Yes, feminine-coded presentation can change how people treat an AI assistant, and one 2026 experiment found lower monetary rewards for a female-presenting assistant. That result does not show that customers generally pay less for female-sounding products, that subscriptions should be cheaper, or that voice alone caused the difference. In the strongest direct study, gender presentation included the assistant’s name and visual embodiment as well as its voice.
The practical conclusion is narrower and more useful: designers should treat perceived gender as a consequential interface choice, test observed behavior rather than relying on stated preferences, and avoid turning a feminine voice into a cue for lower competence, greater submissiveness, or unpaid emotional labor.
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What the best direct evidence actually found
The clearest monetary evidence comes from a peer-reviewed article by Isabelle Cuber, Tarek Alakmeh, Mary Hausfeld, Anand van Zelderen, Moritz Jenny, Jochen Menges and Thomas Fritz in the Proceedings of the 14th Nordic Conference on Human-Computer Interaction (NordiCHI ’26), published October 3, 2026. The University of Limerick research record identifies it as a conference article (DOI: 10.1145/3829807.3829910).
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In a controlled virtual-reality workplace experiment, 189 knowledge workers completed tasks with functionally identical assistants, rated them and allocated monetary rewards. The conditions ranged from a text chatbot and a desk robot to human-like assistants presented as Johanna (female) and Johan (male). Participants were assigned to either the male-presenting or female-presenting human-like condition rather than directly comparing both assistants themselves. Afterward, researchers interviewed 34 participants.
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More human-like assistants received more reward overall. Within the human-like comparison, the female-presenting assistant was judged less human-like and received lower rewards than the male-presenting counterpart, despite technical identity. This is a group-level result from one constructed workplace setting, not a retail price observation. The study does not provide a verified percentage difference that can be applied to products or customers generally.
Thomas Fritz, a study co-author and Professor of Informatics at the University of Zurich, said he was surprised that male-presenting assistants were perceived as more human and received higher rewards even though they were technically identical. The experiment changed representation, not underlying capability. Fritz also said the researchers “can’t conclusively say what causes this discrepancy.” The lower reward could reflect visual embodiment, the names, perceived human likeness, gender stereotypes or interactions among those cues; the design does not isolate sound.
“Sound feminine” is not one design variable
Users infer gender from several signals that can reinforce or contradict one another. A voice may be perceived as feminine or masculine, but an assistant can also be coded through:
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- Visual embodiment: a human-like avatar, a robot body, a text-only interface or no visible body.
- Name and language: names such as Johanna or Johan, pronouns, honorifics and self-descriptions.
- Behavior: apologizing, expressing warmth, taking initiative, refusing requests or performing emotional support.
- Context: whether the task is technical, administrative, social, shopping-related or workplace-oriented.
Because these cues are bundled in the 2026 virtual-reality experiment, “female AI was paid less” is too broad a summary. The defensible statement is that a female-presenting, human-like assistant in that experiment received lower allocated rewards and was perceived as less human-like than a technically identical male-presenting counterpart.
Does this mean people pay less for feminine-sounding AI?
No general consumer willingness-to-pay effect has been established. The reward allocation in the NordiCHI study was an experimental decision made after work tasks. It was not a subscription checkout, a software price test, a market survey or a labor-market wage measurement. The reviewed evidence does not show that users would demand a discount for a feminine voice, that companies can charge more for a masculine voice, or that feminine-sounding products have lower market prices.
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“Pay” can describe several different outcomes, and they should not be conflated:
| Outcome | What the evidence says | What it does not establish |
|---|---|---|
| Experimental reward allocation | Lower allocations went to the female-presenting assistant than to the male-presenting counterpart in the 2026 VR workplace experiment. | A universal percentage penalty or a real-world price difference. |
| Subscription or purchase willingness | No direct study in the reviewed evidence measures checkout behavior or willingness to pay for feminine-sounding assistants. | That customers generally pay less, or that pricing should vary by perceived gender. |
| Compensation for AI labor | No verified labor-market wage gap for AI products was shown. | That feminine-coded agents are systematically assigned less valuable work or lower pay in the economy. |
A reward experiment is still important: it demonstrates that representation can affect an immediate economic judgment even when capabilities are held constant. It should be treated as a warning about bias, not as a pricing formula.
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The 2025 simulated-shopping study
A Johns Hopkins University report dated January 16, 2025 described an in-person study with 40 participants using a simulated assistant for an online-shopping task. Researchers varied feminine, masculine and gender-neutral voice types and also varied how the assistant handled an error: an apology or monetary compensation.
Men interrupted the assistant more often during errors and behaved more socially toward the feminine voice in that experiment. A neutral voice paired with an apology reduced impolite interactions and interruptions, but participants perceived that combination as less warm and more robotic. These observations are specific to a small shopping-and-error scenario. They do not justify saying that users always interrupt female-voiced assistants or always prefer them socially.
Amama Mahmood, the study lead and a PhD student in the Whiting School’s Department of Computer Science, argued that thoughtful gender portrayal is essential for effective support without reinforcing harmful stereotypes. The finding also illustrates why voice cannot be evaluated apart from behavior: apology style and compensation changed the interaction as much as the voice condition.
