Central to this interpretation is the thought that in order to respect persons we need to treat them as ‘opaque', paying attention only to their outward features as agents. This proposed basis of equality has important implications for the (...) currency of egalitarian justice, ruling out a number of the equalizanda favored by contemporary egalitarians. (shrink)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; that the way in which organizations practically engage with emergent (...) expectations about algorithms may be manipulative, adaptive, or moral; and that when accountability relationships are heavily burdened by the opacity and fluidity of complex algorithmic systems, the emphasis of engagement should shift to a rational communication process through which a continuous and tentative assessment of the development, workings, and consequences of algorithms can be achieved over time. The degree to which such engagement is, in fact, rational can be assessed based on four discourse-ethical principles of participation, comprehension, multivocality, and responsiveness. We conclude that the framework may help organizations and their environments to jointly work toward greater accountability for complex algorithms. It may further help organizations in reputational positioning surrounding accountability issues. The discourse-ethical principles introduced in this article are meant to elevate these positioning contests to extend beyond mere adaption or compliance and help guide organizations to find moral and forward-looking solutions to accountability issues. (shrink)

Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the (...) algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping. (shrink)

This paper discusses means for promoting artificial intelligence that is designed to be safe and beneficial for society. The promotion of beneficial AI is a social challenge because it seeks to motivate AI developers to choose beneficial AI designs. Currently, the AI field is focused mainly on building AIs that are more capable, with little regard to social impacts. Two types of measures are available for encouraging the AI field to shift more toward building beneficial AI. Extrinsic measures impose constraints (...) or incentives on AI researchers to induce them to pursue beneficial AI even if they do not want to. Intrinsic measures encourage AI researchers to want to pursue beneficial AI. Prior research focuses on extrinsic measures, but intrinsic measures are at least as important. Indeed, intrinsic factors can determine the success of extrinsic measures. Efforts to promote beneficial AI must consider intrinsic factors by studying the social psychology of AI research communities. (shrink)

Exploring the philosophical foundations of discrimination law as it exists in several jurisdictions, this collection of all new essays bridges the gap between abstract philosophical work on justice and fairness and legal work on specific types of discrimination.

This book addresses these three issues: What is discrimination?; What makes it wrong?; What should be done about wrongful discrimination? It argues: that there are different concepts of discrimination; that discrimination is not always morally wrong and that when it is, it is so primarily because of its harmful effects.

Hardly anyone disputes that discrimination can be a grave moral wrong. Yet this consensus masks fundamental disagreements about what makes something discrimination, as well as precisely why acts of discrimination are wrong. Benjamin Eidelson develops systematic answers to those two questions. He claims that discrimination is a form of differential treatment distinguished by its special connection to the differential ascription of some property to different people, and goes on to argue that what makes some cases of discrimination intrinsically wrongful is (...) that they manifest an attitude of disrespect for the personhood of those who are disfavored. He endeavors to specify what this attitude consists in, and to demonstrate how attending to its character can help us to better understand the moral dimensions of different forms of wrongful discrimination. The book concludes with an extended discussion of racial profiling in law enforcement. (shrink)

How should medical services be distributed within society? Who should pay for them? Is it right that large amounts should be spent on sophisticated technology and expensive operations, or would the resources be better employed in, for instance, less costly preventive measures? These and others are the questions addreses in this book. Norman Daniels examines some of the dilemmas thrown up by conflicting demands for medical attention, and goes on to advance a theory of justice in the distribution of health (...) care. The central argument is that health care, both preventive and acute, has a crucial effect on equality of opportunity, and that a principle guaranteeing equality of opportunity must underly the distribution of health-care services. Access to care, preventive measures, treatment of the elderly, and the obligations of doctors and medical administrations are fully discussed, and the theory is shown to underwrite various practical policies in the area. (shrink)

Why do American ghettos persist? Decades after Moynihan’s report on the black family and the Kerner Commission’s investigations of urban disorders, deeply disadvantaged black communities remain a disturbing reality. Scholars and commentators today often identify some factor―such as single motherhood, joblessness, or violent street crime―as the key to solving the problem and recommend policies accordingly. But, Tommie Shelby argues, these attempts to “fix” ghettos or “help” their poor inhabitants ignore fundamental questions of justice and fail to see the urban poor (...) as moral agents responding to injustice. (shrink)

