The Whiteness of AI

Cave, Stephen; Dihal, Kanta

doi:10.1007/s13347-020-00415-6

Cited by 142 publications

(73 citation statements)

References 36 publications

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“…Accurate age estimation has applications to a range of settings, from facilitating medical diagnosis to early resolution of criminal cases. Although AI algorithms for face recognition (FR) and estimation of age are being developed (e.g., Escalante-B and Wiskott 2020 ; Zhu et al 2018 ), most of these algorithms are biased against non-Caucasian races (Cave and Dihal 2020 ), consequently affecting the classification of emotional facial expressions. In this regard, Rhue ( 2018 ) found that FR algorithms attribute more negative emotions to black men’s faces than white men’s faces.…”

Section: Discussionmentioning

confidence: 99%

Age of smile: a cross-cultural replication report of Ganel and Goodale (2018)

et al. 2021

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Smiling is believed to make people look younger. Ganel and Goodale (Psychon Bull Rev 25(6):612–616, 10.3758/s13423-017-1306-8, 2018) proposed that this belief is a misconception rooted in popular media, based on their findings that people actually perceive smiling faces as older. However, they did not clarify whether this misconception can be generalized across cultures. We tested the cross-cultural validity of Ganel and Goodale’s findings by collecting data from Japanese and Swedish participants. Specifically, we aimed to replicate Ganel and Goodale’s study using segregated sets of Japanese and Swedish facial stimuli, and including Japanese and Swedish participants in groups asked to estimate the age of either Japanese or Swedish faces (two groups of participants × two groups of stimuli; four groups total). Our multiverse analytical approach consistently showed that the participants evaluated smiling faces as older in direct evaluations, regardless of the facial stimuli culture or their nationality, although they believed that smiling makes people look younger. Further, we hypothesized that the effect of wrinkles around the eyes on the estimation of age would vary with the stimulus culture, based on previous studies. However, we found no differences in age estimates by stimulus culture in the present study. Our results showed that we successfully replicated Ganel and Goodale (2018) in a cross-cultural context. Our study thus clarified that the belief that smiling makes people look younger is a common cultural misconception.

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Section: Discussionmentioning

confidence: 99%

Age of smile: a cross-cultural replication report of Ganel and Goodale (2018)

et al. 2021

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“…Although AI algorithms for face recognition (FR) and estimation of age are being developed (e.g., Escalante-B. and Wiskott, 2020;Zhu et al, 2018), most of these algorithms are biased against non-Caucasian races (Cave & Dihal, 2020), consequently affecting the classification of emotional facial expressions. In this regard, Rhue (2018) found that FR algorithms attribute more negative emotions to black men's faces than white men's faces.…”

Section: Discussionmentioning

confidence: 99%

Age of smile: A cross-cultural replication report of Ganel and Goodale (2018)

Yoshimura¹,

Morimoto²,

Murai³

et al. 2020

Preprint

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Smiling is believed to make people look younger. Ganel and Goodale (2018) proposed that this belief is a misconception rooted in popular media, based on their findings that people actually perceive smiling faces as older. However, they did not clarify whether this misconception can be generalized across cultures. We tested the cross-cultural validity of Ganel and Goodale’s findings by collecting data from Japanese and Swedish participants. Specifically, we aimed to replicate Ganel and Goodale’s study using segregated sets of Japanese and Swedish facial stimuli, and including Japanese and Swedish participants in groups asked to estimate the age of either Japanese or Swedish faces (two groups of participants × two groups of stimuli; four groups total). Our multiverse analytical approach consistently showed that the participants evaluated smiling faces as older in direct evaluations, regardless of the facial stimuli culture or their nationality, although they believed that smiling makes people look younger. Further, we hypothesized that the effect of wrinkles around the eyes on the estimation of age would vary with the stimulus culture, based on previous studies. However, we found no differences in age estimates by stimulus culture in the present study. Our results showed that we successfully replicated Ganel and Goodale (2018) in a cross-cultural context. Our study thus clarified that the belief that smiling makes people look younger is a common cultural misconception.

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“…We plan to unpack all the above with an eye towards a collective re-imagining of the computational sciences. To do this, we implore computational scientists to be aware of their fi elds' histories (Cave & Dihal 2020;Roberts, BareketShavit, Dollins, Goldie, & Mortenson 2020;Saini 2019;Syed 2020;Winston 2020) and we propose that through such an awakening we can begin to forge a decolonised future. We also hope our article encourages researchers to consciously avoid repeating previous mistakes, some of which are crimes against humanity, like eugenics (Saini 2019).…”

mentioning

confidence: 99%

“…Ultimately, our goal is to make inroads upon radically decolonised computational sciences (cf. Birhane 2019;Cave & Dihal 2020).…”

mentioning

confidence: 99%

Towards Decolonising Computational Sciences

Birhane¹,

Guest²

2021

KKF

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This article sets out our perspective on how to begin the journey of decolonising computational fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour; and b) rejection of the idea that centring individual people is a solution to system-level problems. The longer we ignore these two steps, the more “our” academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fi elds’ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences — including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away fromthese fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurturesdemographically diverse researchers and ideas that critically challenge the status quo.

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The Whiteness of AI

Cited by 142 publications

References 36 publications

Age of smile: a cross-cultural replication report of Ganel and Goodale (2018)

Age of smile: a cross-cultural replication report of Ganel and Goodale (2018)

Age of smile: A cross-cultural replication report of Ganel and Goodale (2018)

Towards Decolonising Computational Sciences

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