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How can the harm caused by waves of fake news or derogatory speech on social media be minimized without unduly limiting freedom of expression? I draw on an eighteenth-century debate for thinking about this problem: Hume and Smith present two different models of the transmission of emotions and ideas. Empathetic processes are causal, almost automatic processes; sympathy, in contrast, means putting oneself into the other person’s position and critically evaluating how one should react. I use this distinction to argue that the architectural logic of social media should be improved to prevent cumulative harms and to facilitate sympathetic processes.

In this paper, we bring together the literature on citizen science and on deliberative democracy and epistemic injustice. We argue that citizen science can be seen as one element of “deliberative systems,” as described by Mansbridge et al. But in order to fulfil its democratic potential, citizen science needs to be attentive to various forms of exclusion and epistemic injustice, as analyzed by Fricker, Medina and others. Moreover, to tap the potentials of citizen science from the perspective of deliberative democracy, it needs to move towards a more empowered approach, in which citizens do not only deliver data points, but also, in invited or uninvited settings, participate in discussions about the goals and implications of research. Integrating citizen science into the deliberative systems approach embeds it in a broader framework of democratic theory and suggests the transmission of certain practical strategies (e.g., randomized sampling). It can also contribute to realism about both the potentials and the limits of citizen science. As part of a deliberative system, citizen science cannot, and need not, be the only place in which reforms are necessary for creating stronger ties between science and society and for aligning science with democratic values.

This Article examines the right to be acknowledged as the first inventor of a new technology in patent law. Technological inventions usually result from cumulative research and development, and several people sometimes arrive at the same invention almost simultaneously. However, only one person is usually considered to be the “inventor,” and receives all the credit and honor. This Article focuses on the legal systems of Germany and the United States, comparing how they conceptualize the right to be seen as inventor. These systems have developed in substantially different philosophical and cultural climates: while the German legal system has been deeply influenced by Kantian and Hegelian thought, the American legal system has been inspired more strongly by liberal and utilitarian ideas. These two schools of philosophical thought have different perspectives on the relationship between personal identity and work; while the German tradition emphasizes the deeply personal relation between individuals and their work, the Anglo-Saxon approach is, in general, more instrumentalist and utilitarian with regard to work. One way in which these differences express themselves is the different ways in which the right to be acknowledged as the first inventor is understood and regulated. The right to be acknowledged as the first inventor is deeply connected with one’s identity as a professional, whether an engineer, technician, or scientist. On the other hand, this right does not necessarily have pecuniary significance. Hence, the protection of the right to be considered as the first inventor allows a glimpse into the different visions of identity and work found in these legal systems. This Article examines to what extent German and American legal systems recognize and protect the right to be perceived as the first inventor. It demonstrates that the two legal systems differ profoundly in the ways they perceive and protect the right to be considered as the first inventor. True to its visions of professional dignity, German law carefully protects this right, independently from any pecuniary interests. In contrast, American law grants a remarkably weak protection to the right to be considered as the first inventor, focusing primarily on its monetary aspects. Hence, one can here discover different visions of the role of individuals in society, and specifically of the role of individuals as creators and not just consumers. What is at stake here is whether questions of honor, dignity, and symbolic property, above and beyond material benefits, are recognized as playing a role in the economic system.

Recent discussions of accountability in contexts of expert knowledge raise questions about the limits of transparency. Against this background, we discuss the nexus between expert knowledge and meaningful accountability – that is, context-sensitive accountability based on a genuine understanding of a situation. We argue that the concentration of expertise in certain institutions makes it difficult to hold those institutions accountable. In particular, three components challenge meaningful accountability: specialization, inaccessibility and potential biases or conflict of interest. We emphasize the role of ‘epistemic communities’ and their impact on the tension between expert knowledge and independence. Drawing on the deliberative systems literature, we discuss how expert knowledge might be communicated to outsiders to enable meaningful accountability. To illustrate our argument, we draw on the European Central Bank, a case study in which states have chosen a delegation design characterized by a high degree of independence and trust in expert knowledge, to the detriment of accountability. We sketch possible avenues for creating the conditions for meaningful accountability even in the case of institutions with highly concentrated expertise.

