In this article we report the findings of a randomised control clinical trial that assessed the impact of a Philosophy for Children program and replicated a previous study conducted in Scotland by Topping and Trickey. A Cognitive Abilities Test was administered as a pretest and a posttest to randomly selected experimental groups and control groups. The students in the experimental group engaged in philosophy lessons in a setting of structured, collaborative inquiry in their language arts classes for one hour per (...) week for a number of weeks. The control group received the standard language arts curriculum in that one hour. The study found that the seventh grade students who had experienced the P4C program showed significant gains relative to those in the seventh grade control group at a high level of statistical significance, but the eighth grade students in the experimental group did not show such gains over the eighth grade control group. It was discovered that the seventh grade teachers started the program early in the school year and continued it for a period of 22 to 26 weeks, while the eighth grade teachers started much later and used the program for only 4 to 10 weeks. Our findings suggest that the P4C program must involve students in activities for a significant period of time before the program shows results, but that a meaningful impact on students’ cognitive abilities can be achieved in about 24 weeks of lessons, less than half the time evidenced by the study by Topping and Trickey. (shrink)

An insight, Central to platonism, That the objects of pure mathematics exist "in some sense" is probably essential to any adequate account of mathematical truth, Mathematical language, And the objectivity of the mathematical enterprise. Yet a platonistic ontology makes how we can come to know anything about mathematical objects and how we use them a dark mystery. In this paper I propose a framework for reconciling a representation-Relative provability theory of mathematical truth with platonism's valid insights. Besides helping to clarify (...) the ontology of pure mathematics, I think this approach suggests a novel philosophical interpretation of some central results of modern mathematics, Including godel's incompleteness theorems and the independence theorems for the continuum hypothesis. (shrink)

The paper examines the epistemological side of the Laclau’s political theory, incorporating tools that aim to convert discourse theory into a rigorous, useful, and valid method for socio-political and critical analysis in social sciences. In the first part, it displays some epistemological assumptions and arguments. The second part analyses some problematic epistemological aspects derived from the arguments. The proposal is based on Laclau’s main texts, complementing the analysis with related tools from critical hermeneutics and existential phenomenology. It is argued that (...) the incorporation of these tools to political discourse analysis contribute to solve some of the methodological and epistemic deficits of the theory of hegemony. El trabajo examina el plano epistemológico de la teoría política de Ernesto Laclau, aportando herramientas que tienen por objeto convertir a la teoría del discurso en un método riguroso, útil y válido para el análisis sociopolítico y crítico en ciencias sociales. En la primera parte, despliega una serie de postulados y presupuestos epistémicos. En la segunda parte, analiza algunos aspectos gnoseológicos problemáticos que se derivan de los postulados. Para desarrollar la propuesta, se toman como base los principales textos de Laclau, complementando el análisis con herramientas afines provenientes de la hermenéutica crítica y la fenomenología existencialista. Se sostiene que la incorporación de estas herramientas al análisis político del discurso contribuye a resolver algunos de los déficits metodológicos y epistémicos que presenta la teoría de la hegemonía. (shrink)

Recently, unrestrained consequentialism has been defended against the charge that it leads to unacceptable trade-offs by showing a tradeoff accepted by many of us is not justified by any of the usual nonconsequenlist arguments. The particular trade-off involves raising the speed limit on the Interstate Highway System. As a society, we seemingly accept a trade-off of lives for convenience. This defense of consequentialism may be a tu quoque, but it does challenge nonconsequentialists to adequately justify a multitude of social decisions. (...) Work by the deontologist Frances Kamm, conjoined with a perspective deployed by several economists on the relation between social costs and lives lost, is relevant. It provides a starting point by justifying decisions which involve trading lives only for other lives. But the perspective also recognizes that using resources in excess of some figure to save a life causes us to forego other live-saving activities, thus causing a net loss of life. Setting a speed limit as low as 35 miles per hour might indeed save some lives, but the loss of productivity due to the increased time spent in travel would cost an even greater number of lives. Therefore, many trade-offs do not simply involve trading lives for some lesser value, but are justified as allowing some to die in order to save a greater number. (shrink)

