Atia Cortés

The growing presence of artificial intelligence (AI) in several areas of society brings to light complex ethical issues that need to be effectively addressed. With its widespread adoption, it is crucial to ensure ethical principles such as fairness and transparency for the responsible use of AI. AI auditing tools are vital for evaluating, monitoring, and mitigating vulnerabilities in AI systems. While tools like AIF360 or Aequitas audit-specific dimensions of ethical principles, such as fairness, there is currently no comprehensive solution for auditing multiple ethical principles simultaneously. This chapter introduces an integrated tool designed to assess AI systems across multiple dimensions, including fairness, explainability, robustness, transparency, bias, and other principles such as sustainability and legal aspects. By enabling the simultaneous evaluation of these factors, the proposed tool ensures that AI systems are developed and deployed in a more ethical, transparent, and socially responsible manner. This approach promotes trust and security in AI technologies while aligning with fundamental ethical values.

Ensuring trustworthiness in AI-based systems remains a significant challenge, particularly for safety-critical applications such as Advanced Driving Assistance Systems (ADAS). This paper presents a comprehensive framework for evaluating the trustworthiness of AI-based systems for the Connected, Cooperative and Automated Mobility (CCAM) ecosystem. In particular, we present the application of our methodology with a use case of an AI-enhanced ADAS developed within an EU-funded project. The approach combines simulation-based testing with predefined scenarios and expected outcomes to rigorously assess the system’s decision-making reliability and transparency. Trustworthiness is addressed through multiple dimensions, including accuracy, robustness, and explainability, with the framework designed to identify potential weaknesses and prioritise improvements in both the algorithmic and decision-making components.

Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practice remains limited. A major barrier is the quality and fairness of training data, which is often compromised by biased data collection practices. This paper draws on insights from the AI4HealthyAging project, part of Spain's national R&D initiative, where our task was to detect biases during clinical data collection. We identify several types of bias across multiple use cases, including historical, representation, and measurement biases. These biases manifest in variables such as sex, gender, age, habitat, socioeconomic status, equipment, and labeling. We conclude with practical recommendations for improving the fairness and robustness of clinical problem design and data collection. We hope that our findings and experience contribute to guiding future projects in the development of fairer AI systems in healthcare.

This paper presents the results of an extensive study investigating the opinions on Artificial Intelligence (AI) of a sample of 4,006 European citizens from eight distinct countries (France, Germany, Italy, Netherlands, Poland, Romania, Spain, and Sweden). The aim of the study is to gain a better understanding of people’s views and perceptions within the European context, which is already marked by important policy actions and regulatory processes. To survey the perceptions of the citizens of Europe, we design and validate a new questionnaire (PAICE) structured around three dimensions: people’s awareness, attitude, and trust. We observe that while awareness is characterized by a low level of self-assessed competency, the attitude toward AI is very positive for more than half of the population. Reflecting on the collected results, we highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust and the development of inclusive AI policies. The introduction of rules that ensure legal and ethical standards, along with the activity of high-level educational entities, and the promotion of AI literacy are identified as key factors in supporting a trustworthy AI ecosystem.We make some recommendations for AI governance focused on the European context and conclude with suggestions for future work.

Today, Artificial Intelligence (AI) has a direct impact on the daily life of billions of people. Being applied to sectors like finance, health, security and advertisement, AI fuels some of the biggest companies and research institutions in the world. Its impact in the near future seems difficult to predict or bound. In contrast to all this power, society remains mostly ignorant of the capabilities and standard practices of AI today. To address this imbalance, improving current interactions between people and AI systems, we propose a transparency scheme to be implemented on any AI system open to the public. The scheme is based on two pillars: Data Privacy and AI Transparency. The first recognizes the relevance of data for AI, and is supported by GDPR. The second considers aspects of AI transparency currently unregulated: AI capabilities, purpose and source. We design this pillar based on ethical principles. For each of the two pillars, we define a three-level display. The first level is based on visual signs, inspired by traffic signs managing the interaction between people and cars, and designed for quick and universal interpretability. The second level uses factsheets, providing limited details. The last level provides access to all available information. After detailing and exemplifying the proposed transparency scheme, we define a set of principles for creating transparent by design software, to be used during the integration of AI components on user-oriented services.

Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of society through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans contribute to the good of people and society as a whole. Our contribution consists of three parts: (i) a conceptualization of AI for social good highlighting the role of AI policy, in particular, the one put forward by the European Commission (EC); (ii) a qualitative analysis of 15 European national strategies mapping investment plans and suggesting their relation to the social good (iii) a reflection on the current status of investments in socially good AI and possible steps to move forward. Our study suggests that while European national strategies incorporate money allocations in the sphere of AI for social good (e.g. education), there is a broader variety of underestimated actions (e.g. multidisciplinary approach in STEM curricula and dialogue among stakeholders) that can boost the European commitment to sustainable and responsible AI innovation.

The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.