ZusammenfassungDie republikanische Tradition hat eine natürliche Affinität zu einem Verständnis von Demokratie als nicht nur repräsentativ, sondern auch partizipativ. Ohne die Bedeutung repräsentativer Strukturen leugnen zu wollen, betont diese Richtung demokratischen Denkens die Einbindung aller Bürger:innen in demokratische Prozesse, nicht nur im politischen Geschehen auf großer (sprich nationaler) Bühne, sondern auch auf allen Ebenen föderaler Strukturen und auch in Bereichen des Lebens, die oft nicht als „politisch“ im engeren Sinne gefasst werden, zum Beispiel der Zivilgesellschaft und der Wirtschaft. Denn nur, wenn Demokratie im Alltag der Einzelnen, in ihren Begegnungen untereinander und in der Gestaltung ihrer gemeinsamen Praktiken und Regeln verwurzelt ist, kann eine wirklich demokratische Haltung entstehen, die das Versprechen einlöst, nicht der Willkür anderer unterworfen zu sein.

In more and more contexts, human decision-making is replaced by algorithmic decision-making. While promising to deliver efficient and objective decisions, algorithmic decision systems have specific weaknesses, some of which are particularly dangerous if data are collected and processed by profit-oriented companies. In this paper, I focus on two problems that are at the root of the logic of algorithmic decision-making: (1) (in)tolerance for ambiguity, and (2) instantiations of Campbell’s law, i. e. of indicators that are used for “social decision-making” being subject to “corruption pressures” and tending to “distort and corrupt” the underlying social processes. As a result, algorithmic decision-making can risk missing the point of the social practice in question. These problems are intertwined with problems of structural injustice; hence, if algorithms are to deliver on their promises of efficiency and objectivity, accountability and critical scrutiny are needed.

Democratic societies need media that uphold journalistic standards of truthfulness and objectivity. But sensationalism has always been a temptation for journalists, and given the intense competition between news outlets, especially in the online world, there is pressure on them to 'chase the clicks.' The article analyzes the incentive structures for journalists - focusing on the harmfulness of sensationalist framing as an example - and the challenges of establishing shared standards in a highly competitive online environment. Drawing on concepts and arguments from business ethics, it argues that the structure of this problem points to the need for an 'ethics of sportsmanship' that upholds journalistic standards despite competitive pressures. But the specific role and nature of the media imply that there can be no once-and-for-all solution. Instead, there is a need for reflexivity, that is, for an ongoing dialogue about journalistic standards and the role of media in democratic societies.

This commentary, part of the book symposium on Katharina Pistor’s The Code of Capital, focuses in particular on the epistemic dimensions of the phenomena she describes. Knowledge can be coded as capital, through intellectual property rights, and these can be done in ways that unjustly favor privileged actors at the cost of the common good, as Pistor demonstrates with regard to the patenting of genetic markers. Interestingly, however, knowledge can also be appropriated without the use of legal tools, simply by appropriating data from consumers, as Zuboff, for example, has argued. A second epistemic dimension of Pistor’s work lies in the fact that the legal coding of capital is itself little know and receives little public discussion. This, of course, plays into the hands of those who benefit by problematic forms of such coding. By putting these issues up for debate, The Code of Capital fulfill itself an important epistemic role, which can be categorized as a form of "democratic professionalism", i.e., professionals enabling a critical public discourse about the activities in their own field.

More and more decisions in our societies are made by algorithms. What are such decisions like, and how do they compare to human decision-making? I contrast central features of algorithmic decision-making with three key elements—plurality, natality, and judgment—of Hannah Arendt's political thought. In “Arendtian practices,” human beings come together as equals, exchange arguments, and make joint decisions, sometimes bringing something new into the world. With algorithmic decision-making taking over more and more areas of life, opportunities for “Arendtian practices” are under threat. Moreover, there is the danger that algorithms are tasked with decisions for which they are ill-suited. Analyzing the contrast with Arendt's thinking can be a starting point for delineating realms in which algorithmic decision-making should or should not be welcomed.

This paper discusses a novel kind of argument for assessing the moral significance of acts of lying and misleading. It is based on considerations about valuable social norms that might be eroded by these actions, because these actions function as signals. Given that social norms can play an important role in supporting morality, individuals have a responsibility to preserve such norms and to prevent ‘cultural slopes’ that erode them. Depending on whether there are norms against lying, misleading, or both, and how likely it is that they might be eroded, these actions can thus have different moral significance. In cases in which the rule ‘do not lie’, as a relatively simple rule, functions as a ‘focal point’, acts of misleading are often morally preferable. In other words, in such cases the possibility of ‘cultural slopes’ can ground a context-dependent slippery slope argument for a moral difference between lying and misleading.

This short introduction presents the theme of the special issue and provides a preview of the articles.

The paper introduces the notion of 'digital behavioral technologies' and discusses them from the perspectives of vulnerability and justice, thereby integrating perspectives from bioethics or public health ethics and political philosophy. Digital behavioral technologies have seen a massive uptake in recent years, but the market for them is hardly regulated. We argue that understanding the impact of digital behavioral technologies requires understanding individuals not as abstract, atomized agents, but rather to take their embeddedness into social structures into account. This also allows extending the focus to groups, relationships and whole societies, which are often structurally unjust. This perspective provides a corrective to an overly individualistic consideration of digital behavioral technologies, which may suggest itself because of their focus on individual bodies. We point out some implications of this integrated approach with regard to the regulation of digital behavioral technologies. We conclude by describing some implications both for those who work on digital behavioral technologies and for those who work on questions of vulnerability and justice.

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression which fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semi-structured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both, tabular clinical and brain imaging data, are useful for functional outcome prediction, while models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semi-structured data and that they have the potential to support clinical decision making., Preprint under review

What do human beings do when they work, how is work organized, and what are its economic, social, political, biographical, ecological effects? We cannot answer these questions without drawing on the numerous categories that we use to describe work, such as "skilled" or "unskilled" work, "domestic work" or "wage labor," "gig work" or "platform work." But where do these categories come from, what are their histories, how do they differ between countries, and how are they evolving? Shifting Categories of Work asks these questions, illuminating the many ways in which our societies categorize work.

The chapter discusses the problem of algorithmic bias in decision-making processes that determine access to opportunities, such as recidivism scores, college admission decisions, or loan scores. After describing the technical bases of algorithmic bias, it asks how to evaluate them, drawing on Iris Marion Young’s perspective of structural (in)justice. The focus is in particular on the risk of so-called ‘Matthew effects’, in which privileged individuals gain more advantages, while those who are already disadvantaged suffer further. Some proposed solutions are discussed, with an emphasis on the need to take a broad, interdisciplinary perspective rather than a purely technical perspective. The chapter also replies to the objection that private firms cannot be held responsible for addressing structural injustices and concludes by emphasizing the need for political and social action.

This article considers how legal systems capture different cultural perceptions of work in an individual’s life. We inquire how two models—“human capital,” based on the works of Adam Smith; and “vocation,” based on the works of G. W. F. Hegel—are reflected in legal regulations and judicial rhetoric in the United States and Germany. Specifically, we examine how these two legal systems treat the practice of using personal names—the most direct referents to individuals’ identities—in business. We discuss three sets of cases: cases involving the use of personal names as trademarks, cases involving conflicts between parties with similar names, and cases involving the transfer of rights in personal names. The article demonstrates that the US legal system treats work as a commercial asset, as “human capital” in Smith’s sense, whereas German law perceives work as an integral part of one’s identity, echoing the Hegelian line of “vocation.”

Can the alleged conflict between efficiency and justice in the debate about workplace democracy be overcome? We argue that this might be possible by applying arguments from epistemic democratic the...

This paper discusses global reserve currencies from the perspective of structural global justice. Drawing on notions of structural justice and background justice, it suggests that the structures of...

