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However, most current SIAs are grounded in White- and Western-centric assumptions, limiting their ability to express and interpret social cues appropriately across cultures. Here, we demonstrate how the data-driven psychophysical method of reverse correlation can help address these limitations by modeling users\u2019 perceptual expectations, preferences, and sociocultural norms and strategically integrating these insights into SIA design. Drawing on examples from our research group, we show how this method could enable SIAs to exhibit social signals that are psychologically grounded, culturally adaptive, and ethnically inclusive. By informing the design of SIA appearance and expressive behavior with empirically derived user models, our approach aims to improve user engagement and trust while contributing to broader efforts to mitigate algorithmic bias, reduce access inequality, and challenge real-world prejudice in both human-AI and human\u2013human interaction contexts.<\/jats:p>","DOI":"10.1007\/s12193-026-00472-9","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T17:33:59Z","timestamp":1770312839000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-driven psychophysical methods to diversify SIAs and address bias"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-8616","authenticated-orcid":false,"given":"Valentina","family":"Gosetti","sequence":"first","affiliation":[]},{"given":"Rachael E.","family":"Jack","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"472_CR1","doi-asserted-by":"publisher","DOI":"10.1177\/17298814211066740","volume":"18","author":"N Gasteiger","year":"2025","unstructured":"Gasteiger N, Hellou M, Ahn HS (2025) Deploying social robots in museum settings: a quasi-systematic review exploring purpose and acceptability. 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