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Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations

Joseph Alderman, Joanne Palmer, Elinor Laws, Melissa D. McCradden, Johan Ordish, Marzyeh Ghassemi, Stephen Pfohl, Negar Rostamzadeh, Heather Cole-Lewis, Ben Glocker, Melanie Calvert, Tom Pollard, Jaspret Gill, Jacqui Gath, Ade Adebajo, Jude Beng, Cheuk Wing Leung, Stephanie Kuku, Lesley-Anne Farmer, Rubeta Matin, Bilal A. Mateen, Francis McKay, Katherine Heller, Alan Karthikesalingam, Darren Treanor, Maxine Mackintosh, Lauren Oakden‐Rayner, Russell Pearson, Arjun K. Manrai, Puja Myles, Judit Kumuthini, Zoher Kapacee, Neil J. Sebire, Lama Nazer, Jarrel Seah, Ashley Akbari, Lewis E. Berman, Judy Wawira Gichoya, Lorenzo Righetto, Diana Samuel, William Wasswa, Maria Charalambides, Anmol Arora, Sameer Pujari, Charlotte Summers, Elizabeth Sapey, Sharon Wilkinson, Vishal Thakker, Alastair K. Denniston, Xiaoxuan Liu

2024NEJM AI13 citationsDOI

Topics & Concepts

Transparency (behavior)Data scienceComputer scienceInternet privacyComputer securityArtificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareEthics in Clinical Research
Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations | Litcius