Name any valued human trait—intelligence, wit, charm, grace, strength—and you will find an inexhaustible variety and complexity in its expression among individuals. Yet we insist that such diversity does not provide grounds for differential treatment at the most basic level. Whatever merit, blame, praise, love, or hate we receive as beings with a particular past and a particular constitution, we are always and everywhere due equal respect merely as persons. -/- But why? Most who attempt to answer this question appeal (...) to the idea that all human beings possess an intrinsic dignity and worth—grounded in our capacities, for example, to reason, reflect, or love—that raises us up in the order of nature. Andrea Sangiovanni rejects this predominant view and offers a radical alternative. -/- To understand our commitment to basic equality, Humanity without Dignity argues that we must begin with a consideration not of equality but of inequality. Rather than search for a chimerical value-bestowing capacity possessed to an equal extent by each one of us, we ought to ask: Why and when is it wrong to treat others as inferior? Sangiovanni comes to the conclusion that our commitment to moral equality is best explained by a rejection of cruelty rather than a celebration of rational capacity. He traces the impact of this fundamental shift for our understanding of human rights and the norms of anti-discrimination that underlie it. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

This paper reconsiders some themes and arguments from my earlier paper “Fairness, Respect and the Egalitarian Ethos.” That work is often considered to be part of a cluster of papers attacking “luck egalitarianism” on the grounds that insisting on luck egalitarianism's standards of fairness undermines relations of mutual respect among citizens. While this is an accurate reading, the earlier paper did not make its motivations clear, and the current paper attempts to explain the reasons that led me to write the (...) earlier paper, assesses the force of its arguments, and locates it in respect to work of Richard Arneson and Elizabeth Anderson. The paper concludes by bringing out what now appears to be the main message of the earlier paper: that the attempt to implement an “ideal” theory of equality can harm the very people that the theory is designed to help. (shrink)

Safe-by-Design (SBD) frameworks for the development of emerging technologies have become an ever more popular means by which scholars argue that transformative emerging technologies can safely incorporate human values. One such popular SBD methodology is called Value Sensitive Design (VSD). A central tenet of this design methodology is to investigate stakeholder values and design those values into technologies during early stage research and development (R&D). To accomplish this, the VSD framework mandates that designers consult the philosophical and ethical literature to (...) best determine how to weigh moral trade-offs. However, the VSD framework also concedes the universalism of moral values, particularly the values of freedom, autonomy, equality trust and privacy justice. This paper argues that the VSD methodology, particularly applied to nano-bio-info-cogno (NBIC) technologies, has an insufficient grounding for the determination of moral values. As such, an exploration of the value-investigations of VSD are deconstructed to illustrate both its strengths and weaknesses. This paper also provides possible modalities for the strengthening of the VSD methodology, particularly through the application of moral imagination and how moral imagination exceed the boundaries of moral intuitions in the development of novel technologies. (shrink)

Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and (...) public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution. Design/methodology/approach The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them. Findings The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation. Research limitations/implications The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations. Practical implications The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans. Social implications The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms. Originality/value The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed (...) at clarifying the aim and structure of the paper, in section “Fairness in algorithmic decision-making” we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings; in section “Fairness as an ethical value”, we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension; it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship; these components are grounded in the need to respect persons both as persons and as particular individuals. In section “Fairness in algorithmic decision-making revised”, we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section “Concluding remarks”, we sketch some broader implications and conclude. (shrink)

There is widespread agreement that there should be a principle requiring that artificial intelligence be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” :689–707, 2018). There (...) is a strong intuition that if an algorithm decides, for example, whether to give someone a loan, then that algorithm should be explicable. I argue here, however, that such a principle is misdirected. The property of requiring explicability should attach to a particular action or decision rather than the entity making that decision. It is the context and the potential harm resulting from decisions that drive the moral need for explicability—not the process by which decisions are reached. Related to this is the fact that AI is used for many low-risk purposes for which it would be unnecessary to require that it be explicable. A principle requiring explicability would prevent us from reaping the benefits of AI used in these situations. Finally, the explanations given by explicable AI are only fruitful if we already know which considerations are acceptable for the decision at hand. If we already have these considerations, then there is no need to use contemporary AI algorithms because standard automation would be available. In other words, a principle of explicability for AI makes the use of AI redundant. (shrink)

