Ethical and Statistical Considerations in Models of Moral Judgments

Abstract

This work extends recent advancements in computational models of moral decision making by using mathematical and philosophical theory to suggest adaptations to state of the art. It demonstrates the importance of model assumptions and considers alternatives to the normal distribution when modeling ethical principles. We show how the ethical theories, utilitarianism and deontology can be embedded into informative prior distributions. We continue to expand the state of the art to consider ethical dilemmas beyond the Trolley Problem and show the adaptations needed to address this complexity. The adaptations made in this work are not solely intended to improve recent models but aim to raise awareness of the importance of interpreting results relative to assumptions made, either implicitly or explicitly, in model construction.

Research on the influence and mechanism of human–vehicle moral matching on trust in autonomous vehicles

“…Utilitarian moral individuals are more likely to rely on objects whose behavior is predictable than those of deontological moral types. Besides, studies on human–computer interaction generally select typical moral types (utilitarian and deontology) to conduct research ( Baniasadi et al, 2018b ; Sivill, 2019 ; de Melo et al, 2021 ; Yokoi and Nakayachi, 2021 ; Nijssen et al, 2022 ; Vianello et al, 2022 ). Therefore, our study uses two typical moral types (utilitarian and deontological) to study the impact of the moral types of people and autonomous vehicles on the trust in autonomous vehicles.…”

A Theoretical Study of Modelling and Simulation in Mathematical Sociology: Future Directions and Challenges

“…Future research should actively engage in discussions on responsible modeling practices, transparency, and accountability in model design and interpretation. This includes considering the potential biases and unintended consequences of models, ensuring fairness and inclusivity, and actively involving stakeholders in the modeling process (Sivill, 2019;Bak, 2022).…”