Distinct patient characteristics have been proposed for ischaemic stroke in the anterior versus posterior circulation. However, data on functional outcome according to stroke territory in patients with acute stroke treatment are conflicting and information on outcome predictors is scarce. In this retrospective study, we analysed functional outcome in 517 patients with stroke and thrombolysis and/or thrombectomy treated at the University Hospital Zurich. We compared clinical factors and performed multivariate logistic regression analyses investigating the effect of outcome predictors according to stroke territory. Of the 517 patients included, 80 (15.5%) suffered a posterior circulation stroke (PCS). PCS patients were less often female (32.5% vs. 45.5%, p = 0.031), received thrombectomy less often (28.7% vs. 48.3%, p = 0.001), and had lower median admission NIHSS scores (5 vs. 10, p 0.001) as well as a better median three months functional outcome (mRS 1 vs. 2, p = 0.010). Predictors for functional outcome were admission NIHSS (OR 0.864, 95% CI 0.790–0.944, p = 0.001) in PCS and age (OR 0.952, 95% CI 0.935–0.970, p 0.001), known symptom onset (OR 1.869, 95% CI 1.111–3.144, p = 0.018) and admission NIHSS (OR 0.840, 95% CI 0.806–0.876, p 0.001) in ACS. Acutely treated PCS and ACS patients differed in their baseline and treatment characteristics. We identified specific functional outcome predictors of thrombolysis and/or thrombectomy success for each stroke territory.

In this paper we argue that liberal-egalitarian theorists of justice should take power, especially economic power, seriously and make it explicit. We argue that many theories of justice have left power implicit, relying on what we call the “primacy of politics” model as a background assumption. However, this model does not suffice to capture the power relations of today’s globalized world, in which the power of nation states has been reduced and material inequality has sky-rocketed. We suggest replacing it by a “political economy” model that emphasizes the possibility of self-reinforcing cycles. Doing so has direct implications for how to theorize justice, not only on the non-ideal, but also on the ideal level.

In recent years, a plethora of well-known data scandals has led to calls for alternative forms of social media governance. What challenges of institutional design would have to be met for developing meaningful democratic governance structures for a social media platform? Intertwining philosophical and technological considerations, this article explores the possibility and feasibility of democratically governed social media. We focus on the necessary technological requirements that guarantee secure voting for social media user participation. While we provide several arguments in favor of democratically governed social media from within, we argue that it should not be considered as an alternative to social media regulation from the outside.

Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning (DL) models are state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered and lack interpretability. In contrast, classical ordinal regression models consider the outcome's order and yield interpretable predictor effects but are limited to tabular data. We present ordinal neural network transformation models (ONTRAMs), which unite DL with classical ordinal regression approaches. ONTRAMs are a special case of transformation models and trade off flexibility and interpretability by additively decomposing the transformation function into terms for image and tabular data using jointly trained neural networks. The performance of the most flexible ONTRAM is by definition equivalent to a standard multi-class DL model trained with cross-entropy while being faster in training when facing ordinal outcomes. Lastly, we discuss how to interpret model components for both tabular and image data on two publicly available datasets., 41 pages (incl. appendix, figures and literature), 11 figures in main text, 4 figures in appendix

The Fair Priority Model offers a practical way to fulfill pledges to distribute vaccines fairly and equitably

In recent years, there has been a revival of discussions about democracy in the enterprise, not only out of a frustration about the powerlessness of the external political control of firms, but als...

Markets allow for the processing of decentralized information through the price mechanism. But in addition, many markets rely on other mechanisms in markets, or non-market institutions, that provide and manage other forms of knowledge. Within national economies, these institutions form an ‘epistemic infrastructure’ for markets. In global markets, in contrast, this epistemic infrastructure is very patchy, undermining the preconditions for morally responsible agency. New technologies might help to improve the epistemic infrastructure of global markets, but they require conceptualizing knowledge not only as a tradable good, but also as a precondition of morally responsible agency.