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Cited by 2Ethics and Social Impacts of AIEthics in Business and EducationLiterary and Philosophical Studies

Artificial moral agents: should machines take ethical responsibility?

Open access
Reiter, Patrick, Norman, Utku, Weinberger, Nora, Bruno, Barbara·2025·2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO)
DOI 10.1109/arso64737.2025.11124921
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Abstract

Robots and AI systems are increasingly being developed for and deployed in contexts where decision-making entails moral implications. Examples include autonomous vehicles navigating the ethical dilemmas of traffic, healthcare robots tasked with ensuring patient safety and well-being, assistive robots upholding the autonomy and dignity of the elderly and people with disabilities, and social robots guiding children through educational experiences while assisting their emotional and cognitive development. The transition from passive tools to autonomous entities with moral decision-making capabilities has ignited extensive debate about the ethical responsibilities of Artificial Moral Agents (AMAs). This paper synthesizes recent advancements in AMA research, addressing evolving debates on their ethical feasibility and societal integration. We assess key arguments against and for AMAs, highlighting impacts on moral responsibility, cultural perspectives, and stakeholder trust. Our analysis reveals that while AMAs remain a subject of theoretical debate, their integration into ethically sensitive contexts is increasingly proposed, necessitating clearer governance strategies. Given the recent AI advancements and increased deployment of robotics in high-stakes settings, this synthesis is timely and highlights the urgency of addressing these ethical challenges. By analyzing recent advancements and diverse perspectives, we aim to provide a concise but comprehensive understanding of the complexities involved in empowering machines with moral decision-making capabilities.

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  • Agent reasoning prioritizes generalized patterns and optimization goals, often failing to incorporate individual, cultural, or psychosocial context.

    “Contextual blindness and loss of nuance | Agent reasoning prioritizes generalized patterns and optimization goals, often failing to incorporate individual, cultural, or psychosocial context (37, 43).”

    From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [43]

    In “Impossibility to Teach Correct Ethics to a Machine” (page unavailable), the paper says computational moral-reasoning models are limited in capturing cultural variation and rarely allow cultural influences or societal preferences. “Preventing Normative Lock-In and Promoting Moral Pluralism” (page unavailable) also warns that fixed ethical frameworks could marginalize alternative perspectives. These passages support the concern about cultural context, but do not establish that agents prioritize generalized patterns and optimization goals or fail to incorporate individual or psychosocial context. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The review identified contextual blindness and loss of nuance as a key ethical challenge in multi-agent AI systems in healthcare.

    “(7) contextual blindness, reflecting a loss of individualized patient understanding in modular AI workflows.”

    From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [43]

    The section “A. Impossibility to Teach Correct Ethics to a Machine” says computational models struggle to capture the complexity of human ethical reasoning and cultural variation, which supports a general concern about lost nuance. The review does not identify contextual blindness or loss of individualized patient understanding in modular, multi-agent healthcare workflows as a key challenge. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • If a multi-agent system negotiates a care pathway based on probabilistic efficiency without understanding the "lived experience" of the patient, and if the clinician cannot explain the system's "why" due to compound opacity, the ethical validity of informed consent is compromised.

    “If a multi-agent system negotiates a care pathway based on probabilistic efficiency without understanding the “lived experience” of the patient (43), and if the clinician cannot explain the system's “why” due to compound opacity, the ethical validity of informed consent is compromised (49).”

    From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [43]

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Multi-agent AI systems do not merely "add" risks on top of existing medical AI concerns; rather, they reconfigure the ethical landscape by making clinical agency emergent from interactions among multiple components and actors, thereby weakening traditional assumptions about traceability, contestability, and professional control.

    “Taken together, these findings suggest that multi-agent AI systems do not merely “add” risks on top of existing medical AI concerns; rather, they reconfigure the ethical landscape by making clinical agency emergent from interactions among multiple components and actors, thereby weakening traditional assumptions about traceability, contestability, and professional control (6, 15, 31, 43, 52).”

    From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [43]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Agents lack the "lived experience" necessary to effectively navigate social dynamics, making them susceptible to reinforcing normative hierarchies or prioritizing economic efficiency at the expense of moral subtleties essential for personalized healthcare.

    “Given that agents lack the “lived experience” necessary to effectively navigate these social dynamics, they are susceptible to reinforcing normative hierarchies or prioritizing economic efficiency at the expense of the moral subtleties essential for personalized healthcare (43).”

    From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [43]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

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Authors
Reiter, P., Norman, U., Weinberger, N., & Bruno, B.
Affiliations
Karlsruhe Institute of Technology (DE)
ORCID
Venue
2025 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO)
Publisher
IEEE
Published
2025-07-17
Pages
218-224
DOI
10.1109/arso64737.2025.11124921
OpenAlex
W4413319439
Cited by
2
References
35
Citations by year
2026: 2
FWCI
2.06
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) (publishedVersion)
  • Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
  • Social Sciences › Political Science and International Relations › Literary and Philosophical Studies
Keywords
artificial moral agents, autonomous decision-making, ethical AI, autonomous vehicles, healthcare robots, assistive robots, social robots, ethical decision-making, AI governance, stakeholder trust

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