Up the Africanist: the possibilities and problems of ‘studying up’ in Africa

Peters, Rebecca Amanda Warne; Wendland, Claire

doi:10.1080/21681392.2016.1244945

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…We also adopt this relational thinking in studying up to ourselves. Since the study is performed by a black female and a white male, we reflect on what this means when we interpret the specific algorithmic inferences in emic terms, and how we come to the final intersubjectively generated knowledge (Peters & Wendland, 2016). Furthermore, we acknowledge our individual position in the social hierarchy, and the automatic limitations this poses in holistically uncovering all of its features.…”

Section: Introductionmentioning

confidence: 99%

Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm

Papakyriakopoulos

Mboya

2022

Social Science Computer Review

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We perform a socio-computational interrogation of the google search by image algorithm, a main component of the google search engine. We audit the algorithm by presenting it with more than 40 thousands faces of all ages and more than four races and collecting and analyzing the assigned labels with the appropriate statistical tools. We find that the algorithm reproduces white male patriarchal structures, often simplifying, stereotyping and discriminating females and non-white individuals, while providing more positive descriptions of white men. By drawing from Bourdieu’s theory of cultural reproduction, we link these results to the attitudes of the algorithm’s designers, owners, and the dataset the algorithm was trained on. We further underpin the problematic nature of the algorithm by using the ethnographic practice of studying-up: We show how the algorithm places individuals at the top of the tech industry within the socio-cultural reality that they shaped, many times creating biased representations of them. We claim that the use of social-theoretic frameworks such as the above are able to contribute to improved algorithmic accountability, algorithmic impact assessment and provide additional and more critical depth in algorithmic bias and auditing studies. Based on the analysis, we discuss the scientific and design implications and provide suggestions for alternative ways to design just socio-algorithmic systems.

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Section: Introductionmentioning

confidence: 99%