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 decisionmaking. 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 chapter 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.

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.

Artificial intelligence is bound to have a significant impact on societies and global politics. Given that everyone is affected by the advent of AI technology, its development and deployment should arguably be under democratic control. This essay describes the global AI governance regime complex that has developed in recent years, and discusses what should be democratically governed, who should have a say, and how this should happen. The answers to these questions depend on why we value the democratic ideal and what reasons there are to extend it to the AI domain.

Artificial intelligence is bound to have a significant impact on societies and global politics. Given that everyone is affected by the advent of AI technology, its development and deployment should arguably be under democratic control. This essay describes the global AI governance regime complex that has developed in recent years, and discusses what should be democratically governed, who should have a say, and how this should happen. The answers to these questions depend on why we value the democratic ideal and what reasons there are to extend it to the AI domain.

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’.

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. 

Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.

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.

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.