Robot Stereotyping & Blame in HRI

• Robot stereotyping and blame attribution are constructs in HRI that define how human social cues and design features influence perceptions and responsibility assignments.
• Empirical research reveals that human-like design cues such as voice, morphology, and behavior amplify stereotypical expectations and skew blame during system errors.
• Studies suggest that bias-conscious design and transparent accountability mechanisms can mitigate stereotype reinforcement and improve collaborative human-robot interactions.

Robot stereotyping and blame attribution are interrelated constructs in Human–Robot Interaction (HRI) that encompass how humans ascribe social group characteristics, intentions, and responsibility to robotic agents, especially during collaborative tasks and in the presence of errors or breakdowns. Stereotyping in this context refers to the projection of human social categories (e.g., gender, ethnicity, status) onto robots via their design features or behaviors, while blame attribution concerns the allocation of moral, practical, or legal responsibility for robotic performance—especially failures. Empirical and theoretical research shows that robot embodiment, behavioral cues, and the assignment of social roles can reinforce or challenge existing human stereotypes, which in turn affect both the way robots are perceived and how blame is distributed following system errors or violations of normativity.

1. Mechanisms of Robot Stereotyping: Social Identity, Cues, and Embodiment

Robots frequently inherit social roles through features such as voice, morphology, and programmed behavior, often mapping directly onto stereotyped human social categories. For instance, voice assistants and service robots with female-coded voices or behaviors reinforce associations between femininity and caregiving or subservient roles, while male-coded robots are more often attributed expertise and authority (Stedtler et al., 2 Oct 2025). Subtle behavioral cues, such as gaze duration, greeting forms, and politeness strategies, can lead users to attribute specific ethnicities or cultural backgrounds to robots, even when gross features (e.g., accent, overt appearance) are neutralized (Makatchev et al., 2013). These cues are systematically perceived, as shown in controlled laboratory and crowdsourcing studies, leading to elevated attributions of ethnicity or gender congruent with stereotype-driven expectations.

Stereotyping is not limited to explicitly anthropomorphic platforms; even non-humanoid or industrial robots readily acquire gender attributions through anthropomorphic design cues such as body dimensions, voice modulation, or named identities, affecting perceived suitability for traditionally gendered occupational roles (Ramezani et al., 2023). The presence or absence of stereotype priming (e.g., requiring participants to label a robot’s "race" or "gender" before evaluation) directly modulates the salience and impact of such stereotypical attributions (Ogunyale et al., 2018).

2. Stereotyping, Dehumanization, and Social Impact

Robots racialized or gendered in the likeness of marginalized social identities are subject to significantly greater dehumanization than those racialized as White or male (Strait et al., 2018). Dehumanization frequency notably increases for robots with marginalized cues (e.g., Bina48: M = 0.42 vs. Nadine: M = 0.18), and the valence of language used toward these robots is substantially more negative. This amplification is attributed to both the artificial "ontology" of robots and the activation of pre-existing human biases, leading to a greater frequency of objectification, verbal abuse, and stereotype-based derogation than observed toward human analogs with similar identity cues.

Physical embodiment magnifies these effects: for example, humanoid robots imbued with salient features experience more vivid attributions of animacy and emotional capability, which can prompt strong human emotional responses such as sympathy or even moral outrage when robots are mistreated (Carlson et al., 2017). At the same time, humans often deny robots perceived as non-biological beings the full spectrum of intangible humanlike attributes such as consciousness or moral patiency, even when their behavioral sophistication equals or surpasses that of humans—a tendency termed "anti-robot speciesism" (Freitas et al., 26 Mar 2025). This dynamic both facilitates the exploitation of robots for unsavory tasks and limits their social acceptance.

3. Blame Attribution: Mechanisms, Asymmetries, and Social Roles

The attribution of blame to robots or their human collaborators is contingent on factors including embodiment, social cues, cognitive sophistication, and roles played in collaborative systems.

• Shared-Control and Multi-Agent Contexts: In cases of shared human–robot control (e.g., semi-autonomous vehicles), if a single actor (human or machine) makes an error, that actor is held responsible regardless of their nature (Awad et al., 2018). However, if both the human and the machine err, blame is disproportionately shifted away from the machine and toward the human, a departure from typical algorithm aversion seen in other domains.
• Delegation and Scapegoating: Experiments demonstrate that humans delegating tasks to artificial agents incur less blame for bad outcomes than when delegating to other humans, particularly in morally sensitive situations. Delegators who use machines receive higher post-outcome rewards (machine: 12.96 ECU vs. self-performance: 8.53 ECU, p = 0.041), evidencing exculpation effects (Feier et al., 2021). This finding upends normative assumptions about algorithmic accountability and shifts responsibility away from the original human operator, facilitating "hiding behind machines."
• Cognitive Sophistication and Blame Transfer: Humans ascribe blame to AI systems when those systems exhibit a threshold level of computational or quasi-cognitive capabilities, especially the capacity for a theory of mind. When agents are seen as capable of recklessness (i.e., knowingly endangering outcomes), the cognitive sophistication of the robot mediates blame transfer from humans towards the AI system itself (Kneer et al., 2021). Lower sophistication leads to greater blame for the human, while higher sophistication enables blame-shifting.
• Group Spillover and Double Standards: Negative actions by an AI agent can drive spillover effects, whereby blame and negative moral agency are attributed not only to the offending agent but to all similar AIs ("AI double standard")—in contrast to human contexts, where individuation prevents broad generalization (Manoli et al., 8 Dec 2024).

