Journal ArticleDOI
Algorithms of Oppression: How Search Engines Reinforce Racism
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Noble as mentioned in this paper is one of the pre-eminent works that explicitly addressees the relationship between race and gender in the media, and it is a seminal work in the field of communication.Abstract:
Authored by Dr. Safiya U. Noble, an assistant professor at the University of Southern California Annenberg School of Communication, this text is one of the preeminent works that explicitly addresse...read more
Citations
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MonographDOI
Algorithms of Oppression: How Search Engines Reinforce Racism
TL;DR: Noble's Algorithms of Oppression: How Search Engines Reinforce Racism is devastating as mentioned in this paper, which reduces to rubble the notion that technology is neutral and ideology-free.
Journal ArticleDOI
Algorithms at Work: The New Contested Terrain of Control
TL;DR: This work uses Edwards’ (1979) perspective of “conteste... to explore how algorithms may reshape organizational control in the rapidly changing environment.
Posted Content
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh,Daniel G. Goldstein,Jake M. Hofman,Jennifer Wortman Vaughan,Hanna Wallach +4 more
TL;DR: A sequence of pre-registered experiments showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box).
Proceedings ArticleDOI
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning
TL;DR: This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning and uncovers the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions.
Journal ArticleDOI
Organizational Decision-Making Structures in the Age of Artificial Intelligence:
TL;DR: A novel framework is built outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making and three structural categories in which decisions of organizational members can be combined with AI-based decisions are presented.
References
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MonographDOI
Algorithms of Oppression: How Search Engines Reinforce Racism
TL;DR: Noble's Algorithms of Oppression: How Search Engines Reinforce Racism is devastating as mentioned in this paper, which reduces to rubble the notion that technology is neutral and ideology-free.
Journal ArticleDOI
Algorithms at Work: The New Contested Terrain of Control
TL;DR: This work uses Edwards’ (1979) perspective of “conteste... to explore how algorithms may reshape organizational control in the rapidly changing environment.
Posted Content
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh,Daniel G. Goldstein,Jake M. Hofman,Jennifer Wortman Vaughan,Hanna Wallach +4 more
TL;DR: A sequence of pre-registered experiments showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box).
Proceedings ArticleDOI
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
Zeyu Wang,Klint Qinami,Ioannis Christos Karakozis,Kyle Genova,Prem Qu Nair,Kenji Hata,Olga Russakovsky +6 more
TL;DR: In this article, Zhao et al. design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation, and provide a thorough analysis of a wide range of techniques, highlighting the shortcomings of popular adversarial training approaches for bias mitigation.
Proceedings ArticleDOI
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning
TL;DR: This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning and uncovers the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions.