Markus Furendal

This article develops and defends a novel argument for why individuals have a duty to contribute to the realisation of justice by making a productive contribution. Analysing the shortcomings of attempting to ground such a duty in either reciprocity or the idea of sharing burdens, I suggest that the best alternative is a hybrid account which draws on both. The account says that firstly, everyone must make an equally good effort, in relation to their ability, to contribute the socially necessary labour needed to realise a just state of affairs, where everyone receives what they are due. Secondly, if and when this level is reached, everyone has a conditional obligation to further benefit others, if they benefit from the additional work of others. Opting out is thus permissible, since this part is only triggered if one wants to take part of the social surplus produced by others.

The emerging global governance of artificial intelligence (AI) is shaped by numerous political actors. Inviting non-state actors into such processes is typically assumed to address a perceived democratic deficit, by promoting increased representation, transparency, and openness. In the AI sphere, however, non-state actors include the same multinational companies that develop the technology to be regulated. Surprisingly, the task of normatively theorizing the democratic role of non-state actors in global AI governance has nevertheless been largely ignored. This paper addresses this by specifying, first, under what conditions non-state actors contribute to the actual democratization of global AI governance, as ‘democratic agents’, and second, under what conditions they instead contribute to the strengthening of the prerequisites for future democratization, as ‘agents of democracy’. We conclude that, although few non-state actors are authorized to act as ‘democratic agents’, their exercise of ‘moral’, ‘epistemic’, and ‘market authority’ could make them legitimate ‘agents of democracy’.

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.

AI technology promises to be both the most socially important and the most profitable technology of a generation. At the same time, the control over – and profits from – the technology is highly concentrated to a handful of large tech companies. This chapter discusses whether bringing AI technology under collective ownership and control is an attractive way of counteracting this development. It discusses justice-based rationales for collective ownership, such as the claim that, since the training of AI systems relies on a form of enclosing of the data commons, the value created by those systems should be fairly distributed. It also considers democracy-based rationales, like the suggestion that collective ownership is needed to ensure democratic control over the way this crucial technology is being developed and deployed. The paper also discusses possible forms of collective ownership, like publicly funded advanced AI research and democratically controlled AI companies. It concludes that the case for shifting to a model with collective ownership is the most compelling when based on the concern that the private model could come to concentrate economic and political power to such a degree that it threatens institutions designed to promote justice and democracy.

Work has always been a significant source of ethical questions, philosophical reflection, and political struggle. Although the future of work in a sense is always at stake, the issue is particularly relevant right now, in light of the advent of advanced AI systems and the collective experience of the COVID-19 pandemic. This has reinvigorated philosophical discussion and interest in the study of the future of work. The purpose of this survey article is to provide an overview of the emerging philosophical field that engages with the future of work, with a special focus on equality and justice, and to outline a research agenda that can help the field to develop further. Section 2 provides some historical context for the current surge in interest in the topic. Then, we discuss what work is and whether there is a philosophy of work (Section 3). The four main sub-debates we then turn to concern the value of work (Section 4), distributive justice and work (Section 5), productive justice (Section 6), and institutional reforms prompted by changes in how work is organized (Section 7). The final section discusses the importance of the distinction between ideal and non-ideal theory in philosophical investigations into the future of work.

The last decade has seen significant improvements in Artificial Intelligence (AI) technologies, including robotics, machine vision, speech recognition and text generation. Increasing automation will undoubtedly affect the future of work, and discussions on how the development of AI in the workplace will impact labor markets often include two scenarios: (1) labor replacement and (2) labor enabling. The former involves replacing workers with machines, while the latter assumes that human-machine cooperation can significantly improve worker productivity. In this context, it is often argued that (1) could lead to mass unemployment and that (2) therefore would be more desirable. We argue, however, that the labor-enabling scenario conflates two distinct possibilities. On the one hand, technology can increase productivity while also promoting “the goods of work,” such as the opportunity to pursue excellence, experience a sense of community and contribute to society (human augmentation). On the other hand, higher productivity can also be achieved in a way that reduces opportunities for the “goods of work” and/or increases “the bads of work,” such as injury, reduced physical and mental health, reduction of autonomy, privacy and human dignity (human stunting). We outline the differences of these outcomes and discuss the implications for the labor market in the context of contemporaneous discussions on the value of work and human wellbeing.

Tekniska framsteg inom Artificiell Intelligens (AI) väcker en rad frågor av moralisk och politisk karaktär kring hur AI-system bör användas, och hur de kan påverka våra samhällen och sociala relationer. Denna artikel ger en överblick över några centrala frågor inom det framväxande fält som kallas AI-etik, utifrån fyra nyligen utgivna böcker. Inom AI-etik har särskilt fokus ofta riktats mot det så kallade likriktningsproblemet: hur vi ska få en AI att frambringa ett utfall som ligger i linje med vad vi vill. I detta unga fält kan emellertid politisk-filosofiska bidrag tillhandahålla en bredare och mer nyanserad analysapparat kring AI-etiska frågor, vilket i sin tur kan leda till teoretiska framsteg.

