RT info:eu-repo/semantics/doctoralThesis
T1 Responsible decision-making in trustworthy human-AI interaction: linking counterfactual explanations and regret
A1 Martín Peña, Rosa Esther
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Inteligencia artificial
K1 Responsible AI
K1 IA responsable
K1 72 Filosofía
AB This dissertation addresses the issue of responsible decision-making in trustworthy human-AI interaction from an interdisciplinary perspective. This approach is motivated by the growing use of AI systems with increasingly autonomous decision-making capabilities. However, designing and implementing these AI systems with cognitive and affective human-like abilities to replace them in their decisions is not exempt from limitations and challenges as well as new opportunities. In this way, to understand more about the risks and possibilities that this emerging scenario brings us, this dissertation presents, describes, and analyzes the fields of machine ethics and explainable AI, along with other advances in the areas of neuroscience and affective computing for their studies on the impact of emotions in human behavior. Thus, all these disciplines comprise the design proposal of the multi-ethical interdisciplinary framework for responsible AI. Thus, the proposed framework is divided into three levels in which human and artificial agents cooperate within goal-driven XAI. The aim is to create a theory of mind about the normative value of regret to prove whether the somatic marker hypothesis driven by the counterfactual component of the anticipated regret could serve as a recommendation norm for preventing unspecified errors before they occur.Chapter 1 introduces the dissertation topic and justifies the motivations for conducting it. Chapter 2 provides an overview of the state of AI ethics. Chapter 3 discusses the research methodology employed. Chapter 4 discusses and presents the opportunities and constraints of the field of machine ethics for de-signing responsible AI. Chapter 5 deals with different approaches to Explainable AI and the phenomenon of biases in algorithmic systems and human behavior. Chapter 6 focuses on the current debate on theories about emotions and the possibilities and risks of using emotional data in affective computing.  Chapter 7 concerns the ethical power of the human imagination in creating counterfactual scenarios by repressing those associated with negative emotional charges, such as regret. The last chapter of this research, Chapter 8, closes this dissertation by presenting the multi-ethical interdisciplinary framework for responsible AI in trustworthy human-AI interaction. The conclusions are in chapter 9.
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/71450
UL https://uvadoc.uva.es/handle/10324/71450
LA eng
NO Escuela de Doctorado
DS UVaDOC
RD 27-nov-2024