Data_Day_2021_November 5, 2021

Coded Bias 

Watch party and panel discussion

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Coded Bias

   A Shalini Kantayya Film 


CODED BIAS explores the fallout of MIT Media Lab researcher Joy Buolamwini’s discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all. 


Modern society sits at the intersection of two crucial questions: What does it mean when artificial intelligence increasingly governs our liberties? And what are the consequences for the people AI is biased against? When MIT Media Lab researcher Joy Buolamwini discovers that many facial recognition technologies do not accurately detect darker-skinned faces or classify the faces of women, she delves into an investigation of widespread bias in algorithms. As it turns out, artificial intelligence is not neutral, and women are leading the charge to ensure our civil rights are protected. 

The film won a SIMA Award for Best Director, and has been nominated for a Critics’ Choice Award, and a NAACP Image Award for Outstanding Documentary, among others. 


In connection with Data Day and in keeping with the theme of Bias, Miscommunication, and Equity in Data, University of Cincinnati Libraries jointly with Office of Research Technologies is hosting a weeklong screening of Coded Bias October 29, 2021 – November 5, 2021.  

UC Screening Site 

The password to watch the film will be given to those who indicate an interest in viewing when registering for Data Day 2021. 

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Good vs. Bad AI

Join us for a panel on  Good vs. Bad AI, featuring Deeptankar DeMazumder, Nirmalya Thakur.  Moderated by Chris Collins  

Date: Friday, November 5, 2021  

Time: 1pm  

Good vs. Bad AI will contrast how AI can be used (consciously and unconsciously) to insert bias into data and ways that it is used to provide meaningful information that can provide novel insights into today’s problems. During the panel UC researchers will discuss how they are using AI to solve complex issues regarding health and the use of “smart” technologies to provide improved quality of life and early detection of health concerns.  

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