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The 2024 voice, accent and task experiment
A Korea University research record describes a 2024 experiment with 270 participants. It systematically varied Siri’s voice gender, American or Indian accent, task type (social or functional) and an additional gender-match condition. The design matters because a response to a voice can depend on accent, what the assistant is being asked to do and whether the listener identifies with the agent’s perceived gender.
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This study does not turn femininity into a single, stable effect. It supports a more cautious model: voice gender interacts with social context, accent and the person using the system. A result observed during a social conversation may not carry over to a calendar task, technical troubleshooting or a purchase decision.
Why stated opinions and behavior can diverge
The University of Zurich account of the 2026 experiment reports that many interviewees said gender did not matter to them, while their reward allocations differed between conditions. This gap is common in sensitive judgments: people may sincerely endorse equal treatment and still respond to cues outside conscious awareness, or they may interpret the same cue differently in an unfamiliar setting.
The interviews do not identify one cause. Possible explanations include stereotypes about competence, differences in perceived humanness, expectations about who should provide service, or discomfort with a human-like female agent. Since the researchers did not manipulate voice independently of embodiment and name, none of these explanations can be presented as proven.
Could gendered assistants change attitudes toward people?
Evidence about spillover into broader gender stereotypes is mixed, and it measures a different outcome from treatment of the assistant itself. An open-access 2022 study summary describes work on perceived voice gender and agency and references earlier findings in which exposure to a female-featured assistant affected some participants’ expectations and treatment of female subordinates at work. A 2025 study summary, by contrast, reports no statistically significant effect of gendered-assistant interaction on the explicit stereotype measures it tested.
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These findings cannot be combined into a single claim that assistants either cause or do not cause sexism. The studies differ in sample, task, exposure, measures and whether they test behavior, implicit expectations or explicit questionnaire responses. A designer should therefore monitor both immediate interactions with the system and any evidence of downstream effects, while avoiding claims that one brief interaction will transform users’ attitudes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the study sizes do—and do not—tell you
| Study or record | Participants | Setting and measured outcome |
|---|---|---|
| 2026 NordiCHI VR workplace experiment | 189 participants; 34 post-experiment interviewees | Work tasks, assistant ratings and monetary reward allocation; human likeness and gender presentation. |
| 2025 Johns Hopkins study | 40 participants | Simulated shopping, interruptions, politeness and responses to apology or compensation. |
| 2024 Korea University experiment | 270 participants | Voice gender, American or Indian accent, social or functional task, and gender-match condition. |
These are study-specific samples, not prevalence estimates. None supplies a population-wide effect size for how much less money a feminine assistant receives, and the institutional reports reviewed do not provide a reliable percentage difference. Small or highly controlled studies can reveal mechanisms worth testing without predicting every user or market.
How to design and evaluate an assistant responsibly
Separate the cues before drawing conclusions
If you want to know whether voice matters, hold the avatar, name, wording, task and capabilities constant and vary only the voice. Run separate tests for visual embodiment and naming. Otherwise, a result may be attributed to “feminine sound” when it actually reflects a human-like face, a gendered name or a different conversational script.
Measure behavior, not only preference surveys
Ask users what they prefer, but also record interruptions, task completion, correction acceptance, trust ratings, escalation requests, reward allocations and abusive language. The 2026 findings show why stated beliefs alone are insufficient: participants often said gender did not matter while their observed allocations differed.
Test across task and user contexts
Use functional and social tasks, include different accents where relevant, and examine whether participant–agent gender matching changes responses. A voice that feels warm in customer support may be judged less authoritative in a technical workflow. Report the context with every result rather than publishing one overall “female voice effect.”
Avoid stereotype-loaded behavior
Do not make a feminine-coded assistant automatically more apologetic, deferential, emotionally available or responsible for smoothing over errors. Give every voice the same authority to refuse unsafe requests, state uncertainty, correct a user and request compensation or escalation when appropriate. If warmth is a product goal, test it independently from gender presentation.
Audit economic decisions for parity
Where users allocate tips, bonuses, ratings or other rewards, compare technically identical agents across representation conditions. Predefine the outcome, record uncertainty and avoid converting one experiment into a commercial pricing rule. If a gap appears, investigate perceived humanness, competence and task expectations before changing the assistant’s price or capability.
How to read claims about feminine AI assistants
- “Users treat female voices differently”: ask which behavior, task, sample and error response were measured.
- “Female AI gets paid less”: check whether “paid” means an experimental reward allocation or an actual purchase and subscription transaction.
- “The voice caused the bias”: verify that avatar, name, accent, wording and embodiment were controlled.
- “People prefer feminine assistants”: distinguish warmth or social engagement from competence, trust, obedience and economic value.
- “Gendered assistants change sexism”: check whether the study measured direct treatment, implicit expectations or explicit stereotype questionnaires.
The answer for product teams and users
For product teams, perceived gender is not cosmetic. It can influence perceived humanness, interruptions, politeness and experimental rewards, so representation belongs in usability, safety and fairness testing. The appropriate response is not to assume that a masculine voice will sell better or to remove every human cue automatically. It is to test alternatives, publish the context and ensure that capability, authority and compensation are not tied to a gender stereotype.
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