Politicians and engineers are increasingly realizing that values are important in the development of technological artefacts. What is often overlooked is that different conceptualizations of these abstract values lead to different design-requirements. For example, designing social media platforms for deliberative democracy sets us up for technical work on completely different types of architectures and mechanisms than designing for so-called liquid or direct forms of democracy. Thinking about Democracy is not enough, we need to design for the proper conceptualization of these (...) values. As we see it, we cannot responsibly engineer and innovate and shape technology in accordance with our moral values without engaging in systematic and continuous conceptual engineering: This is not only an academic, or theoretical issue, it is also not simply an issue for public policy or politics, or regulators, it has become a central problem for engineering and the world of technology. In this paper, we present a framework for doing the necessary conceptual work in the context of requirement engineering. We draw on the literature on conceptual engineering to lay out a methodology to (1) assess different conceptions and (2) to develop new conceptions. Moreover, we integrate this methodology with extant approaches in the philosophy of technology which aim at designing technological artefacts ethically. In the final section we apply this integrated framework to freedom in the context of social media networks. (shrink)

Relational egalitarians sometimes argue that a degree of distributive equality is necessary for social equality to obtain among members of society. In this paper, we consider how such arguments fare when extended to the intergenerational case. In particular, we examine whether relational reasons for distributive equality apply between non-overlapping generations. We claim that they do not. We begin by arguing that the most common reasons relational egalitarians offer in favour of distributive equality between contemporaries do not give us reasons to (...) object to distributive inequality between non-overlapping generations. This argument by itself however will not fully suffice to show that there are no relational reasons to care about intergenerational distributive equality, given the nature of relational equality and its requirements in the intergenerational case are likely to be qualitatively different than in the contemporary case. Therefore, we also make the positive argument that for the intergenerational case to satisfy the requirements demanded by the ideal of relational equality it suffices that future persons’ interests are meaningfully incorporated and protected in the decision-making of preceding generations, and there is no basis for a concern with distributive equality. While some have argued that the one-way and asymmetrical causal influence between non-overlapping generations means concerns of social equality are inapplicable in the intergenerational case, we argue that the ongoing nature of this influence makes concerns of social equality appropriate. If successful, the upshot of the argument is that it can be coherent to maintain a commitment to relational equality between non-overlapping generations, all while remaining agnostic about distributive equality between them. (shrink)

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three notable notions (...) of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (shrink)

Epistocracy is rule by whoever is more likely to make correct decisions. AI epistocracy is rule by an artificial intelligence that is more likely to make correct decisions than any humans, individually or collectively. I argue that although various objections have been raised against epistocracy, the most popular do not apply to epistocracy organized around AI rule. I use this result to show that epistocracy is fundamentally flawed because none of its forms provide adequate opportunity for people (as opposed to (...) individuals) to develop a record of meaningful moral achievement. This Collective Moral Achievement Objection provides a novel reason to value democracy. It also provides guidance on how we ought to incorporate digital technologies into politics, regardless of how proficient these technologies may become at identifying correct decisions. (shrink)

This article analyses the forms of dividuation workers undergo when they are linked to technologies, such as algorithms or artificial intelligence. It examines functionalities and operations deployed by certain types of Talent Management software and apps—UKG, Tribepad, Afiniti, RetailNext and Textio. Specifically, it analyses how talented workers materialise in relation to the profiles and the statistical models generated by such artificial intelligence machines. It argues that these operate as a nooscope that allows the transindividual plane to be quantified through a (...) process known as dividuation. Finally, by way of conclusion, the notion of the dividual is presented as the logic that characterises the human–machine relationship in the case of artificial intelligence and as the horizon of what Felix Guattari called “machinic capitalism”. (shrink)

Algorithmic biases, or algorithmic unfairness, have been a topic of public and scientific scrutiny for the past years, as increasing evidence suggests the pervasive assimilation of human cognitive biases and stereotypes in such systems. This research is specifically concerned with analyzing the presence of discursive biases in the text generated by GPT-3, an NLPM which has been praised in recent years for resembling human language so closely that it is becoming difficult to differentiate between the human and the algorithm. The (...) pertinence of this research object is substantiated by the identification of race, gender and religious biases in the model’s completions in recent research, suggesting that the model is indeed heavily influenced by human cognitive biases. To this end, this research inquires: How does the Natural Language Processing Model GPT-3 replicate existing social biases?. This question is addressed through the scrutiny of GPT-3’s completions using Critical Discourse Analysis (CDA), a method which has been deemed as amply valuable for this research as it is aimed at uncovering power asymmetries in language. As such, the analysis is specifically centered around the analysis of gender and race biases in the model’s generated text. Research findings suggest that GPT-3’s language generation model significantly exacerbates existing social biases while replicating dangerous ideologies akin to white supremacy and hegemonic masculinity as factual knowledge. (shrink)

The emergence of new digital technologies in modern work organizations is also changing the way employees and employers communicate, design work processes and responsibilities, and delegate. This paper takes an interdisciplinary—namely sociological and philosophical—perspective on the use of AI in healthcare work organizations. Using this example, structural power relations in modern work organizations are first examined from a sociological perspective, and it is shown how these structural power relations, decision-making processes, and areas of responsibility shift when AI is used. In (...) the subsequent ethical part, opportunities for a fairer organization of work, but also dangers due to possibly changed power relations are elaborated and evaluated by presenting a realistic scenario from everyday clinical practice. After combining a proceduralist account of organizational ethics with a virtue-ethical approach, it is argued that certain organizational and character dispositions are necessary for employers and employees to meet the challenge of changing structural power relations in the future. With the same goal, a summative sociological perspective discusses challenges to workplace co-determination. (shrink)