4. Social Scripts, Power Dynamics, and the Reification of Norms

When robots are designed to embody specific social identities, they not only reproduce stereotypical expectations but can also set new descriptive and normative standards within HRI. The mapping of identities onto specific roles (e.g., caregiving, technical, leadership) can lead to "freezing" of social roles, such that errors or failures by the robot reinforce prevailing stereotypes about the associated human social group (Stedtler et al., 2 Oct 2025). This process can create new expectations regarding who is obligated to compensate for a robot's failures, increasing the likelihood that already disadvantaged groups (e.g., women in caregiving roles) are "over-blamed" when robots coded as members of their group err.

A stylized conceptualization is:

Embodiment of Social Role	Normative Expectation	Stereotyped Blame Risk
Female-coded domestic robot	Caregiving, compliance	Blame shifted to women/care
Male-coded expert robot	Competence, authority	Blame shifted to technical

This dynamic not only shapes HRI but can produce carryover effects into Human–Human Interaction (HHI), as the reinforcement of role expectations and blame patterns with robots migrates into broader social settings.

5. Ethical, Legal, and Design Implications

The embedding of bias into physical robots, both through design choices and underlying AI models, introduces legal, ethical, and practical challenges. Legal blame for adverse outcomes is often complicated by the diffusion of agency across design teams, users, and the algorithmic backbone (Qi et al., 5 Nov 2024). Contemporary SCM-based frameworks enable granular causal modeling of robot decisions and outcomes using counterfactual reasoning (e.g., δ(a, a′) = max{0, ℙ(φ = 1 | 𝓜^a) − ℙ(φ = 1 | 𝓜^a′)}), permitting more nuanced responsibility attribution that can account for context, intentional interventions, and system design trade-offs.

Mitigation of stereotyping effects and misattribution of blame requires a multifaceted approach. This includes:

• Bias-conscious, intersectional design that avoids or critically reconfigures stereotypical cues (Seaborn, 17 Dec 2024).
• Regular audits and rigorous identity safety assessment frameworks for robot learning systems, particularly when using large pretrained models known to amplify stereotypes (Hundt et al., 2022).
• Explicit interdisciplinary reflection on the ethical consequences of reinforcing or challenging social roles via robots.
• Pragmatic design solutions such as "embracing the glitch" (intentional use of error or non-normative behavior to unsettle entrenched scripts), degendering, or sociomorphing (designing robots to interact socially without anthropomorphic mimicry) (Stedtler et al., 2 Oct 2025).
• Implementation of robust human-in-the-loop mechanisms and transparent apologies to contain negative spillover and group blame effects (Manoli et al., 8 Dec 2024).

6. Conceptual Foundations and Taxonomies

Philosophical analysis demonstrates that attributions of mental state and agency to robots are themselves structured by "folk-ontological stances"—categories such as realism, eliminativism, reductionism, fictionalism, and instrumentalism (Datteri, 17 Jun 2024). These stances, reflecting underlying beliefs about the reality of robot minds, drive both stereotyping and blame patterns. Users who hold folk-psychological realist attitudes toward robots are more prone to ascribe intentionality and blame in human-like ways, while those adopting eliminativist or agnostic stances are less likely to see robots as blameworthy agents. This provides a theoretical framework for interpreting empirical findings on anthropomorphism, dehumanization, and agency perception.

A simplified schema (see Table 1 in (Datteri, 17 Jun 2024)):

Ontological Stance	Blame Attributed	Human-Likeness Perceived
Realism	Human-like blame patterns	High psychological similarity
Non-realism/Eliminativism	Diminished robot blame	Low psychological similarity
Instrumentalism	Contextual, pragmatic	Variable, not ontologically committed

7. Future Research Directions

Ongoing research is necessary to address the spillover of blame and stereotype effects from HRI into HHI, track the changing landscape of group-level responsibility as robots proliferate, and empirically ground interventions for inclusive design. Empirical validation of design methods such as counter-stereotypical cues, degendering, and "embracing the glitch" is needed, as well as robust institutional frameworks for liability, safety audits, and transparent system documentation. Further investigation into the interplay between ontological stance and moral judgment will refine both the theoretical and practical handling of robot stereotyping and blame attribution as robots become more salient societal actors.