Rethinking backward-looking moral responsibility as care robots move toward superintelligence

Abstract The use of AI-based care robots raises numerous questions, including the attribution of responsibility. Although there is a wealth of work on the concept of responsibility in relation to AI-based systems, this article takes a new approach. It focuses on backward-looking moral responsibility for bad outcomes and super-intelligent care robots. The starting point is the presentation of realistic scenarios in which current care robots contribute to responsibility gaps. A distinction is made between forward-looking and backward-looking moral responsibility, with a focus on backward-looking moral responsibility for bad outcomes. Using hypothetical scenarios such as careful programmer , unlucky nurse , and robot mistake , it is shown that current robots do not fulfill central conditions (control, knowledge, intention) for moral responsibility. In such scenarios, however, the attribution of moral responsibility to human actors has to be seen as a burden. Afterward, super-intelligent care robots are examined. Such machines could not only fill responsibility gaps, but also actively contribute to the avoidance of bad outcomes. Approaches to collective or extended responsibility are discussed. Finally, it is argued that moral responsibility concerning super-intelligent care robots is not only possible but could be necessary in order to address moral responsibility adequately.

Authors

Institutions

Publication Details

Journal
Discover Artificial Intelligence
Published
2026-02-26
DOI
https://doi.org/10.1007/s44163-026-01025-5
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rethinking backward-looking moral responsibility as care robots move toward superintelligence

Mario Kropf
Discover Artificial Intelligence
Social Robot Interaction and HRI
article

Rethinking backward-looking moral responsibility as care robots move toward superintelligence

Mario Kropf
article en

Abstract

Abstract The use of AI-based care robots raises numerous questions, including the attribution of responsibility. Although there is a wealth of work on the concept of responsibility in relation to AI-based systems, this article takes a new approach. It focuses on backward-looking moral responsibility for bad outcomes and super-intelligent care robots. The starting point is the presentation of realistic scenarios in which current care robots contribute to responsibility gaps. A distinction is made between forward-looking and backward-looking moral responsibility, with a focus on backward-looking moral responsibility for bad outcomes. Using hypothetical scenarios such as careful programmer , unlucky nurse , and robot mistake , it is shown that current robots do not fulfill central conditions (control, knowledge, intention) for moral responsibility. In such scenarios, however, the attribution of moral responsibility to human actors has to be seen as a burden. Afterward, super-intelligent care robots are examined. Such machines could not only fill responsibility gaps, but also actively contribute to the avoidance of bad outcomes. Approaches to collective or extended responsibility are discussed. Finally, it is argued that moral responsibility concerning super-intelligent care robots is not only possible but could be necessary in order to address moral responsibility adequately.

Discover Artificial Intelligence
Private Pädagogische Hochschule Augustinum (AT)
Peace, Justice and strong institutions
Openalex Percentile: Top 50%
Social Robot Interaction and HRI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.