The EU has adopted a hybrid governance approach to address the challenges posed by Artificial Intelligence (AI), emphasizing the role of harmonized European standards (HES). Despite advantages in expertise and flexibility, HES processes face legitimacy problems and struggle with epistemic gaps in the context of AI. This article addresses the problems that characterize HES processes by outlining the conceptual need, theoretical basis, and practical application of experimental standardization, which is defined as an ex-ante evaluation method that can be used to test standards for their effects and effectiveness. Experimental standardization is based on theoretical and practical developments in experimental governance, legislation, and innovation. Aligned with ideas and frameworks like Science for Policy and evidence-based policymaking, it enables co-creation between science and policymaking. We apply the proposed concept in the context of HES processes, where we submit that experimental standardization contributes to increasing throughput and output legitimacy, addressing epistemic gaps, and generating new regulatory knowledge.

The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.

This article sheds light on legal implications and challenges surrounding emotion data processing within the EU’s legal framework. Despite the sensitive nature of emotion data, the GDPR does not categorize it as special data, resulting in a lack of comprehensive protection. The article also discusses the nuances of different approaches to affective computing and their relevance to the processing of special data under the GDPR. Moreover, it points to potential tensions with data protection principles, such as fairness and accuracy. Our article also highlights some of the consequences, including harm, that processing of emotion data may have for individuals concerned. Additionally, we discuss how the AI Act proposal intends to regulate affective computing. Finally, the article outlines the new obligations and transparency requirements introduced by the DSA for online platforms utilizing emotion data. Our article aims at raising awareness among the affective computing community about the applicable legal requirements when developing AC systems intended for the EU market, or when working with study participants located in the EU. We also stress the importance of protecting the fundamental rights of individuals even when the law struggles to keep up with technological developments that capture sensitive emotion data.

Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. A literature review reveals how affective computing systems may work differently for different groups due to, for instance, mental health conditions impacting facial expressions and speech or age-related changes in facial appearance and health. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation. By emphasizing the need for more inclusive sampling strategies and standardized documentation of demographic factors in datasets, this paper provides recommendations and calls for greater attention to inclusivity and consideration of societal consequences in affective computing research to promote ethical and accurate outcomes in this emerging field.

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3-5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations.

In healthcare, gender and sex considerations are crucial because they affect individuals' health and disease differences. Yet, most algorithms deployed in the healthcare context do not consider these aspects and do not account for bias detection. Missing these dimensions in algorithms used in medicine is a huge point of concern, as neglecting these aspects will inevitably produce far from optimal results and generate errors that may lead to misdiagnosis and potential discrimination. This paper explores how current algorithmic-based systems may reinforce gender biases and affect marginalized communities in healthcare-related applications. To do so, we bring together notions and reflections from computer science, queer media studies, and legal insights to better understand the magnitude of failing to consider gender and sex difference in the use of algorithms for medical purposes. Our goal is to illustrate the potential impact that algorithmic bias may have on inadvertent discriminatory, safety, and privacy-related concerns for patients in increasingly automated medicine. This is necessary because by rushing the deployment of AI technologies that do not account for diversity, we risk having an even more unsafe and inadequate healthcare delivery. By promoting the account for privacy, safety, diversity, and inclusion in algorithmic developments with health-related outcomes, we ultimately aim to inform the Artificial Intelligence (AI) global governance landscape and practice on the importance of integrating gender and sex considerations in the development of algorithms to avoid exacerbating existing or new prejudices.

This paper addresses the interplay between robots, cybersecurity, and safety from a European legal perspective, a topic under-explored by current technical and legal literature. The legal framework, together with technical standards, is a necessary parameter for the production and deployment of robots. However, European law does not regulate robots as such, and there exist multiple and overlapping legal requirements focusing on specific contexts, such as product safety and medical devices. Besides, the recently enacted European Cybersecurity Act establishes a cybersecurity certification framework, which could be used to define cybersecurity requirements for robots, although concrete cyber-physical implementation requirements are not yet prescribed. In this article, we illustrate cybersecurity challenges and their subsequent safety implications with the concrete example of care robots. These robots interact in close, direct contact with children, elderly, and persons with disabilities, and a malfunctioning or cybersecurity threat may affect the health and well-being of these people. Moreover, care robots may process vast amounts of data, including health and behavioral data, which are especially sensitive in the healthcare domain. Security vulnerabilities in robots thus raise significant concerns, not only for manufacturers and programmers, but also for those who interact with them, especially in sensitive applications such as healthcare. While the latest European policymaking efforts on robot regulation acknowledge the importance of cybersecurity, many details, and their impact on user safety have not yet been addressed in depth. Our contribution aims to answer the question whether the current European legal framework is prepared to address cyber and physical risks from care robots and ensure safe human–robot interactions in such a sensitive context. Cybersecurity and physical product safety legal requirements are governed separately in a dual regulatory framework, presenting a challenge in governing uniformly and adequately cyber-physical systems such as care robots. We conceptualize and discuss the challenges of regulating cyber-physical systems’ security with the current dual framework, particularly the lack of mandatory certifications. We conclude that policymakers need to consider cybersecurity as an indissociable aspect of safety to ensure robots are truly safe to use.

Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include explicit obligations relating to harmful and wrongful stereotyping, these stereotypes persist online and offline. By identifying how inferential analytics may reinforce gender stereotyping and affect marginalized communities, opportunities for addressing these concerns and thereby increasing privacy, diversity, and inclusion online can be explored. This is important because misgendering reinforces gender stereotypes, accentuates gender binarism, undermines privacy and autonomy, and may cause feelings of rejection, impacting people's self-esteem, confidence, and authenticity. In turn, this may increase social stigmatization. This study brings into view concerns of discrimination and exacerbation of existing biases that online platforms continue to replicate and that literature starts to highlight. The implications of misgendering on Twitter are investigated to illustrate the impact of algorithmic bias on inadvertent privacy violations and reinforcement of social prejudices of gender through a multidisciplinary perspective, including legal, computer science, and critical feminist media-studies viewpoints. An online pilot survey was conducted to better understand how accurate Twitter's gender inferences of its users’ gender identities are. This served as a basis for exploring the implications of this social media practice.

This paper explores ethical and legal implications arising from the intertwinement of cloud services, healthcare and robotics. It closes an existing gap in the literature by highlighting the distinctive ethical and legal concerns associated with the inter-dependence of the cyber- and the physical aspects of healthcare cloud robotics. The identified core concerns include uncertainties with regard to data protection requirements; distributed responsibilities for unintended harm; achievement of transparency and consent for cloud robot services especially for vulnerable robot users; secondary uses of cloud data derived from robot activities; data security; and wider social issues. The paper aims to raise awareness and stimulate reflection of the legal and ethical impacts on different stakeholders arising from the use of cloud services in healthcare robotics. We show that due to the complexity of these concerns the design and implementation of such robots in healthcare requires an interdisciplinary development and impact assessment process. In light of legal requirements and ethical responsibilities towards end-users and other stakeholders, we draw practical considerations for engineers developing cloud services for robots in healthcare.