The fair use doctrine is at once the most significant and the most problematic qualification of the copyright owner's right to exclusivity. An affirmative defense against copyright liability, the fair use doctrine legitimates certain unauthorized reproductions of copyrighted materials that would otherwise be regarded as copyright infringements. Notwithstanding its importance, “fair use” continues to be “the most troublesome [doctrine] in the whole law of copyright.” Throughout its long history, neither courts nor legislatures have provided a useful definition (...) of “fair use” nor have they adumbrated its objectjves. Since the doctrine's inception over two and a half centuries ago, courts and legislatures have attempted to formulate, explicate, refine, and revamp the fair use doctrine. Generally, these efforts have proven unfruitful. At best, they have resulted in various formulations of how to approach fair use questions that offer courts and users of copyrighted works scant guidance on how fair use should be recognized. All this would not have been of grave concern had judges shared a common understanding of fair use or of the principles that should guide them in deciding fair use cases. The problem is that they do not. Rather, the case law reflects wdely divergent notions of the concept of fair use. The lack of consensus is best witnessed in the multiple reversals and divided courts that have become the hallmark of fair use litigation. (shrink)

This paper concerns the deficiencies of currentlyaccepted principles governing the fair use ofelectronically recorded data when applied to geneticinformation. Principles are proposed by which to dealwith the unique group-characteristics of geneticinformation.

The age of digital technology has introduced new complications into the issues of fair and private use of copyrighted material. In fact, the question of private use of another's work has been transformed from a side issue in intellectual property jurisprudence into the very center of intellectual property discussions about rights and privileges in a networked world. This paper will explore the nuanced difference between fair and private use as articulated in the US and the European Copyright Laws. (...) Part One will explain the legal use and meaning of fair use and its justifications. We maintain that it is almost impossible to resolve the issue of the fair/private use's legal nature, if one has not previously resolved copyright's legal nature. Legal resolution of such conflicts, as we shall demonstrate, follows different rules, and the messages sent to users and authors vary greatly depending on the jurisdiction. Part Two will examine the issue of Private Use as Fair Use by Design. We will look at the impact of Digital Rights Management on fair use and explore if the nature of private use as fair use has changed because we are dealing with the protection of digital content. We will show that protecting digital works by code, via various digital rights management schemes is shrinking the public domain of information and restricting private use as fair use. (shrink)

Appropriation art involves the use of pre-existing works of art with little to no transformation. Works of AA fail to satisfy established criteria for originality, such as creative labour and transformative use. As such, appropriation artists are often subject to copyright lawsuits and defend their work under the fair use doctrine of US copyright law. In legal cases regarding AA and fair use, judges lack a general principle whereby they can determine whether or not the offending party has (...) ‘transformed’ the original work. Further, it is not the case that there is some antecedent fact that could determine the outcome one way or another. I diagnose debates surrounding the transformative nature of works of AA as cases of ‘metalinguistic negotiation’ over what concepts we should attach to terms like ‘copy’, ‘transformative’, and ‘work of art’. (shrink)

Should public health authorities have access to your personal medical information without your permission? The usual justifications for requiring informed consent do not necessarily apply. That is not to say that consent may be avoided in all situations, just that the reasons for requiring consent in public health are different than for traditional medical treatment.

Looking at the metaphorical similarities between abortion statutes and copyright law reveals reproductive laws' stake in property ideologies. "Fair use" provisions in copyright law are analogous to abortion rights, delimiting the extent to which a non-owner (one who copies, a mother) can exert control over material "belonging" to another. The similarity suggests that the way to understand abortion fury is as a manifestation of property rights.

Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating (...) using this technology. (shrink)

El artículo analiza en qué medida la doctrina del fair use es consistente con tres de las principales justificaciones de la propiedad intelectual, para evaluar en qué medida puede decirse que es una doctri- na justa. Para desarrollar el análisis, se examina primero cómo han interpretado los jueces estadounidenses el alcance y la aplicación de esta doctrina y luego se evalúa en qué medida dicha interpretación resulta consistente con cada una de las tres justificaciones mencionadas.

