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)

Approaches aimed at regulating artificial intelligence (AI) include a particular form of risk regulation, i.e. a risk-based approach. The most prominent example is the European Union’s Artificial Intelligence Act (AI Act). This article addresses the challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values. This is mainly due to the normative ambiguity of such rights and societal values when attempts are made to (...) select, interpret, specify or operationalise them for the purposes of risk assessments and risk mitigation. This is exemplified by (1) human dignity, (2) informational self-determination, data protection and privacy, (3) anti-discrimination, fairness and justice, and (4) the common good. Normative ambiguities require normative choices, which are assigned to different actors under the regime of the AI Act. Particularly critical normative choices include selecting normative concepts by which to operationalise and specify risks, aggregating and quantifying risks (including the use of metrics), balancing value conflicts, setting levels of acceptable risks, and standardisation. To ensure that these normative choices do not lack democratic legitimacy and to avoid legal uncertainty, further political processes and scientific debates are suggested. (shrink)

The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development of (...) policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector. (shrink)

Since the beginning of the current hype around Artificial Intelligence (AI), governments, research institutions, and the industry invited ethical, legal, and social sciences (ELS) scholars to research AI’s societal challenges from various disciplinary viewpoints and perspectives. This approach builds upon the tradition of supporting research on the societal aspects of emerging sciences and technologies, which started with the Ethical, Legal, and Social Implications (ELSI) Program in the Human Genome Project (HGP) in the early 1990s. However, although a diverse ELS research (...) community has formed since then, AI’s societal challenges came to be mostly understood under the narrow framing of ethics and disconnected from the insights and experiences of past ELS research. In this article, we make up for this gap and connect insights from past ELS researchers with current approaches to research the societal challenges of AI. We analyse and summarize the history of “ELS programs” (programs that emerged since the HGP to support ELS research in a given domain) as three distinct eras: a genomics era, a nano era, and an RRI era. Each of these eras comprises several achievements and challenges relevant to ELS programs in AI research, such as the setup of independent funding bodies, the engagement of the wider public in research practice, and the increasing importance of private actors. Based on these insights, we argue that AI research currently falls back on self-regulatory, less participatory, and industry-led approaches that trouble ELS programs’ past achievements and hinder opportunities to overcome the still-existing challenges. (shrink)