Brick by brick

Rader, Emilee; Echelbarger, Margaret; Cassell, Justine

doi:10.1145/1978942.1979382

Cited by 13 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work replicates results from Rader et al (2011) by showing that biculturals switch their interpretive frames and related cognitive processing as a function of culturally congruent accent. The results here are also consistent with the work of Nass and colleagues and show that accent can be a strong cue in how users treat computers as social actors (Dahlbäck et al 2007;Nass and Brave 2007;Reeves and Nass 1996).…”

Section: Discussionsupporting

confidence: 91%

“…Nass and colleagues have demonstrated that people communicate with computers as social actors, even when there is no visual representation of a graphically rendered virtual character (Nass and Brave 2007;Nass and Yen 2010;Reeves and Nass 1996). In cases where there is a graphical representation of an animated virtual character, Blascovich defines communicative realism as a character's fidelity to human-like movements, anthropometrics, and photographic realism (Blascovich and McCall 2013;Blascovich 2002Blascovich , 2013.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The cultural influence model: when accented natural language spoken by virtual characters matters

et al. 2014

View full text Add to dashboard Cite

Advances in artificial intelligence and computer graphics digital technologies have contributed to a relative increase in realism in virtual characters. Preserving virtual characters' communicative realism, in particular, joined the ranks of the improvements in natural language technology, and animation algorithms. This paper focuses on culturally relevant paralinguistic cues in nonverbal communication. We model the effects of an Englishspeaking digital character with different accents on human interactants (i.e., users). Our cultural influence model proposes that paralinguistic realism, in the form of accented speech, is effective in promoting culturally congruent cognition only when it is self-relevant to users. For example, a Chinese or Middle Eastern English accent may be perceived as foreign to individuals who do not share the same ethnic cultural background with members of those cultures. However, for individuals who are familiar and affiliate with those cultures (i.e., in-group members who are bicultural), accent not only serves as a motif of shared social identity, it also primes them to adopt culturally appropriate interpretive frames that influence their decision making.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

The cultural influence model: when accented natural language spoken by virtual characters matters

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Further, the number of papers that matched race keywords a priori was nearly the same number of papers that were coded for focus on race a posteriori. Focus on race was comprised of one or more of the following: 1) selecting participants from specific sets of racial or ethnic groups [15,75,104], 2) developing or evaluating HCI interventions aimed at helping individuals from a particular racial or ethnic group [46,76,89], or 3) detailing the ways race or ethnicity impact technology use, appropriation, or creation [8,50,102].…”

Section: Racementioning

confidence: 99%

“…Nearly a quarter of the manuscripts in the corpus matched to a keyword in the class umbrella category a priori, with an even larger number of publications including a focus on class a posteriori. Focus on class included one or more of the following: 1) recruiting participants from distinct socioeconomic classes [60,65,79], 2) investigating HCI interventions for communities of a specific socio-economic class [76,86,100], 3) reporting the ways certain class groups operate differentially from one another [25,64,90]. to better understand what designs would most help their participant population [26,60,90,100].…”

Section: Classmentioning

confidence: 99%

Intersectional HCI

Schlesinger

Edwards

Grinter

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

289

View full text Add to dashboard Cite

Understanding users becomes increasingly complicated when we grapple with various overlapping attributes of an individual's identity. In this paper we introduce intersectionality as a framework for engaging with the complexity of users'-and authors'-identities, and situating these identities in relation to their contextual surroundings. We conducted a meta-review of identity representation in the CHI proceedings, collecting a corpus of 140 manuscripts on gender, ethnicity, race, class, and sexuality published between 1982-2016. Drawing on this corpus, we analyze how identity is constructed and represented in CHI research to examine intersectionality in a human-computer interaction (HCI) context. We find that previous identity-focused research tends to analyze one facet of identity at a time. Further, research on ethnicity and race lags behind research on gender and socioeconomic class. We conclude this paper with recommendations for incorporating intersectionality in HCI research broadly, encouraging clear reporting of context and demographic information, inclusion of author disclosures, and deeper engagement with identity complexities.

show abstract

“…Not surprisingly, chatbots, and conversational agents more broadly, are already being employed to build better worlds, both in research and in industry. There have been a range of studies involving conversational agents presented at CHI that have artificial agents involved in nursing, educational settings, activism, and conflict resolution [58,66,68,80,81]. Much of this work is focused on the interaction interface, studying things like the impacts of various avatars on embodied agents.…”

Section: Building Better Worldsmentioning

confidence: 99%

Let's Talk About Race

Schlesinger

O’Hara

Taylor

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Why is it so hard for AI chatbots to talk about race? By researching databases, natural language processing, and machine learning in conjunction with critical, intersectional theories, we investigate the technical and theoretical constructs underpinning the problem space of race and chatbots. We explore how the context of database corpora, the syntactic focus of language processing, and the unadjustable nature of deep learning algorithms cause bots to have difficulty handling race-talk. In each focus area, the tensions of this problem space open up possibilities for creating new technologies, theories, and relationships between people and machines. Through making tangible the abstract and disparate qualities involved in working with race and chatbots, we can pursue possible futures where chatbots are more capable of handling race-talk in its many forms. In this paper, we provide the HCI community with ways to tackle the question, how can chatbots handle racetalk in new and improved ways?

show abstract

Brick by brick

Cited by 13 publications

References 7 publications

The cultural influence model: when accented natural language spoken by virtual characters matters

The cultural influence model: when accented natural language spoken by virtual characters matters

Intersectional HCI

Let's Talk About Race

Contact Info

Product

Resources

About