Critical 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.

In comics, Textual Sound Effects (TE) can describe sounds, but also actions, events, etc. TE could be used in Virtual Environment to efficiently create an easily recognizable scene and add more information to objects at a relatively low design cost. We investigate the impact of TE in a Virtual Environment on objects’ material perception (on category and properties) and on sound perception (on volume [dB] and spatial position). Participants (N=13, repeated measures) categorized metallic and wooden spheres and significantly changed their reaction time depending on the TE congruence with the spheres’ material/sound. They then rated a sphere’s properties (i.e., wetness, warmness, softness, smoothness, and dullness) and significantly changed their rating depending on the TE. When comparing 2 sound volumes, they perceived a sound associated with a shrinking TE as less loud and a sound associated with a growing TE as louder. When locating an audio source location, they located it significantly closer to a TE.