Author: lnbressa
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What Do We Do About the Biases in AI?
Algorithms, Amazon, Artificial Intelligence, Bias Mitigation, Explainability, Fairness, Human Bias, Racial Bias, TransparencyAuthor: James Manyika, Jake Silberg, Brittany Presten Publisher: Harvard Business Review Publication Year: 2019 Summary: The following article discusses how artificial intelligence (AI) can help identify and reduce the impact of human biases. But it…
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Artificial Intelligence Ethics and Bias
Author: James M. Connolly; Publisher: Information Week; Publication Year: 2022. The following article is a great, comprehensive description of artificial intelligence (AI) ethics and bias with a lot of supporting articles linked throughout. It begins…
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Building Ethical Guardrails into AI-Driven Robotic Assistants
Author: James Kobielus; Publisher: Transforming Data with Intelligence (TDWI); Publication Year: 2021. The following article focuses on why there are ethical boundaries needed when building robotic assistants in this article. Developers of the artificial intelligence…
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A.I. for Good
Author: James Hodson, Jon Krohn; Publisher: Super Data Science; Publication Year: 2021. The following podcast episode is an interview with James Hodson who founded AI for Good. He started the organization to focus on using…
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Ethical AI Development: Evidence from AI Startups
Author: James Bessen, Stephen Michael Impink, Robert Seamans; Publisher: The Center on Regulation and Markets at Brookings; Publication Year: 2022. The following paper discusses an academic approach to researching and quantifying artificial intelligence ethic progress…
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Data Ethics is More Than Just What We Do with Data, It’s Also About Who’s Doing It
Author: James Arvanitakis, Andrew Francis, Oliver Obst; Publisher: The Conversation; Publication Year: 2018. The following article discusses how, as analysts, the main 2 ethical questions that are being asked are 1). What data should be…
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fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models
Author: Jakub Wisniewski, Przemyslaw Biecek; Publisher: The R Journal; Publication Year: 2022. The following article discusses how as more sophisticated machine learning methods become more ubiquitous, a culture of classifying between “explainable” and “unexplainable” models…
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Tackling Bias in Artificial Intelligence
Author: Jake Silberg, James Manyika; Publisher: McKinsey & Company; Publication Year: 2019. The following article discusses 2 opportunities that artificial intelligence (AI) presents to today’s society: the opportunity to reduce the effect of human bias,…