Category: Legal & Policy

Gender Shades

Accuracy, Artificial Intelligence, Face++, Gender Classification, IBM, Microsoft, Transparency

Author: Joy Buolamwini, Timnit Gebru; Publisher: MIT Media Lab; Publication Year: 2018. The following video discusses the Gender Shades project which evaluates the bias of 3 commercial artificial intelligence (AI) gender classification products (Microsoft, Face++,…
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Race, Technology, and Algorithmic Bias

Algorithmic Bias, Facial Recognition Systems, Racial Bias, Training Data

Author: Joy Buolamwini, Latanya Sweeney, and Darren Walker; Publisher: Radcliffe Institute for Advanced Study, Harvard University; Publication Year: 2019. The following video documents a conversation between Joy Buolamwini, Latanya Sweeney, and Darren Walker. Leading the…
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IBM Leads, More Should Follow: Racial Justice Requires Algorithmic Justice

AI Abuse, AI Bias, Equity, Facial Recognition Systems, IBM

Author: Joy Buolamwini, Aaina Agarwal, and Sasha Costanza-Chock; Publisher: The Medium; Publication Year: 2020. The following article applauds IBM for the decision to restrict sales of facial recognition technology in response to bias and abuse.…
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Keynote Speaker: Joy Buolamwini Presented by SpeakInc

Algorithmic Bias, Coded Gaze, Discriminatory Practices, Exclusionary Experiences, Facial Recognition Systems, Gender Gap, IBM, Racial Gaps

Author: Joy Buolamwini Publisher: SpeakInc Publication Year: 2018 Summary: In the following video, Joy Buolamwini expands on her mission of the ‘Coded Gaze’, her term for algorithmic bias which can lead to discriminatory practices or…
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The Algorithmic Justice League

Accountable AI, AI Bias, AI Harm, Artificial Intelligence, Awareness, Equitable AI

Author: Joy Buolamwini; Publisher: The Algorithmic Justice League; Publication Year: N/A. The following group is an organization that addresses the social implications and harms of artificial intelligence (AI) by raising public awareness about the impacts…
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Privacy Violations Undermine the Trustworthiness of the Tim Hortons Brand

Artificial Intelligence, Consent, Data Collection, Data Usage, Office of the Privacy Commisioner, Tim Hortons, Transparency

Author: Jordan Richard Schoenherr Publisher: The Conversation Publication Year: 2022 Summary: The following article discusses how given the growing capabilities of artificial intelligence (AI), coupled with a lack of transparency in how data is collected…
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Can You Hear the Will of the People in the Vote? Assessing Fairness in Redistricting via Monte Carlo Sampling

Algorithms, Congressional Maps, Election, Equity, Fairness, Gerrymandering, Voting Preferences

Author: Jonathan Mattingly; Publisher: Society for Industrial and Applied Mathematics; Publication Year: 2021. The following video is the author presenting his research on how local, state, and national elections depend on the underlying maps that…
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Inherent Trade-Offs in the Fair Determination of Risk Scores

Credit Score, Fairness, Risk Scoring

Author: Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan; Publisher: N/A; Publication Year: 2016. The following publication discusses issues around risk scoring with credit scores and attempts to define “fairness” in this context. The main idea of…
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Would You Trust Your Business with Your Data?

ADMA, Customer Data, Data Collection

Author: Jodie Sangster; Publisher: Smarter Business; Publication Year: 2017. The following interview sheds light on how the laws around data ethics vary by the region. The CEO of ADMA goes on to share how using…
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