Artificial Intelligence's White Guy Problem

By Kate Crawford. 2016

233 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Social Bias in Elicited Natural Language Inferences (2017, background): “High-proﬁle cases of overgener-alization in the public sphere abound (Crawford, 2013; Crawford, 2016; Barocas and Selbst, 2016).”
Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, background): “Far less close to resolution are concerns about fairness raised by the media (Cohen, 2012; Crawford, 2016; Angwin et al., 2016; Dietrerich et al., 2016; Doleac and Stevenson, 2016), government agencies (National Science and Technology Council, 2016: 30-32), foundations (Pew Center of the States,…”
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment (2016, background): “The emergence and widespread usage of automated datadriven decision making systems in a wide variety of applications, ranging from content recommendations to pretrial risk assessment, has raised concerns about their potential unfairness towards people with certain traits [8, 22, 24, 27].”
Conditional variance penalties and domain shift robustness (2017, methodology): “For an example involving Google’s photo app, see Crawford (2016) and Emspak (2016).”
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration (2016, background): “In criminal justice, biases and blindspots can lead to the inappropriate sentencing or incarceration of people charged with crimes or unintentional racial biases (Crawford 2016).”
Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems (2017, background): “Recently, there has been an upsurge of attention given to generalized machine learning algorithms and the practices of inequality and discrimination that are potentially being built into them [29].”
Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017, background): “For instance, in June 2015 Google’s photo app tagged two non-white people as “gorillas”—most likely because the training examples for “people” were mainly photos of white persons, making “color” predictive for the class label (Crawford, 2016; Emspak, 2016).”
What is AI Literacy? Competencies and Design Considerations (2020, background): “Lack of workforce diversity can affect who systems are developed for [33]—a significant issue in AI, where biased algorithms can have pronounced adverse effects on marginalized subgroups [32].”
Goal-Oriented Design for Ethical Machine Learning and NLP (2017, background): “It is necessary to acknowledge and address Crawford (2016)'s critique: most of the people who build technology come from privileged backgrounds, which makes it difficult for our imagination and our empathy to extend out to everyone our systems will affect.”
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “A consistent theme from the literature is the benefit of engaging a variety of stakeholders and maintaining diversity along social lines where bias is a concern (racial diversity, gender diversity, age diversity, diversity of physical ability) [232, 233].”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, background): “Therefore, Crawford (2016, p. 1) infers that since the majority of artificial intelligence is created by Caucasian males, the algorithms would thus reflect “white guy” influences.”
How to Design AI for Social Good: Seven Essential Factors (2020, background): “Such biases may cause discriminatory decisions (e.g., warnings or arrests) that feed back into the increasingly biased data-sets (Crawford 2016), thereby completing a vicious cycle.”