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion as a (...) necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is well known from many application fields that such biases occur frequently and naturally when prediction models are applied to (...) people unless special efforts are made to avoid them. Insurance is no exception. In this paper, we provide a thorough analysis of algorithmic fairness in the case of insurance premiums. We ask what “fairness” might mean in this context and how the fairness of a premium system can be measured. For this, we apply the established fairness frameworks of the fair machine learning literature to the case of insurance premiums and show which of the existing fairness criteria can be applied to assess the fairness of insurance premiums. We argue that two of the often-discussed group fairness criteria, independence (also called statistical parity or demographic parity) and separation (also known as equalized odds), are not normatively appropriate for insurance premiums. Instead, we propose the sufficiency criterion (also known as well-calibration) as a morally defensible alternative that allows us to test for systematic biases in premiums towards certain groups based on the risk they bring to the pool. In addition, we clarify the connection between group fairness and different degrees of personalization. Our findings enable insurers to assess the fairness properties of their risk models, helping them avoid reputation damage resulting from potentially unfair and discriminatory premium systems. (shrink)

Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied to (...) the frequently cited COMPAS versus ProPublica dispute in judicial ethics. The application provides a broader model for settling contemporary and topical debates about fairness and solidarity. It also implies a deeper and disorienting truth about AI ethics principles and their justification. (shrink)

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...) would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced. (shrink)

This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified (...) substantive views about justice in outcome distributions. (shrink)

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in (...) which such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation. (shrink)

Algorithm-assisted decision procedures—including some of the most high-profile ones, such as COMPAS—have been described as unfair because they compound injustice. The complaint is that in such procedures a decision disadvantaging members of a certain group is based on information reflecting the fact that the members of the group have already been unjustly disadvantaged. I assess this reasoning. First, I distinguish the anti-compounding duty from a related but distinct duty—the proportionality duty—from which at least some of the intuitive appeal of the (...) former illegitimately derives. Second, I distinguish between different versions of the anti-compounding duty, arguing that, on some versions, uses of algorithm-assisted decision procedures rarely clash with the anti-compounding duty. Third, drawing on examples of algorithm-assisted decision procedures, I present three objections to the idea that there is a reason not to compound injustice. The most important of these is that one can compound injustice in a non-disrespectful way, and that the wrongfulness of non-disrespectfully compounding injustice is fully explained by the proportionality duty. (shrink)

John Broome has proposed a theory of fairness according to which fairness requires that agents’ claims to goods be satisfied in proportion to the relative strength of those claims. In the case of competing claims for a single indivisible good, Broome argues that what fairness requires is the use of a weighted lottery as a surrogate to satisfying the competing claims: the relative chance of each claimant's winning the lottery should be set to the relative strength of each claimant's claim. (...) In this journal, James Kirkpatrick and Nick Eastwood have objected that the use of weighted lotteries in the case of indivisible goods is unacceptable. In this article, I explain why Kirkpatrick and Eastwood's objection misses its mark. (shrink)

Computer scientists have made great strides in characterizing different measures of algorithmic fairness, and showing that certain measures of fairness cannot be jointly satisfied. In this paper, I argue that the three most popular families of measures - unconditional independence, target-conditional independence and classification-conditional independence - make assumptions that are unsustainable in the context of an unjust world. I begin by introducing the measures and the implicit idealizations they make about the underlying causal structure of the contexts in which they (...) are deployed. I then discuss how these idealizations fall apart in the context of historical injustice, ongoing unmodeled oppression, and the permissibility of using sensitive attributes to rectify injustice. In the final section, I suggest an alternative framework for measuring fairness in the context of existing injustice: distributive fairness. (shrink)

With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be _fair_. Rather they express (...) different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account. (shrink)

Biodiversity scientists often describe their field as aiming to save life and humanity, but the field has yet to reckon with the history and contemporary practices of colonialism and systematic racism inherited from natural history. The online data portals scientists use to store and share biodiversity data are a growing class of organizations whose governance can address or perpetuate and further institutionalize the implicit assumptions and inequitable social impacts from this extensive history. In this context, researchers and Indigenous Peoples are (...) developing and implementing new strategies to examine and change assumptions about which agents should count as salient participants to scientific projects, especially projects that build and manage large digital data portals. Two notable efforts are the FAIR and CARE Principles for scientific data management and governance. To characterize how these influence the governance of biodiversity data portals, we develop an account of fitness-for-use that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity data projects, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about salient participants to data science. (shrink)

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we (...) provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. (shrink)

Distribution of responsibility is one of the main focus areas in discussions about climate change ethics. Most of these discussions deal with the distribution of responsibility for climate change mitigation at the international level. The aim of this paper is to investigate if and how these principles can be used to inform the search for a fair distribution of responsibility for climate change adaptation on the local level. We found that the most influential distribution principles on the international level (...) were in turn built on one or more of seven basic principles: (P1) equal shares, (P2) desert, (P3) beneficiary pays, (P4) ability, (P5) self-help, (P6) limited responsibility for the worst off, and (P7) status quo preservation. It was found that all the basic principles, but P1, P3, and P7, are to some extent translatable to local climate adaptation. Two major problems hamper their usefulness on the local level: (1) several categories of agents need to take on responsibility; and (2) emissions do not work as a base for all principles. P4, P5, and P6 are applicable to local adaptation without changes. P4 is of particular importance as it seems to solve the first problem. P2 is applicable only if the second problem is solved, which can be achieved by using risk of harm instead of emissions as the basis for desert. (shrink)

The “impossibility results” in algorithmic fairness suggest that a predictive model cannot fully meet two common fairness criteria – sufficiency and separation – except under extraordinary circumstances. These findings have sparked a discussion on fairness in algorithms, prompting debates over whether predictive models can avoid unfair discrimination based on protected attributes, such as ethnicity or gender. As shown by Otto Sahlgren, however, the discussion of the impossibility results would gain from importing some of the tools developed in the philosophical literature (...) on feasibility. Utilizing these tools, Sahlgren sketches a cautiously optimistic view of how algorithmic fairness can be made feasible in restricted local decision-making. While we think it is a welcome move to inject the literature on feasibility into the debate on algorithmic fairness, Sahlgren says very little about what are the general gains of bringing in feasibility considerations in theorizing algorithmic fairness. How, more precisely, does it help us make assessments about fairness in algorithmic decision-making? This is what is addressed in this Reply. More specifically, our two-fold argument is that feasibility plays an important but limited role for algorithmic fairness. We end by offering a sketch of a framework, which may be useful for theorizing feasibility in algorithmic fairness. (shrink)

Recently, there has been much discussion of ‘fair machine learning’: fairness in data-driven decision-making systems (which are often, though not always, made with assistance from machine learning systems). Notorious impossibility results show that we cannot have everything we want here. Such problems call for careful thinking about the foundations of fair machine learning. Sune Holm has identified one promising way forward, which involves applying John Broome's theory of fairness to the puzzles of fair machine learning. Unfortunately, his (...) application of Broome's theory appears to be fatally flawed. This article attempts to rescue Holm's central insight – namely, that Broome's theory can be useful to the study of fair machine learning – by giving an alternative application of Broome's theory, which involves thinking about fair machine learning in counterfactual (as opposed to merely statistical) terms. (shrink)

The use of polyurethane swimsuits at the 2009 World Aquatics Championships resulted in world records being set for almost all swimming events. This paper explores the implications that the use of these performance-enhancing swimsuits had on fairness in relative and absolute outcomes in swimming. I claim that the use of ?super swimsuits? unfairly influenced relative outcomes within the competition because not all swimmers used, or had access to, the same types of swimsuit (some of which were clearly ?faster? than others). (...) Furthermore, I claim that the use of performance-enhancing swimsuits by current competitors unfairly influences inter-temporal relative outcomes. It is evident that competitors and coaches in elite swimming place great importance on making inter-temporal comparisons on the basis of absolute outcomes in the sport (as measured by a swimmer's race time). A ban on the use of super swimsuits may be justified in order to preserve the fairness of such inter-temporal comparisons, and records set using performance-enhancing swimsuits at the 2009 World Championships should be demarcated from others. (shrink)

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; (...) that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair. (shrink)

Weighted lottery proposals give guidance in rescue dilemma situations by balancing the demands of comparative and absolute fairness. While they do not advocate for saving the greater number outright, they are responsive to absolute fairness insofar as they show a certain sensitivity to the numbers involved. In this paper we investigate what criterion of absolute fairness we should demand weighted lotteries to fulfill. We do so by way of critically examining what is probably the most sophisticated weighted lottery on the (...) market: Gerard Vong's Exclusive Composition-Sensitive (EXCS) lottery. We find that both the standard that seems most common in the debate, and a different standard Vong uses to criticize Jens Timmermann's Individualist Lottery are in contradiction to basic demands placed upon weighted lotteries and are therefore unsuitable as necessary conditions for absolute fairness. We instead propose a purely gradual understanding of absolute fairness. (shrink)

