Operationalizing Perceptions of Agent Gender: Foundations and GuidelinesThe “gender” of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people's interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours—from preferences to toxicity to stereotyping—across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how agent gender has been operationalized—labelled, defined, and measured—as a perceptual variable. One-third of studies manipulated but did not measure agent gender. Norms in operationalizations remain obscure, limiting comprehension of results, congruity in measurement, and comparability for meta-analyses. The dominance of the gender binary model and latent anthropocentrism have placed arbitrary limits on knowledge generation and reified the status quo. We contribute a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.2026KSKatie Seaborn et al.Institute of Science TokyoAgent Personality & AnthropomorphismGender & Race Issues in HCITechnology Ethics & Critical HCICHI
Radical Gender Neutrality: Agender Euphoria in Gaming and Play ExperiencesAgender euphoria is a new term representing the powerful feelings of happiness, joy, and contentment derived from experiences in gender-free embodiments, spaces, and activities. People with and without agender and adjacent identities (e.g., genderless, gender-free, non-binary, gender-apathetic) may have such experiences under the right circumstances. Video games can offer gender minorities a safe haven for gender euphoric experiences. However, the possibility of agender euphoric experiences was unexplored. We considered this overlooked frame of self-actualization with 142 people who identified as having or desiring agender euphoric experiences. Using the critical incident technique (CIT), we uncovered how games and play experiences create (and inhibit) agender euphoria. We surface this experiential phenomenon and provide empirically-grounded criteria for the design of games to elicit agender euphoric experiences for everyone, but especially agender and agender adjacent players. This work adds to the growing critical literatures on marginalized experiences in games research and human-computer interaction.2026KSKatie Seaborn et al.Institute of Science TokyoGame UX & Player BehaviorGender & Race Issues in HCIEmpowerment of Marginalized GroupsCHI
Access Over Deception: Fighting Deceptive Patterns through AccessibilityDeceptive patterns, i.e. dark patterns and manipulative user interfaces (UI), are a widely used design method that aims to manipulate users to act against their own interests. These patterns may particularly influence people with less education, visual impairments, and older adults. Yet, access is a critical feature of the user experience (UX), development standards, and law. We considered whether and how the Web Content Accessibility Guidelines (WCAG) and related legislation, such as the European Accessibility Act (EAA), can act as a tool against deceptive patterns. We used these guidelines and legal statues in a heuristic evaluation to analyze whether and how deceptive patterns violate or conform to these standards. Although statistical analysis revealed no significant relationship, we identified three patterns implicated by the WCAG guidelines: Countdown Timer, Auto-Play, and Hidden Information. We offer this approach as one tool in the fight against UI-based deception and in support of inclusive design.2026TPTobias Pellkvist et al.TU WienDark Patterns RecognitionUniversal & Inclusive DesignPrivacy by Design & User ControlCHI
Hearing Ambiguity: Exploring Beyond-Gender Impressions of Artificial Ambiguous VoicesVoice perception plays a fundamental role in all types of interactions, from human-to-human communication to human-technology interaction. When it comes to technology, we sometimes have the option to choose the type of voice we want to hear. But why is the default (almost) always a feminine or masculine voice? In this research, we evaluated user perceptions of gender-ambiguous voices, a relatively unexplored option. In our novel comparative study, we evaluated six gender-ambiguous voices with participants of diverse gender identities (men, women, and non-binary individuals), with 74 participants in each group. Additionally, half of the participants were told in advance that the voices had been designed to be gender-ambiguous, and half were not. We aimed to move beyond subjective perceptions of voice gender by exploring how such voices are perceived across different dimensions: trustworthiness, appeal, comfort, anthropomorphism, and aversion. Our findings reveal that while men and women had similar perceptions, non-binary participants rated the voices more negatively, with lower trust and higher aversion. Interestingly, priming participants about the voices' ambiguity did not significantly affect overall perceptions, though it increased critical evaluations from non-binary individuals. These findings contribute to growing research on gender-ambiguous voices by providing perceptual comparisons of multiple voices and highlighting the need for more inclusive voice designs that appeal to non-binary users.2025MCMartina De Cet et al.Voice User Interface (VUI) DesignMultilingual & Cross-Cultural Voice InteractionAgent Personality & AnthropomorphismCUI
