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AI and Responsible Data Science

How to Define and Execute Your Data and AI Strategy

by Ulla Kruhse-Lehtonen and Dirk Hofmann

The People Machine: The Earliest Machine Learning?

by Francine Berman and Jill Lepore

Data Science in Times of Pan(dem)ic

by Sabina Leonelli

Connections

Commentaries (7):

Timothy Francis Clement-Jones

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Eric D. Kolaczyk & 3 others

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Nicolette Louissaint

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Elizabeth L. Ogburn

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Theodore M. Porter

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Barbara Prainsack

Tackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction

by David Leslie

Data Science and Cities: A Critical Approach

by Fábio Duarte and Priyanka deSouza

Deep Learning With Gaussian Differential Privacy

by Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie Su

With Malice Toward None: Assessing Uncertainty via Equalized Coverage

by Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Candès

The Age of Secrecy and Unfairness in Recidivism Prediction

by Cynthia Rudin, Caroline Wang, and Beau Coker

Connections

Commentaries (6):

Shawn D. Bushway

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Alexandra Chouldechova

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Sarah L. Desmarais

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Brandon L. Garrett

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Eugenie Jackson & 1 other

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Rejoinders (1):

Cynthia Rudin & 2 others

Should We Trust Algorithms?

by David Spiegelhalter

Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From an Explainable AI Competition

by Cynthia Rudin and Joanna Radin

Artificial Intelligence

by Stephanie Dick

A Unified Framework of Five Principles for AI in Society

by Luciano Floridi and Josh Cowls

Artificial Intelligence—The Revolution Hasn’t Happened Yet

by Michael I. Jordan

Connections

Commentaries (11):

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Emmanuel Candès & 2 others

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Barbara J. Grosz

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Rebecca Willett

Rejoinders (1):

Michael I. Jordan

Transforming Hiring and Retention: Mr. Edison Meets AI/ML

by Maryse Williams