Does one have special obligations to support the political institutions of one’s own country precisely because it is one’s own? In short, does one have political obligations? This book argues for an affirmative answer, construing one’s country as a political society of which one is a member, and a political society as a special type of social group. The obligations in question are not moral requirements derived from general moral principles. They come, rather, from one’s participation in a special kind (...) of commitment: a joint commitment. This theory is referred to as the plural subject theory of political obligation since, by the author’s definition, those who are party to any joint commitment constitute a plural subject of some action in a broad sense of the term. Several alternative theories are compared and contrasted with plural subject theory, with a particular focus on the most famous — actual contract theory — according to which membership in a political society is a matter of participation in an agreement. The book offers plural subject accounts of both social rules and everyday agreements, and includes discussion of political authority and punishment. (shrink)

Deeply embedded in popular cultural portrayals of admirable lives, in everyday conceptions of maturity, and in philosophical work in ethics and political philosophy is the idea that people not only will, but ought to, make commitments and that it is good for the individual herself to do so. In part one I briefly raise skeptical doubts about the defensibility of the normative pressure to commit, and suggest that commitment may only be one style of managing one’s diachronic existence. In part (...) two I examine the distinguishing features of commitment and how commitment differs from “mere” intention. Parts three and four are devoted to detailed critique of the principal philosophical defenses of the value of commitment, including both Pragmatic and Better Life arguments. In the last section, I try to construct an explanation for what makes commitment attractive to many persons if not universally so. (shrink)

Respect, Pluralism, and Justice is a series of essays which sketches a broadly Kantian framework for moral deliberation, and then uses it to address important social and political issues. Hill shows how Kantian theory can be developed to deal with questions about cultural diversity, punishment, political violence, responsibility for the consequences of wrongdoing, and state coercion in a pluralistic society.

The importance of online social spaces is growing. New Web 2.0 resources allow the creation of social networks by any netizen with minimal technical skills. These communities can be extremely narrowly focussed. In this paper, I identify two potential costs of membership in narrowly focussed virtual communities. First, that narrowly focussed communities can polarise attitudes and prejudices leading to increased social cleavage and division. Second, that they can lead sick individuals to revel in their illness, deliberately indulging in their disease (...) and denying the edicts of the medical profession. I specifically examine illness communities centred on the now defunct Multiple Personality Disorder. I highlight these potential problems and point to some technologies that may help combat them. (shrink)

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)

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)

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)

When and why do socially constructed norms—including the laws of the land, norms of etiquette, and informal customs—generate moral obligations? I argue that the answer lies in the duty to respect others, specifically to give them what I call “agency respect.” This is the kind of respect that people are owed in light of how they exercise their agency. My central thesis is this: To the extent that (i) existing norms are underpinned by people’s commitments as agents and (ii) they (...) do not conflict with morality, they place moral demands on us on agency-respect grounds. This view of the moral force of socially constructed norms, I suggest, is superior to views that deny the moral force of such norms, and it elegantly explains certain instances of wrongdoing that would otherwise remain unaccounted for. (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)

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)

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)

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.

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)

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)

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)

Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the mechanical (...) tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (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)

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)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

In this paper we discuss the social and ethical issues that arise as a result of digitization based on six dominant technologies: Internet of Things, robotics, biometrics, persuasive technology, virtual & augmented reality, and digital platforms. We highlight the many developments in the digitizing society that appear to be at odds with six recurring themes revealing from our analysis of the scientific literature on the dominant technologies: privacy, autonomy, security, human dignity, justice, and balance of power. This study shows that (...) the new wave of digitization is putting pressure on these public values. In order to effectively shape the digital society in a socially and ethically responsible way, stakeholders need to have a clear understanding of what such issues might be. Supervision has been developed the most in the areas of privacy and data protection. For other ethical issues concerning digitization such as discrimination, autonomy, human dignity and unequal balance of power, the supervision is not as well organized. (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.

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)

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)

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.

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)

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)