Beyond Algorithmic Bias
A Socio-Computational Interrogation of the Google Search by Image Algorithm
Bibliographic Data
| ID | 12170484 |
|---|---|
| Authors | Orestis Papakyriakopoulos (0000-0003-4680-0022, Princeton University, Princeton, NJ, USA, corresponding author), Arwa Michelle Mboya (IIT@MIT) |
| Year | 2022 |
| Volume | 41 |
| Issue | 4 |
| Pages | 1100-1125 |
| Publication date | 2022-02-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Science Computer Review (JOURNAL) |
| Journal identifiers | ISSN: 0894-4393 • E-ISSN: 1552-8286 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/08944393211073169 |
| OpenAlex | W3165288160 |
| Language | EN |
| Citations received | 6 |
| References cited | 57 |
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
Algorithm · Audit · Interrogation · White (mutation · Computer Science · Ethics and Social Impacts of AI · Misinformation and Its Impacts · Mobile Crowdsensing and Crowdsourcing
Data Feminism
Algorithms of Oppression
Model Cards for Model Reporting
Fairness and Abstraction in Sociotechnical Systems
Biased Voices of Sports
Statsmodels
The Relevance of Algorithms
The Black Box Society
Do Artifacts Have Politics
Racial microaggressions and the Asian American experience
"Racial Ideology, Model Minorities, and the "Not-So-Silent Partner
Black hair culture, politics and change
Black Women and the Politics of Skin Color and Hair
Differences in Experiences of Racial and Ethnic Microaggression among Asian, Latino/Hispanic, Black, and White Young Adults
Thinking relationally about studying ‘up’
The Acquisition of Gender Stereotypes about Intellectual Ability
Facial Profiling
Racial stereotypes
Up the Africanist
Reproduction in education, society, and culture
Towards Multi-Dimensional Ethnography
Situating methods in the magic of Big Data and AI
The ethics of algorithms
Algorithms as culture
How the machine 'thinks
Stereotyping
Big Data, Thick Mediation, and Representational Opacity
Situated Knowledges
In Other Words
Ethics and Politics of Studying Up in Technoscience
What Should an Anthropology of Algorithms Do
The promises of computational ethnography
Diffractive Possibilities
The ethnographer and the algorithm
| Unique citing works | 6 |
|---|---|
| Citations per year | 2 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |