Are Machines Gender Neutral? Gender‐Stereotypic Responses to Computers With Voices

By Clifford Nass, Youngme Moon, Nancy Green. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Human-Computer Interaction

Citations in the corpus

LaMDA: Language Models for Dialog Applications (2022, background): “These expectations range from projecting social stereotypes [106] to reciprocating self-disclosure with interactive chat systems [105].”
Achieving customer value from electronic channels through identity commitment, calculative commitment, and trust in technology (2007, background): “…agued that when computers mediate interaction among individuals or when computers use expanded sensory channels, like voice or gesturing, to communicate with users, humanlike perceptions, such as gender stereotyping, may be attributed to computers (Nass, Moon, & Green, 1997; Reeves & Nass, 2000).”
AI-based chatbots in customer service and their effects on user compliance (2020, background): “For instance, CASA studies have shown that politeness norms (Nass et al. 1999), gender and ethnicity stereotypes (Nass and Moon 2000; Nass et al. 1997), personality response (Nass et al. 1995), and flattery effects (Fogg and Nass 1997) are also present in HCI.”
Sensitive questions in surveys. (2007, background): “Studies by Nass, Moon, and Green (1997) and by Sproull, Subramani, Kiesler, Walker, and Waters (1996) demonstrate that seemingly incidental features of the computer interface, such as the gender of the voice it uses (Nass et al., 1997), can evoke reactions similar to those produced by a person…”
A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application (2004, background): “Most ‘‘gender’’-related studies in human–computer interaction were concerned with ‘‘gender’’ manifested in the voice output as opposed to the voice input (Nass et al., 1997; Lee, 2003).”
Stereotype Threat in the Marketplace: Consumer Anxiety and Purchase Intentions (2011, background): “Women, compared to men, often are believed to be less competent in mathematics (Bradach and Eccles 1989; DarNimrod and Heine 2006; Spencer et al. 1999) as well as science, technology, and engineering (Martin, Wood, and Little 1990; Nass, Moon, and Green 1997).”
Effects of Mental Model Quality on Collaborative System Performance (2007, background): “6 interacted with these machines in a social way, even applying gender roles in one circumstance with the subtlest of cues (Nass et al., 1997).”
The media inequality: Comparing the initial human-human and human-AI social interactions (2017, background): “Participants reported a computer with a male voice to be more credible and dominant (Nass, Moon, & Green, 1997).”
Interactions With Robots: The Truths We Reveal About Ourselves (2017, background): “For example, just as women are assumed to have more caring competencies and men to have more agentic competencies (Huddy & Terkildsen 1993), computers with a female voice were rated more informative about love and relationships, whereas computers with a male voice were rated more knowledgeable about”
‘If you sound like me, you must be more human’: On the interplay of robot and user features on human-robot acceptance and anthropomorphism (2012, background): “…Acceptance and Anthropomorphism Friederike Eyssel Dieta Kuchenbrandt Simon Bobinger ++49 521 106 12044 Center of Excellence in Cognitive ++49 521 106 12042 feyssel@cit-ec.uni-bielefeld.de Interaction Technology (CITEC) sbobinge@cit-ec.uni-bielefeld.de Bielefeld University Universitätsstraße…”