This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they (...) always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities. (shrink)

The recent proliferation of AI scandals led private and public organisations to implement new ethics guidelines, introduce AI ethics boards, and list ethical principles. Nevertheless, some of these efforts remained a façade not backed by any substantive action. Such behaviour made the public question the legitimacy of the AI industry and prompted scholars to accuse the sector of ethicswashing, machinewashing, and ethics trivialisation—criticisms that spilt over to institutional AI ethics boards. To counter this widespread issue, contributions in the literature have (...) proposed fixes that do not consider its systemic character and are based on a top-down, expert-centric governance. To fill this gap, we propose to make use of qualified informed lotteries: a two-step model that transposes the documented benefits of the ancient practice of sortition into the selection of AI ethics boards’ members and combines them with the advantages of a stakeholder-driven, participative, and deliberative bottom-up process typical of Citizens’ Assemblies. The model permits increasing the public’s legitimacy and participation in the decision-making process and its deliverables, curbing the industry’s over-influence and lobbying, and diminishing the instrumentalisation of ethics boards. We suggest that this sortition-based approach may provide a sound base for both public and private organisations in smart societies for constructing a decentralised, bottom-up, participative digital democracy. (shrink)

The evolving dynamics of the marketplace, coupled with concerns regarding the finite capacity to meet increasing demands, have led to the emergence of new phenomena and practices. These developments, while heralding significant changes in the perception and selection of products and services, also elicit substantial concerns. Greenwashing is defined as the strategic practice by which corporations create a misleading impression of their environmental initiatives. This paper examines the theoretical foundations and multifaceted nature of greenwashing, identifying key deceptive strategies such as (...) hidden trade-offs, lack of verifiable evidence, vagueness, irrelevance, and false labels. It explores the motivations behind greenwashing, which often stem from the pressure to appear environmentally responsible without incurring the costs associated with genuine sustainability. The analysis underscores the broader implications of greenwashing, including its impact on consumer trust, corporate governance, and environmental policy. Additionally, the paper addresses the emerging issue of "machinewashing," wherein companies make exaggerated claims about the ethical aspects of their AI technologies. Furthermore, the concept of environmental vices is explored, illustrating how greenwashing perpetuates unethical behaviors and undermines authentic sustainability efforts. To address these practices, the study advocates for a comprehensive approach that includes stricter regulations, increased transparency, and greater engagement from civil society. By promoting genuine sustainability and holding corporations accountable, it is possible to restore consumer trust and support authentic environmental initiatives, thereby fostering a more sustainable and ethical commerce. Keywords: greenwashing, environmental vices, ecology, machinewashing, business ethics, sustainability. (shrink)

The complex relationships between humans and AI-empowered machines have created and inspired new products and services as well as controversial debates, fiction and entertainment, and last but not least, a striving and vital field of research. The convergence between the two categories of entities has created stimulating concepts and theories in the past, such as the uncanny valley, machinization of humans through datafication, or humanization of machines, known as anthropomorphization. In this article, we identify a new gap in the relational (...) interaction between humans and AI triggered by commercial interests, making use of AI through advertisement, marketing, and corporate communications. Our scope is to broaden the field of AI and society by adding the business-society-nexus. Thus, we build on existing research streams of machinewashing and the analogous phenomenon of greenwashing to theorize about the humanwashing of AI-enabled machines as a specific anthropomorphization notion. In this way, the article offers a contribution to the anthropomorphization literature conceptualizing humanwashing as a deceptive use of AI-enabled machines aimed at intentionally or unintentionally misleading organizational stakeholders and the broader public about the true capabilities that AIEMs possess. (shrink)