The increasing adoption of Artificial Intelligence (AI) systems in high-stakes applications brings new opportunities for innovation, economic growth and the digital transformation of society. However, this often comes with associated risks to the safety, health or fundamental rights of people, highlighting an urgent need for the systematic adoption of trustworthy AI practices. Transparency is key for building trust in AI systems, as it facilitates their understanding and scrutiny. This article discusses transparency obligations introduced in the AI Act, the recently proposed European regulatory framework for Artificial Intelligence. Specifically, we look at requirements for providers of high-risk AI systems in terms of provision of information to users and technical documentation. An analysis of the extent to which current approaches for AI documentation satisfy these requirements is presented, assessing their suitability as a basis for future technical standards and making recommendations for their potential development in this direction.

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. Our findings reveal notable correlations between different types of diversity, such as gender and institutional type, geography and topics, as well as topics and first author’s gender. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Finally, we analyse diversity initiatives that have been undertaken in other AI-related research communities to foster diversity, and conclude on a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The data collected in this work will be publicly available, ensuring strict personal data protection and governance rules.