AUTHOR=Gros Chloe , Kester Leon , Martens Marieke , Werkhoven Peter 

TITLE=Modelling societal preferences for automated vehicle behaviour with ethical goal functions

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1676225

DOI=10.3389/frai.2025.1676225

ISSN=2624-8212

ABSTRACT=IntroductionAs automated vehicles (AVs) assume increasing decision-making responsibilities, ensuring their alignment with societal values becomes essential. Existing ethical frameworks for AVs have primarily remained conceptual, lacking empirical operationalization. To address this gap, this study develops an Ethical Goal Function (EGF)—a quantitative model that encodes societal moral preferences for AV decision-making—within the theoretical framework of Augmented Utilitarianism (AU). AU integrates consequentialist, deontological, and virtue-ethical principles while remaining adaptable to evolving societal values. This work also proposes embedding the EGF into a Socio-Technological Feedback (SOTEF) Loop, enabling continuous refinement of AV decision systems through stakeholder input.MethodsThe EGF was constructed using discrete choice experiments (DCEs) conducted with Dutch university students (N = 89). Participants evaluated AV-relevant moral scenarios characterized by six ethically salient attributes: physical harm, psychological harm, moral responsibility, fair innings, legality, and environmental harm. These attributes were derived from biomedical ethics and moral psychology and validated in prior AV ethics research. Using participants’ choices, a multinomial logit (MNL) model was estimated to derive attribute weights representing aggregate societal moral preferences. Model performance was evaluated using 5-fold cross-validation.ResultsThe MNL model produced stable attribute weights across folds, achieving an average predictive accuracy of 63.8% (SD = 3.3%). These results demonstrate that the selected attributes and underlying AU-based framework can meaningfully predict participants’ ethical preferences in AV decision scenarios. The EGF thus represents a data-driven, empirically grounded method for translating societal moral judgments into computationally usable parameters for AV decision-making systems.DiscussionThis study contributes the first empirical operationalization of ethical frameworks for AVs through the development of an Ethical Goal Function and demonstrates how it can be embedded in a Socio-Technological Feedback (SOTEF) Loop for continuous societal alignment. The dual contribution advances both the theoretical grounding and practical implementation of human-centered ethics in automated decision-making. However, several limitations remain. The reliance on a Dutch university sample restricts cultural generalizability, and textual presentation may limit ecological validity. Future work should expand the cultural diversity of participants and compare alternative presentation modalities (e.g., visual, immersive) to better capture real-world decision contexts.