Who Is Responsible? Social Identity, Robot Errors and Blame Attribution
This paper examines how human blame practices in human-robot interaction (HRI) intersect with power dynamics, social identities, and gender stereotypes. The authors argue that conventional blame attribution fails to account for structural injustices, and that gendering robots risks reinforcing discriminatory norms that carry over into human-human interactions. They suggest embracing constructive disruptions and moving away from anthropomorphized designs to address these shortcomings.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This paper is for researchers, ethicists, and roboticists interested in the moral, social, and philosophical implications of human-robot interaction.
What the document contains
The paper addresses how errors and responsibility in human-robot interactions relate to human blame practices, Strawsonian reactive attitudes, and the reinforcement of gender and power inequalities through robot design and gendering.
The document does not provide technical specifications for robot error mitigation software, legal compliance frameworks, or quantitative empirical data generated by the authors themselves.
- Conventional blame practices and reactive attitudes often fail to account for power dynamics, social inequalities, and discriminatory social practices.
- Gendering social robots and assigning them specific care or authority roles risks reinforcing existing human gender stereotypes.
- Behaviors and attitudes learned during human-robot interactions can carry over into human-human interactions.
- Robots should not be treated as empty placeholders for blame, as their embodiment carries heavy social projections and narratives.
- Designers can counter harmful stereotypes by embracing disruptive behaviors ('embracing the glitch') and moving toward sociomorphic rather than anthropomorphic designs.
· 1. Introduction - 1.1. Aim and Positionality · 2. Errors and Blame in Human-Robot Interaction - 2.1. Why Blame? - 2.2. Perception of Responsibility in Cases of Robot Error · 3. Revisiting Reactive Attitudes · 4. Social Identity, Norms and Power in HRI · 5. Are Robots Responsible? - 5.1. Robots' Influence on Blame Practices - 5.2. Embracing the Glitch · 6. Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
3/5Clear responsibility for AI outcomes
When an error occurs, people usually try to find the cause of it i.e. find out who is responsible, and thus, who is to blame.
1. Introduction
screening score · 2026-10-24
Fairness
3/5Non-discrimination and equitable treatment
It is evident that robots, embodying roles linked to specific social groups, pose a risk of reinforcing stereotypes of how these groups behave or should behave, so they set a normative and descriptive standard.
Abstract
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
1/5Openness about how AI systems work and are used
Companies and researchers are therefore investigating how to rectify these situations, often by testing strategies such as delivering explanations and apologies
1. Introduction
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-24