Abstract
Algorithms have advanced in status from supporting human decision-making to making decisions for themselves. The fundamental issue here is the relationship between Big Data and algorithms, or how algorithms empower data with direction and purpose. In this paper, I provide a conceptual framework for analyzing and improving ethical decision-making in Human-AI interaction. On the one hand, I examine the challenges and the limitations facing the field of Machine Ethics and Explainability in its aim to provide and justify ethical decisions. On the other hand, I propose connecting counterfactual explanations with the emotion of regret, as requirements for improving ethical decision-making in novel situations and under uncertainty. To test whether this conceptual framework has empirical value, I analyze the COVID-19 epidemic in terms of “what might have been” to answer the following question: could some of the unintended consequences of this health crisis have been avoided if the available data had been used differently before the crisis happened and as it unfolded?
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Martín-Peña, R. (2022). Does the COVID-19 Pandemic have Implications for Machine Ethics?. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_82
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