Inter(sectional) Alia(s): Ambiguity in Voice Agent Identity via Intersectional Japanese Self-ReferentsConversational agents that mimic people have raised questions about the ethics of anthropomorphizing machines with human social identity cues. Critics have also questioned assumptions of identity neutrality in humanlike agents. Recent work has revealed that intersectional Japanese pronouns can elicit complex and sometimes evasive impressions of agent identity. Yet, the role of other ``neutral'' non-pronominal self-referents (NPSR) and voice as a socially expressive medium remains unexplored. In a crowdsourcing study, Japanese participants (N=204) evaluated three ChatGPT voices (Juniper, Breeze, and Ember) using seven self-referents. We found strong evidence of voice gendering alongside the potential of intersectional self-referents to evade gendering, i.e., ambiguity through neutrality and elusiveness. Notably, perceptions of age and formality intersected with gendering as per sociolinguistic theories, especially ぼく (boku) and わたくし (watakushi). This work provides a nuanced take on agent identity perceptions and champions intersectional and culturally-sensitive work on voice agents.2025TFTakao Fujii et al.Institute of Science Tokyo, Department of Industrial Engineering and EconomicsIntelligent Voice Assistants (Alexa, Siri, etc.)Multilingual & Cross-Cultural Voice InteractionAgent Personality & AnthropomorphismCHI
Super Kawaii Vocalics: Amplifying the “Cute” Factor in Computer Voice"Kawaii" is the Japanese concept of cute, which carries sociocultural connotations related to social identities and emotional responses. Yet, virtually all work to date has focused on the visual side of kawaii, including in studies of computer agents and social robots. In pursuit of formalizing the new science of kawaii vocalics, we explored what elements of voice relate to kawaii and how they might be manipulated, manually and automatically. We conducted a four-phase study (grand 𝑁 = 512) with two varieties of computer voices: text-to-speech (TTS) and game character voices. We found kawaii "sweet spots" through manipulation of fundamental and formant frequencies, but only for certain voices and to a certain extent. Findings also suggest a ceiling effect for the kawaii vocalics of certain voices. We offer empirical validation of the preliminary kawaii vocalics model and an elementary method for manipulating kawaii perceptions of computer voice.2025YMYuto Mandai et al.Tokyo Institute of Technology, Department of Industrial Engineering and EconomicsIntelligent Voice Assistants (Alexa, Siri, etc.)Agent Personality & AnthropomorphismCHI
Cross-Cultural Validation of Partner Models for Voice User Interfaces Recent research has begun to assess people's perceptions of voice user interfaces (VUIs) as dialogue partners, termed partner models. Current self-report measures are only available in English, limiting research to English-speaking users. To improve the diversity of user samples and contexts that inform partner modelling research, we translated, localized, and evaluated the Partner Modelling Questionnaire (PMQ) for non-English speaking Western (German, n=185) and East Asian (Japanese, n=198) cohorts where VUI use is popular. Through confirmatory factor analysis (CFA), we find that the scale produces equivalent levels of “goodness-to-fit” for both our German and Japanese translations, confirming its cross-cultural validity. Still, the structure of the communicative flexibility factor did not replicate directly across Western and East Asian cohorts. We discuss how our translations can open up critical research on cultural similarities and differences in partner model use and design, whilst highlighting the challenges for ensuring accurate translation across cultural contexts.2024KSKatie Seaborn et al.Voice User Interface (VUI) DesignMultilingual & Cross-Cultural Voice InteractionCUI
Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPTChatGPT is a conversational agent built on a large language model. Trained on a significant portion of human output, ChatGPT can mimic people to a degree. As such, we need to consider what social identities ChatGPT simulates (or can be designed to simulate). In this study, we explored the case of identity simulation through Japanese first-person pronouns, which are tightly connected to social identities in intersectional ways, i.e., intersectional pronouns. We conducted a controlled online experiment where people from two regions in Japan (Kanto and Kinki) witnessed interactions with ChatGPT using ten sets of first-person pronouns. We discovered that pronouns alone can evoke perceptions of social identities in ChatGPT at the intersections of gender, age, region, and formality, with caveats. This work highlights the importance of pronoun use for social identity simulation, provides a language-based methodology for culturally-sensitive persona development, and advances the potential of intersectional identities in intelligent agents.2024TFTakao Fujii et al.Tokyo Institute of TechnologyMultilingual & Cross-Cultural Voice InteractionAgent Personality & AnthropomorphismHuman-LLM CollaborationCHI
Play Across Boundaries: Exploring Cross-Cultural Maldaimonic Game ExperiencesMaldaimonic game experiences occur when people engage in personally fulfilling play through egocentric, destructive, and/or exploitative acts. Initial qualitative work verified this orientation and experiential construct for English-speaking Westerners. In this comparative mixed methods study, we explored whether and how maldaimonic game experiences and orientations play out in Japan, an Eastern gaming capital that may have cultural values incongruous with the Western philosophical basis underlying maldaimonia. We present findings anchored to the initial frameworks on maldaimonia in game experiences that show little divergence between the Japanese and US cohorts. We also extend the qualitative findings with quantitative measures on affect, player experience, and the related constructs of hedonia and eudaimonia. We confirm this novel construct for Japan and set the stage for scale development.2024KSKatie Seaborn et al.Tokyo Institute of TechnologyRole-Playing & Narrative GamesChronic Disease Self-Management (Diabetes, Hypertension, etc.)Inclusive DesignCHI
