The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity

By Ayanna M. Howard, Jason Borenstein. 2017

284 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Safety Research, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Does Gender Matter? Towards Fairness in Dialogue Systems (2019, background): “In the field of computer vision, some face recognition algorithms fail to detect faces of black users (Rose, 2010) or inappropriately label black people as “gorillas” (Howard and Borenstein, 2018).”
A Survey on Bias and Fairness in Machine Learning (2019, background): “[58] also discusses specific examples of how bias has infused itself into current AI and robotic systems, such as bias in face recognition applications, voice recognition, and search engines.”
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020, background): “A variety of AI applications have demonstrated common prejudices towards particular groups of people (Rodger and Pendharkar, 2004; Howard and Borenstein, 2018; Rose, 2010; Yao and Huang, 2017; Tolan et al., 2019).”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, background): “Bias has been conceptualized in various ways by researchers—for example, statistical bias that results in skewed results [35, 72] and representation bias stemming from historical prejudice [69, 108].”
Ethics and governance of trustworthy medical artificial intelligence (2023, background): “Human-induced bias is intentionally or unintentionally written by the developers because individuals are always influenced by their own moral perceptions and relevant interests, which affects data training [25, 26].”
Five sources of bias in natural language processing (2021, background): “There have been several examples of computer vision bias, from an image captioning system labelling pictures of black people as ‘gorillas’ to cameras designed to detect whether a subject blinked, which malfunctioned if Asian people were in the picture (Howard & Borenstein, 2018).”
Trustworthy AI: From Principles to Practices (2021, background): “, underprivileged groups might experience systematic disadvantage in hiring decisions [49], or be disproportionally impacted in criminal risk profiling [104, 146, 161]).”