A person enters the moral realm when she affirms that other persons matter in the same way that she does. This, of course, is just the beginning, for she must then determine what follows from this affirmation. One way in which we treat other persons as mattering is by respecting them. And one way in which we respect persons is by respecting their wishes, desires, decisions, choices, ends, and goals. I will call all of these things ‘aims.’ Sometimes we respect (...) another person's aims simply byrefrainingfrom doing certain things, such as treating her in ways that thwart her aims, or interfering with her attempt to pursue them. Other times we respect a person's aims by taking positive action to help her pursue them.But how exactly does respect forpersonstranslate into respect for theiraims?And which aims merit respect? One answer comes from Kant. (shrink)

A person enters the moral realm when she affirms that other persons matter in the same way that she does. This, of course, is just the beginning, for she must then determine what follows from this affirmation. One way in which we treat other persons as mattering is by respecting them. And one way in which we respect persons is by respecting their wishes, desires, decisions, choices, ends, and goals. I will call all of these things ‘aims.’ Sometimes we respect (...) another person's aims simply byrefrainingfrom doing certain things, such as treating her in ways that thwart her aims, or interfering with her attempt to pursue them. Other times we respect a person's aims by taking positive action to help her pursue them.But how exactly does respect forpersonstranslate into respect for theiraims?And which aims merit respect? One answer comes from Kant. (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)

Liberal political philosophers have underestimated the philosophical relevance of historical injustice. For some groups, injustices from the past—particularly surrounding race, ethnicity, or religion—are a source of entrenched social inequality decades or even hundreds of years later. Rawls does not advocate the importance of redressing historical injustice, yet political liberalism needs a principle of historical redress. Rawls’s principle of fair equality of opportunity, which is designed to prevent the leveraging of class privilege, could be paired with a supporting principle of historical (...) redress that would contend with partiality and bias in open access to positions. (shrink)

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Philosophy & Social Criticism, Volume 48, Issue 9, Page 1226-1246, November 2022. In this study, we claim that political liberalism, despite harsh criticism, is still the best option available for providing a just and stable society. However, we maintain that political liberalism needs to be revised so as to be justifiable from the perspective of not only the “reasonable” in a Rawlsian sense but also the ones whom Rawls labels as “unreasonable.” To support our claim, going beyond Rawls’s original account, (...) we unpack the concept of unreasonableness and identify three different subsets that we label as the “partially reasonable,” the “non-reasonable,” and the “unreasonable.” We argue that both the “fully” reasonable and the “partially reasonable” would be included into the constituency of public justification; more specifically, we claim that the latter would support liberal institutions out of their reasons: we define these reasons as mutually intelligible reasons and claim that they allow to acknowledge the importance of a convergence approach to public justification. As for the “non-reasonable” and “unreasonable,” we claim that they cannot be included in the constituency of public justification, but they nonetheless could be compliant with liberal institutions if political liberalism offers them some reasons to comply: here, we claim that political liberalism should include them through engagement and propose reasoning from conjecture as an effecting way of offering reasons for compliance. In particular, we claim that through reasoning from conjecture, the “non-reasonable” could find conciliatory reasons to comply with liberal institutions on a stable base. With regard to the “unreasonable” in the strict sense, we claim that through reasoning from conjecture, their unreasonableness could be contained and they could find reasons—even if just self-interested—for complying with liberal institutions rather than defying them. In our discussion, we consider the different subsets not as “frozen” but as dynamic and open to change, and we aim to propose a more complex and multilayered approach to inclusion that would be able to include a wider set of people. To strengthen our argument, we show that the need for a wider public justification and for broader inclusion in liberal societies is grounded in respect for persons both as equal persons and as particular individuals. In particular, we claim that individuals’ values, ends, commitments, and affiliations activate demands of respect and can strengthen the commitment to the liberal–democratic order. Through a reformulation of the role of respect in liberal societies, we also show a kind of social and communitarian dimension that, we claim, is fully compatible with political liberalism and opens it up to “civic friendship” and “social solidarity,” which are constitutive elements for the development of a sense of justice and for the realization of a just and stable society. (shrink)