In 2017, Ukraine’s Parliament passed legislation establishing a single health benefit package for the entire population called the Programme of Medical Guarantees,‎ financed through general taxes and administered by a single national purchasing agency. This legislation was in line with key principles for financing universal health coverage. However, health professionals and some policymakers have been critical of elements of the reform, including its reliance on general taxes as the source of funding. Using qualitative methods and drawing on deliberative democratic theory (...) and criteria for procedural fairness, this study argues that the acceptance and sustainability of these reforms could have been strengthened by making the decision-making process fairer. It suggests that three factors limited the extent of stakeholders’ participation in this process: first, a perception among reformers that fast-paced decision-making was required because there was only a short political window for much needed reforms; second, a lack of trust among reformers in the motives, representativeness, and knowledge of some stakeholders; and third, an under-appreciation of the importance of dialogic engagement with the public. These findings highlight a profound challenge for policymakers. In retrospect, some of those involved in the reform’s design and implementation believe that a more meaningful engagement with the public and stakeholders who opposed the reform might have strengthened its legitimacy and durability. At the same time, the study shows how difficult it is to have an inclusive process in settings where some actors may be driven by unconstrained self-interest or lack the capacity to be representative or knowledgeable interlocutors. It suggests that investments in deliberative capital (the attitudes and behaviours that facilitate good deliberation) and in civil society capacity may help overcome this difficulty. (shrink)

This study examines the direct and indirect effects of interpersonal fairness on employees’ willingness-to-stay and organisation-based self-esteem through affective commitment among manufacturing workers in Tema, Ghana. Using the survey design, 300 manufacturing workers in Tema were conveniently sampled for the study. The confirmatory factor analysis and structural equation modelling were used to analyse the data. Results indicated that affective commitment partially mediated the relationship between interpersonal fairness and employees' willingness-to-stay. Affective commitment also fully mediated the interpersonal fairness- organisation based self-esteem (...) relationship. Results further showed that organisation based self-esteem partially mediated the affective commitment and willingness to stay relationship, such that, an increase in organisation-based self-esteem leads to a decrease in employees’ willingness-to-stay. These findings emphasised the roles of interpersonal fairness and affective commitment in organisations, where affective commitment increases as a result of an increase in interpersonal fairness and makes employees have an intention to stay. The findings imply that employees who are very confident and have higher organisation-based self-esteem stand lower chances of staying in their current organisations. This study is the first to examine how affective commitment transfers the effects of interpersonal fairness unto employees’ intention-to-stay among manufacturing workers in Ghana. (shrink)

Two distinct forms of fairness in sport are regularly conflated, which produces confusion in important debates concerning the participation of transgender women in female sporting contests. The distinct forms of fairness arise in two distinct forms of sporting contest: the handicap contest and the championship contest. Handicap contests seek to ‘level the playing field’ by ensuring that all participants have an equal or ‘sporting’, chance of winning. Championship contests seek to find the person or team that is best at a (...) particular event – to find a champion. Each form of contest makes use of female categories, although for different reason. Arguments for and against the use of handicapping to ensure fairness in championship contests such as the Olympics conflate these forms of contest and their distinct accounts of fairness. (shrink)

This paper explores how fair trade social enterprises manage paradoxes in stakeholder-oriented governance models. We use narrative accounts from board members, at governance events and board documents to report an exploratory study of paradoxes in three FTSEs which are partly farmer-owned. Having synthesized the key social enterprise governance literature and framed it alongside the broader paradox theory, we used narratives to explore how tensions are articulated, how they can be applied within an adapted paradox framework, and how governance actors (...) seek to mitigate paradoxes. The paper contributes to current debates in social enterprise scholarship concerning hybridity :455–476, 2010; in Institutional logics in action, Part B, 2012), hybrid organizing :397–441, 2014) and operational tensions :407–442, 2013) by illustrating empirically how the central social/enterprise paradox manifests in FTSEs governance arrangements. We build on the paradox categories proposed by Lüscher and Lewis :221–240, 2008) and adapted in Smith et al. :407–442, 2013) by developing a recursive model of legitimacy-seeking governance processes, conceptualizing how boards seek to mitigate, but not necessarily resolve, paradoxes. (shrink)