Many contemporary political movements focus more on changing the values and principles that people act on in their daily lives rather than institutions and legal frameworks. Political-philosophical theories of justice, however, often focus more on the Rawlsian “basic structure” than the “ethos” of a just society, and rarely discuss how individuals may be encouraged to act in accordance with principles of justice. This article attempts to redress this, and draws on moral, social and political philosophy to argue that an ethos exists in a group or society either i) when its members internalize and act from a particular principle, or ii) when its members uphold a decentralized system of informal social sanctions that increases their compliance with a particular principle.

This article makes the case for a specific variety of what we call Collective Capital Institutions (CCIs), by returning to the idea of Wage-Earner Funds (WEFs) – a 1970s Swedish policy proposal designed gradually to shift ownership and control over parts of the economy to democratically controlled institutions. We identify two attractive rationales in favour of such a scheme and argue that both can fruitfully be transposed to the current worldwide economic situation. The egalitarian rationale is that WEFs could help in the pursuit of equality by giving a wider set of people a stake in collectively owned companies and a right to their profits. The democratic rationale is that WEFs redistribute not only these profits, but also the power over economic decisions made within companies. We then contrast such schemes for collective capital ownership with the similar but much more privatised proposals set out in, for instance, John Rawls's idea of a ‘property-owning democracy’. We argue that CCIs ultimately are more likely to contribute to the development of the ‘sense of justice’ within society that is needed for a stable just society. We conclude that CCIs deserve a great deal more exploration in academic and political discussions of egalitarian economic systems.

Although the concept of “AI governance” is frequently used in the debate, it is still rather undertheorized. Often it seems to refer to the mechanisms and structures needed to avoid “bad” outcomes and achieve “good” outcomes with regard to the ethical problems artificial intelligence is thought to actualize. In this article we argue that, although this outcome-focused view captures one important aspect of “good governance,” its emphasis on effects runs the risk of overlooking important procedural aspects of good AI governance. One of the most important properties of good AI governance is political legitimacy. Starting out from the assumptions that AI governance should be seen as global in scope and that political legitimacy requires at least a democratic minimum, this article has a twofold aim: to develop a theoretical framework for theorizing the political legitimacy of global AI governance, and to demonstrate how it can be used as a compass for critially assessing the legitimacy of actual instances of global AI governance. Elaborating on a distinction between “governance by AI” and “governance of AI” in relation to different kinds of authority and different kinds of decision-making leads us to the conclusions that much of the existing global AI governance lacks important properties necessary for political legitimacy, and that political legitimacy would be negatively impacted if we handed over certain forms of decision-making to artificial intelligence systems.

The creation of increasingly complex artificial intelligence (AI) systems raises urgent questions about their ethical and social impact on society. Since this impact ultimately depends on political decisions about normative issues, political philosophers can make valuable contributions by addressing such questions. Currently, AI development and application are to a large extent regulated through non-binding ethics guidelines penned by transnational entities. Assuming that the global governance of AI should be at least minimally democratic and fair, this paper sets out three desiderata that an account should satisfy when theorizing about what this means. We argue, first, that an analysis of democratic values, political entities and decision-making should be done in a holistic way; second, that fairness is not only about how AI systems treat individuals, but also about how the benefits and burdens of transformative AI are distributed; and finally, that justice requires that governance mechanisms are not limited to AI technology, but are incorporated into a range of basic institutions. Thus, rather than offering a substantive theory of democratic and fair AI governance, our contribution is metatheoretical: we propose a theoretical framework that sets up certain normative boundary conditions for a satisfactory account.

If there is a duty of justice to contribute to society, which asks individuals to produce a specific amount of goods and services that can be redistributed, we need a decision-procedure to know when we have done our part. This paper analyses and critically assesses the commonly suggested decision-procedure of relying on market prices to measure the value of one’s contribution. It is usually assumed that a high salary indicates that one’s talents are put to good use, but this presupposes both that market prices of labour are correct reflections of supply and demand, and that market prices are correct reflections of social value. I criticise both assumptions and argue that the social value of a contribution cannot simply be a function of its market value, but is also influenced by the principles of justice that support the duty to contribute. Further, the market solution is incapable of valuing contributions that lack market prices, like non-marketised care labour. The market solution thus fails as a decision-procedure under other than special circumstances. This does not mean, however, that we need to give up the idea of a duty to contribute.

G. A. Cohen has argued that egalitarian justice proscribes equality-upsetting economic incentives, but that individuals nevertheless are required to make a sufficiently large productive contribution to society. This article argues, however, that Cohen’s claim that justice is insensitive to Pareto concerns and simply is equality, undermines such a duty. In fact, Cohen cannot say that justice prefers a distribution where everyone is equally well off to one where everyone is equally badly off. Individuals hence cannot have a duty of justice to use their talents at a more productive level. This indifference risks removing Cohen’s egalitarianism’s appeal as an alternative to the Rawlsian position it challenges. Several ways of avoiding this problem are proposed and evaluated. Ultimately, it is argued that such a duty to contribute must instead be based on a concern for both equality and human flourishing. The ethos Cohen defends must hence be made pluralist, and encourage a commitment to both principles.