Browsing by Keyword "AI ethics"
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Item Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation(2023-11) Díaz-Rodríguez, Natalia; Del Ser, Javier; Coeckelbergh, Mark; López de Prado, Marcos; Herrera-Viedma, Enrique; Herrera, Francisco; IATrustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.Item The Right to Be Forgotten in Artificial Intelligence: Issues, Approaches, Limitations and Challenges(Institute of Electrical and Electronics Engineers Inc., 2023) Lobo, Jesus L.; Gil-Lopez, Sergio; Del Ser, Javier; IAThe Right To Be Forgotten is widely conceived as a fundamental principle of the human being. It has become a subject of capital importance in domains where sensitive information is collected from individuals, requiring the provision of monitoring, governance and audit tools to control where such information is used. Artificial Intelligence models are not an exception to this statement: since they are learned from data, this fundamental right should allow individuals to have their personal information erased from AI-based systems. However, the application of this right is not straightforward: what does erasing mean in the context of a model learned from data? Is it just a matter of removing the concerned data and retraining the models? This manuscript provides a brief overview of these and more issues, proposing a desiderata for technical advances noted in this direction, and outlining research directions for prospective studies.