Using a fair allocation approach, this paper identifies and examines important concerns arising from the Philippines’ COVID‐19 response while focusing on difficulties encountered by various sectors in gaining fair access to needed societal resources. The effectiveness of different response measures is anchored on addressing inequities that have permeated Philippine society for a long time. Since most measures that are in place as part of the COVID‐19 response are meant to be temporary, these are unable to resolve the inequities (...) that have led to the magnitude of morbidity and mortality associated with the pandemic. These cannot improve the country’s readiness to deal with pandemics and other emergencies in the future.Transition to a new normal recognizes the possibility that other infectious diseases could come and endanger our health security. Our pandemic experiences are proving that having an egalitarian society will serve the interests not only of disadvantaged sectors but also of everybody else, including the privileged. Response measures should thus take the opportunity to promote equity by giving importance to the concerns of the underprivileged and vulnerable while giving preference to initiatives that can be sustained beyond the period of the current pandemic. (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)

The fairness of agricultural markets is frequently invoked, especially by farmers. But fairness is difficult to define and measure. In this paper we link fairness and power with the concept of constrained choice to develop a framework for assessing fairness in agricultural markets. We use network exchange theory to define power from the dependencies that exist in agricultural networks. The structure of agricultural networks and the options that agricultural producers have to participate in agricultural networks affect the degree to which (...) they are dependent on others within the network. Dependency, in turn, affects the choices that agricultural producers have. We consider both the number and nature of these choices. We argue that constraining or limiting choices—both in number and type—violates principles of justice. Importantly, network exchange theory provides a method for assessing constraints in choices and, hence, the fairness of agricultural markets. Such an assessment could potentially lead to new policies that safeguard the liberties of marketplace participants. We present a brief case to illustrate how this framework can inform on the fairness of agricultural markets and conclude with considerations of what this means for policy, particularly in the arena of anti-trust. (shrink)

Fair grading is modeled on two fundamental principles. The first principle is that grading should be impartial and consistent. The second principle is that a fair grade should be based on the student’s competence in the academic content of the course. I derive corollary principles of fair grading from these two basic principles and use them to evaluate common grading practices. I argue that exempting students from completing certain grade components is unfair, as is grading on attendance, (...) class rank, deportment,tardiness, effort, institutional values, moral virtues such as cheerfulness and helpfulness, and other non-course-content criteria. (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)

Undergraduates (total N = 185) were asked about performance-affecting drugs. Some drugs supposedly affected athletic performance, others memory, and others attention. Some improved performance for anyone who took them, others for the top 10% of performers, others for the bottom 10%, and finally, yet other drugs worked only on the bottom 10% who also showed physical abnormalities. Participants were asked about the fairness of allowing the drug to be used, about banning it, and about whether predictions of future performance based (...) on testing with or without the drug were better. The study found that participants appreciated the "interaction effect", that they felt it was less unfair to allow the drug if it affected the bottom 10% than if it affected everyone, and they were more eager to have the drug banned if it affected everyone. Participants were least tolerant of drugs that affected athletic performance and most tolerant of those that affected attention. (shrink)

One of the main lines of research in algorithmic fairness involves individual fairness (IF) methods. Individual fairness is motivated by an intuitive principle, similar treatment, which requires that similar individuals be treated similarly. IF offers a precise account of this principle using distance metrics to evaluate the similarity of individuals. Proponents of individual fairness have argued that it gives the correct definition of algorithmic fairness, and that it should therefore be preferred to other methods for determining fairness. I argue that (...) individual fairness cannot serve as a definition of fairness. Moreover, IF methods should not be given priority over other fairness methods, nor used in isolation from them. To support these conclusions, I describe four in-principle problems for individual fairness as a definition and as a method for ensuring fairness: (1) counterexamples show that similar treatment (and therefore IF) are insufficient to guarantee fairness; (2) IF methods for learning similarity metrics are at risk of encoding human implicit bias; (3) IF requires prior moral judgments, limiting its usefulness as a guide for fairness and undermining its claim to define fairness; and (4) the incommensurability of relevant moral values makes similarity metrics impossible for many tasks. In light of these limitations, I suggest that individual fairness cannot be a definition of fairness, and instead should be seen as one tool among several for ameliorating algorithmic bias. (shrink)