Philosophy & Social Criticism, Volume 48, Issue 9, Page 1226-1246, November 2022. In this study, we claim that political liberalism, despite harsh criticism, is still the best option available for providing a just and stable society. However, we maintain that political liberalism needs to be revised so as to be justifiable from the perspective of not only the “reasonable” in a Rawlsian sense but also the ones whom Rawls labels as “unreasonable.” To support our claim, going beyond Rawls’s original account, (...) we unpack the concept of unreasonableness and identify three different subsets that we label as the “partially reasonable,” the “non-reasonable,” and the “unreasonable.” We argue that both the “fully” reasonable and the “partially reasonable” would be included into the constituency of public justification; more specifically, we claim that the latter would support liberal institutions out of their reasons: we define these reasons as mutually intelligible reasons and claim that they allow to acknowledge the importance of a convergence approach to public justification. As for the “non-reasonable” and “unreasonable,” we claim that they cannot be included in the constituency of public justification, but they nonetheless could be compliant with liberal institutions if political liberalism offers them some reasons to comply: here, we claim that political liberalism should include them through engagement and propose reasoning from conjecture as an effecting way of offering reasons for compliance. In particular, we claim that through reasoning from conjecture, the “non-reasonable” could find conciliatory reasons to comply with liberal institutions on a stable base. With regard to the “unreasonable” in the strict sense, we claim that through reasoning from conjecture, their unreasonableness could be contained and they could find reasons—even if just self-interested—for complying with liberal institutions rather than defying them. In our discussion, we consider the different subsets not as “frozen” but as dynamic and open to change, and we aim to propose a more complex and multilayered approach to inclusion that would be able to include a wider set of people. To strengthen our argument, we show that the need for a wider public justification and for broader inclusion in liberal societies is grounded in respect for persons both as equal persons and as particular individuals. In particular, we claim that individuals’ values, ends, commitments, and affiliations activate demands of respect and can strengthen the commitment to the liberal–democratic order. Through a reformulation of the role of respect in liberal societies, we also show a kind of social and communitarian dimension that, we claim, is fully compatible with political liberalism and opens it up to “civic friendship” and “social solidarity,” which are constitutive elements for the development of a sense of justice and for the realization of a just and stable society. (shrink)

Most theories of justice rest on the idea that emotions need to be contained or set aside and that rationality serves as the best, if not exclusive, criterion for identifying the principles of a fair distribution. In recent years, however, two important claims have been made. One is that rationality and emotions are not in conflict with one another, but should be conceived of as strictly interconnected; the other is that social justice is not just about distribution, but also – (...) or especially – about social relations. This paper aims to bridge these claims and argues that (a) the role of emotions should be recognised by theories of justice, as it is compatible with the importance such theories attribute to rationality, and (b) an inquiry into emotions is particularly important to investigate the socio-relational dimension of justice. Moreover, the paper claims that (c) in order to adequately investigate the role of emotions in social justice, it is important to acknowledge that emotions are structurally mediated and shaped by powerful transformative forces, among which technologies – especially so-called socially disruptive technologies (SDTs) – prove to be particularly relevant. The paper argues that SDTs have a fundamental role in shaping social relations, shared commitments, and the emotions that follow from them or enable them. (shrink)

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are (...) discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: automation and algorithmic reliance impacting freedom of choice, big data analytics complexity impacting informed consent, reliance on profiling impacting individual and group identities and justice/fair access and increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. (shrink)

This book provides a general theory of discrimination law as practised in liberal democratic jurisdictions. Rejecting accounts that place the value of equality at the heart of the law, it argues that discrimination law protects individual autonomy. Applying the theory, the book tackles the central legal problems in applying discrimination laws.

This book provides a general theory of discrimination law as practised in liberal democratic jurisdictions. Rejecting accounts that place the value of equality at the heart of the law, it argues that discrimination law protects individual autonomy. Applying the theory, the book tackles the central legal problems in applying discrimination laws.

This book brings together a team of leading theorists to address the question 'What is the right measure of justice?' Some contributors, following Amartya Sen and Martha Nussbaum, argue that we should focus on capabilities, or what people are able to do and to be. Others, following John Rawls, argue for focussing on social primary goods, the goods which society produces and which people can use. Still others see both views as incomplete and complementary to one another. Their essays evaluate (...) the two approaches in the light of particular issues of social justice - education, health policy, disability, children, gender justice - and the volume concludes with an essay by Amartya Sen, who originated the capabilities approach. (shrink)

Search engines play an increasingly pivotal role in the distribution and eventual construction of knowledge, yet they are largely unnoticed, their procedures are opaque, and they are almost completely devoid of independent oversight. In this paper the author examines three areas in which we encounter difficult and persistent ethical issues in search engine technology: The problem of algorithm and the lack of transparency of the search process, the problem of privacy with regards of the possibility to monitor search histories, and (...) the problem of local censorship. The given findings lead to the conclusion that the development of structures of accountability for search engines is an important task for the near future. (shrink)