"Fire! Do not fire!": Investigating the effects of autonomous systems on agency and moral decision-making.

Acta Psychol (Amst)

Department of Life Sciences, Royal Military Academy, Brussels, Belgium.

Published: August 2025


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Autonomous systems have pervaded many aspects of human activities. However, research suggests that the interaction with these machines may influence human decision-making processes. These effects raise ethical concerns in moral situations. We created an ad hoc setup to investigate the effects of system autonomy on moral decision-making and human agency in a trolley-like dilemma. In a battlefield simulation, 31 participants had to decide whether to initiate an attack depending on conflicting moral values. Our results suggest that human decision- making in morally challenging scenarios can be influenced by recommendations from autonomous systems. Interestingly, subjective judgement of responsibility decreased with higher levels of autonomy, suggesting that interaction with intelligent systems can influence both moral decision-making and sense of responsibility. Given the growing use of intelligent systems in sensitive domains, further research is essential to better understand these effects and their broader implications.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.actpsy.2025.105350DOI Listing

Publication Analysis

Top Keywords

autonomous systems
12
moral decision-making
12
intelligent systems
8
systems
5
moral
5
"fire! fire!"
4
fire!" investigating
4
effects
4
investigating effects
4
effects autonomous
4

Similar Publications

The miniaturization of separation platforms marks a transformative shift in analytical science, merging microfabrication, automation, and intelligent data integration to meet rising demands for portability, sustainability, and precision. This review critically synthesizes recent technological advances reshaping the field-from microinjection and preconcentration modules to compact, high-sensitivity detection systems including ultraviolet-visible (UV/Vis), fluorescence (FL), electrochemical detection (ECD), and mass spectrometry (MS). The integration of microcontrollers, AI-enhanced calibration routines, and IoT-enabled feedback loops has led to the rise of self-regulating analytical devices capable of real-time decision-making and autonomous operation.

View Article and Find Full Text PDF

Rationale And Objectives: This study systematically evaluates the diagnostic performance of artificial intelligence (AI)-driven and conventional radiomics models in detecting muscle-invasive bladder cancer (MIBC) through meta-analytical approaches. Furthermore, it investigates their potential synergistic value with the Vesical Imaging-Reporting and Data System (VI-RADS) and assesses clinical translation prospects.

Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

View Article and Find Full Text PDF

Deep learning (DL) has significantly improved the diagnostic accuracy and efficiency of cytopathologists. However, current DL-assisted reading modes have yet to be fully evaluated, and there is limited evidence regarding cytopathologists' preferences and experiences. This study employs a randomized, controlled, four-way crossover design to assess the effectiveness of four different reading modes in cervical cytopathology readings.

View Article and Find Full Text PDF

How does large-scale underground mining affect the water cycle? - Comprehensive analysis based on isotopes, water levels and hydrogeological conditions.

J Environ Manage

September 2025

State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot, 010018, China; Inner Mongolia Key Laboratory of Ecohydrology and High Efficient Utilization of Water Resources, Hohhot, 010018, China; Inner Mongolia Section of the Yellow

Large-scale underground coal mining alters regional water cycles, yet the mechanisms governing interactions among water bodies in deep mining areas are poorly understood. For this purpose, by integrating hydrogen and oxygen isotopes, water levels, hydrogeological conditions, and end-member mixing analysis (EMMA), this study systematically analyzed and quantified the circulation and transformation mechanisms among different water bodies influenced by coal mining. Key findings reveal: (1) Mining-induced fractures disrupt the aquitard above the coal seam, establishing a direct hydraulic link between Zhiluo Formation confined groundwater and mine water, with the former contributing 87.

View Article and Find Full Text PDF

Self-Regulating Hydrogel Actuators.

Chem Rev

September 2025

Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH) 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, South Korea.

Self-regulating hydrogels represent the next generation in the development of soft materials with active, adaptive, autonomous, and intelligent behavior inspired by sophisticated biological systems. Nature provides exemplary demonstrations of such self-regulating behaviors, including muscle tissue's precise biochemical and mechanical feedback mechanisms, and coordinated cellular chemotaxis driven by dynamic biochemical signaling. Building upon these natural examples, self-regulating hydrogels are capable of spontaneously modulating their structural and functional states through integrated negative feedback loops.

View Article and Find Full Text PDF