This paper uses John Rawls' theory of justice to defend the patent system against charges that it has an unfair effect on access to medications, from the perspective of national and international justice. The paper argues that the patent system is fair in a national context because it respects intellectual property rights and it benefits the least advantaged members of society by providing incentives for inventors, investors, and entrepreneurs. The paper also argues that the patent system is fair (...) in an international context, provided that developed nations take steps to help disease-stricken countries secure internal justice. Fairness in a national or international context also requires that the patent system should include emergency exceptions to deal with short-term inequities. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals (...) of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

Epistemological differences fuel continuous and frequently divisive debates in the social sciences and the humanities. Sociologists have yet to consider how such differences affect peer evaluation. The empirical literature has studied distributive fairness, but neglected how epistemological differences affect perception of fairness in decision making. The normative literature suggests that evaluators should overcome their epistemological differences by ‘‘translating’’ their preferred standards into general criteria of evaluation. However, little is known about how procedural fairness actually operates. Drawing on eighty-one interviews with (...) panelists serving on five multidisciplinary fellowship competitions in the social sciences and the humanities, we show that Evaluators generally draw on four epistemological styles to make arguments in favor of and against proposals. These are the constructivist, comprehensive, positivist, and utilitarian styles; and Peer reviewers define a fair decision-making process as one in which panelists engage in ‘‘cognitive contextualization,’’ that is, use epistemological styles most appropriate to the field or discipline of the proposal under review. (shrink)

The certification-based Fair Trade initiative has been steadily growing during the last two decades. While many scholars have analyzed its main characteristics and developments, only a few have assessed it against a concept of justice. And those exceptional cases have only focused on distributive justice, proving unable to grasp the important ethical elements that Fair Trade integrates in its project. In reaction to this, this article intends to critically examine what the Fair Trade movement proposes to be (...) ‘fair’ by resorting to the thought of the French philosopher Emmanuel Lévinas. To accomplish this goal, a new understanding of his conception of justice is presented, one that seeks to overcome the limitations that the two most common interpretations in the literature suffer. The idea of Lévinas’ ‘dialectics of justice’ is used to discuss Fair Trade’s relation of alterity, its appropriation of the notion of ‘face’ and its commitment to and responsibility for marginalized producers and workers. This analysis shows that Fair Trade operates with what could be described as a fetishized understanding of Levinasian ethics that justifies a deeply unjust praxis. (shrink)

The growth in demand for corporate social responsibility information raises the question of how various CSR disclosure items are used by investors, an important stakeholder group driven by instrumental, moral, and relational motives. Prior research examines the instrumental motive to maximize individual shareholder wealth and the moral motive to actualize personal stewardship interests. We contribute to the literature by examining investors’ relational motive to realize positive stakeholder relationships within and between organizations and communities. The relational motive arises when investors look (...) at a company’s treatment of other stakeholder groups as a heuristic to form a perception of how fairly they will also be treated by that company in the future, and thus invest in the company they perceive as fair. Fair treatment in the future matters to the investor who purchases stock from the company or via the capital markets in exchange for becoming a shareholder and thus a residual claimant of the company. As such, the investor expects future cash flows from holding and/or reselling the stock and expects to be treated fairly by the company in the future. We propose that investors, use as a fairness heuristic, CSR disclosure items—CSR investment level or CSR assurance—that represent the company’s commitment to its stakeholders, and that the resulting fairness perception affects the extent to which the CSR disclosure items influence their investment decision. Using responses from 113 investors in an online experiment, we find that fairness perceptions are higher when CSR investment is above the industry average, and that fairness perceptions partially mediate the impact of the CSR investment level on investment amount allocations. We do not find that the presence of CSR assurance is used by investors as a fairness heuristic. Our results are robust to controlling for preferences for financial performance and hence investors’ instrumental motive, and to controlling for individual environmental attitudes, and hence investors’ moral motive. Implications for future research and public policy are discussed. (shrink)

A number of fairness issues and principles are developed and discussed from the context of personnel selection. It is noted that not too much attention has been paid to these issues and concerns in the past. A distinction is made between justice and fairness having to do with the procedural components and processes of selection, the nature of the information used to make selection decisions, and the resulting outcomes of the selection process. Ideas for future research and exploration are also (...) extended. (shrink)