The Effect of Population and "Structural" Biases on Social Media-based Algorithms

Johnson, Isaac; McMahon, Connor; Schöning, Johannes; Hecht, Brent

doi:10.1145/3025453.3026015

Cited by 47 publications

(11 citation statements)

References 45 publications

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“…Edelman and Luca also found non-Black hosts were able to charge approximately 12% more than Black hosts, holding location, rental characteristics, and quality constant [27]. Colley et al found Pokémon GO advantaged urban, white, non-Hispanic populations, for example, potentially attracting more tourist commerce to their neighborhoods [15], and Johnson et al found that geolocation inference algorithms exhibited substantially worse performance for underrepresented populations, i.e., rural users [47].…”

Section: Background Algorithmic Fairnessmentioning

confidence: 99%

A Qualitative Exploration of Perceptions of Algorithmic Fairness

Woodruff

Fox

Rousso-Schindler

et al. 2018

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

192

113

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Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

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Section: Background Algorithmic Fairnessmentioning

confidence: 99%

A Qualitative Exploration of Perceptions of Algorithmic Fairness

Woodruff

Fox

Rousso-Schindler

et al. 2018

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

192

113

View full text Add to dashboard Cite

show abstract

“…In the accessibility literature, design choices have repeatedly been shown to discriminate against disabled users [16]. Systemic technological biases against rural users and users of lower socioeconomic status have also been shown in the context of social media algorithms [7] and the sharing economy [13]. The consequences of biased design are particularly severe for vulnerable and marginalized populations.…”

Section: Discussionmentioning

confidence: 99%

Gender-Inclusive Design

Metaxa

Wang

Landay

et al. 2018

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

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We interact with dozens of web interfaces on a daily basis, making inclusive web design practices more important than ever. This paper investigates the impacts of web interface design on ambient belonging, or the sense of belonging to a community or culture. Our experiment deployed two content-identical webpages for an introductory computer science course, differing only in aesthetic features such that one was perceived as masculine while the other was gender-neutral. Our results confirm that young women exposed to the masculine page are negatively affected, reporting significantly less ambient belonging, interest in the course and in studying computer science broadly. They also experience significantly more concern about others' perception of their gender relative to young women exposed to the neutral page, while no similar effect is seen in young men. These results suggest that gender biases can be triggered by web design, highlighting the need for inclusive user interface design for the web.

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“…This effect influences the design of geolocation inference algorithms, which have been shown to exhibit significantly worse performance for underrepresented populations (i.e. rural users), even when overcorrecting for populations biases [26].…”

Section: Media Bias In Communication and Social Mediamentioning

confidence: 99%

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

Ali

Stratmann

Park

et al. 2018

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

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This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.

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The Effect of Population and "Structural" Biases on Social Media-based Algorithms

Cited by 47 publications

References 45 publications

A Qualitative Exploration of Perceptions of Algorithmic Fairness

A Qualitative Exploration of Perceptions of Algorithmic Fairness

Gender-Inclusive Design

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

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