Wizundry: A Cooperative Wizard of Oz Platform for Simulating Future Speech-based Interfaces with Multiple WizardsWizard of Oz (WoZ) as a prototyping method has been used to simulate intelligent user interfaces, particularly for speech-based systems. However, as our societies' expectations on artificial intelligence (AI) grows, the question remains whether a single Wizard is sufficient for it to simulate smarter systems and more complex interactions. Optimistic visions of 'what artificial intelligence (AI) can do' places demands on WoZ platforms to simulate smarter systems and more complex interactions. This raises the question of whether the typical approach of employing a single Wizard is sufficient. Moreover, while existing work has employed multiple Wizards in WoZ studies, a Multi-Wizard approach has not been systematically studied in terms of feasibility, effectiveness, and challenges. We offer Wizundry, a real-time, web-based WoZ platform that allows multiple Wizards to collaboratively operate a speech-to-text based system remotely. We outline the design and technical specifications of our open-source platform, which we iterated over two design phases. We report on two studies in which participant-Wizards were tasked with negotiating how to cooperatively simulate an interface that can handle natural speech for dictation and text editing as well as other intelligent text processing tasks. We offer qualitative findings on the Multi-Wizard experience for Dyads and Triads of Wizards. Our findings reveal the promises and challenges of the Multi-Wizard approach and open up new research questions.2023SHSiying Hu et al.MethodsCSCW
Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese AppsDark patterns are deceptive and malicious properties of user interfaces that lead the end-user to do something different from intended or expected. While now a key topic in critical computing, most work has been conducted in Western contexts. Japan, with its booming app market, is a relatively uncharted context that offers culturally- and linguistically-sensitive differences in design standards, contexts of use, values, and language, all of which could influence the presence and expression of dark patterns. In this work, we analyzed 200 popular mobile apps in the Japanese market. We found that most apps had dark patterns, with an average of 3.9 per app. We also identified a new class of dark pattern: “Linguistic Dead-Ends” in the forms of “Untranslation” and “Alphabet Soup.” We outline the implications for design and research practice, especially for future cross-cultural research on dark patterns.2023SHShun Hidaka et al.Tokyo Institute of TechnologyDark Patterns RecognitionCHI
Transcending the "Male Code": Implicit Masculine Biases in NLP ContextsCritical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are “coded” into language and the assumption of “male” as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.2023KSKatie Seaborn et al.Tokyo Institute of TechnologyAgent Personality & AnthropomorphismAI Ethics, Fairness & AccountabilityCHI
What Pronouns for Pepper? A Critical Review of Gender/ing in ResearchGender/ing guides how we view ourselves, the world around us, and each other—including non-humans. Critical voices have raised the alarm about stereotyped gendering in the design of socially embodied artificial agents like voice assistants, conversational agents, and robots. Yet, little is known about how this plays out in research and to what extent. As a first step, we critically reviewed the case of Pepper, a gender-ambiguous humanoid robot. We conducted a systematic review (n=75) involving meta-synthesis and content analysis, examining how participants and researchers gendered Pepper through stated and unstated signifiers and pronoun usage. We found that ascriptions of Pepper’s gender were inconsistent, limited, and at times discordant, with little evidence of conscious gendering and some indication of researcher influence on participant gendering. We offer six challenges driving the state of affairs and a practical framework coupled with a critical checklist for centering gender in research on artificial agents.2022KSKatie Seaborn et al.Tokyo Institute of TechnologyAgent Personality & AnthropomorphismSocial Robot InteractionGender & Race Issues in HCICHI
Scaling Up to Tackle Low Levels of Urban Food Waste RecyclingAddressing societal problems is complex; little is known about which paths or approaches are successful. We discuss what is involved in knowing when and how and for whom change needs to occur, as well as the impact of doing so at scale—especially when novelty and academic contributions may be compromised. To this end, we present a ‘scaling up’ framework based on a societal project where we worked with multiple stakeholders to improve food waste recycling rates in a housing estate. We propose three main factors involved in scaling up: (i) ‘the people,’ through reimagining roles and relationships, (ii) ‘the method,’ requiring flexibility in design and research, and (iii) ‘the impact,’ informing new measures by handing over the evaluation. We reflect on the challenges, dilemmas, and successes encountered, as well as discuss the benefit of ‘handing over’ the evaluation process to gather scalable metrics based on economic modelling.2020KSKatie Seaborn et al.Sustainable HCIEcological Design & Green ComputingClimate Change Communication ToolsDIS