Researchers at Pusan National University have examined how responsibility should be understood when AI systems cause harm. Their work points to a long-standing issue in AI ethics: traditional moral theories depend on human mental capacities such as intention, awareness, and control. Because AI systems operate without consciousness or free will, these frameworks struggle to identify a responsible party when an autonomous system contributes to a harmful outcome.
The study outlines how complex and semi-autonomous systems make it difficult for developers or users to foresee every consequence. It notes that these systems learn and adapt through internal processes that can be opaque even to those who build them. That unpredictability creates what scholars describe as a gap between harmful events and the agents traditionally held accountable.
The research incorporates findings from experimental philosophy that explore how people assign agency and responsibility in situations involving AI systems. These studies show that participants often treat both humans and AI systems as involved in morally relevant events. The study uses these results to examine how public judgments relate to non-anthropocentric theories and to consider how those judgments inform ongoing debates about responsibility in AI ethics.
The research analyzes this gap and reviews approaches that move responsibility away from human-centered criteria. These alternatives treat agency as a function of how an entity interacts within a technological network rather than as a product of mental states. In this view, AI systems participate in morally relevant actions through their ability to respond to inputs, follow internal rules, adapt to feedback, and generate outcomes that affect others.
The study examines proposals that distribute responsibility across the full network of contributors involved in an AI system's design, deployment, and operation. Those contributors include programmers, manufacturers, and users. The system itself is also part of that network. The framework does not treat the network as a collective agent but assigns responsibilities based on each participant's functional role.
According to the research, this form of distribution focuses on correcting or preventing future harm rather than determining blame in the traditional sense. It includes measures such as monitoring system behavior, modifying models that produce errors, or removing malfunctioning systems from operation. The study also notes that human contributions may be morally neutral even when they are part of a chain that produces an unexpected negative outcome. In those cases, responsibility still arises in the form of corrective duties.
The work compares these ideas with findings from experimental philosophy. Studies show that people routinely regard AI systems as actors involved in morally significant events, even when they deny that such systems possess consciousness or independent control. Participants in these studies frequently assign responsibility to both AI systems and the human stakeholders connected to them. Their judgments tend to focus on preventing recurrence of mistakes rather than on punishment.
Across the reviewed research, people apply responsibility in ways that parallel non-anthropocentric theories. They treat responsibility as something shared across networks rather than as a burden placed on a single agent. They also interpret responsibility as a requirement to address faults and improve system outcomes.
The study concludes that the longstanding responsibility gap reflects assumptions tied to human psychology rather than the realities of AI systems. It argues that responsibility should be understood as a distributed function across socio-technical networks and recommends shifting attention toward the practical challenges of implementing such models, including how to assign duties within complex systems and how to ensure those duties are carried out.
Notes: This post was drafted with the assistance of AI tools and reviewed, edited, and published by humans. Image: DIW-Aigen.
Read next: Study Finds Most Instagram Users Who Feel Addicted Overestimate Their Condition
The study outlines how complex and semi-autonomous systems make it difficult for developers or users to foresee every consequence. It notes that these systems learn and adapt through internal processes that can be opaque even to those who build them. That unpredictability creates what scholars describe as a gap between harmful events and the agents traditionally held accountable.
The research incorporates findings from experimental philosophy that explore how people assign agency and responsibility in situations involving AI systems. These studies show that participants often treat both humans and AI systems as involved in morally relevant events. The study uses these results to examine how public judgments relate to non-anthropocentric theories and to consider how those judgments inform ongoing debates about responsibility in AI ethics.
The research analyzes this gap and reviews approaches that move responsibility away from human-centered criteria. These alternatives treat agency as a function of how an entity interacts within a technological network rather than as a product of mental states. In this view, AI systems participate in morally relevant actions through their ability to respond to inputs, follow internal rules, adapt to feedback, and generate outcomes that affect others.
The study examines proposals that distribute responsibility across the full network of contributors involved in an AI system's design, deployment, and operation. Those contributors include programmers, manufacturers, and users. The system itself is also part of that network. The framework does not treat the network as a collective agent but assigns responsibilities based on each participant's functional role.
According to the research, this form of distribution focuses on correcting or preventing future harm rather than determining blame in the traditional sense. It includes measures such as monitoring system behavior, modifying models that produce errors, or removing malfunctioning systems from operation. The study also notes that human contributions may be morally neutral even when they are part of a chain that produces an unexpected negative outcome. In those cases, responsibility still arises in the form of corrective duties.
The work compares these ideas with findings from experimental philosophy. Studies show that people routinely regard AI systems as actors involved in morally significant events, even when they deny that such systems possess consciousness or independent control. Participants in these studies frequently assign responsibility to both AI systems and the human stakeholders connected to them. Their judgments tend to focus on preventing recurrence of mistakes rather than on punishment.
Across the reviewed research, people apply responsibility in ways that parallel non-anthropocentric theories. They treat responsibility as something shared across networks rather than as a burden placed on a single agent. They also interpret responsibility as a requirement to address faults and improve system outcomes.
The study concludes that the longstanding responsibility gap reflects assumptions tied to human psychology rather than the realities of AI systems. It argues that responsibility should be understood as a distributed function across socio-technical networks and recommends shifting attention toward the practical challenges of implementing such models, including how to assign duties within complex systems and how to ensure those duties are carried out.
Notes: This post was drafted with the assistance of AI tools and reviewed, edited, and published by humans. Image: DIW-Aigen.
Read next: Study Finds Most Instagram Users Who Feel Addicted Overestimate Their Condition
