Category: Facial Recognition and Biometrics

Racist Technology in Action: Amazon’s racist facial ‘Rekognition’

Different faces with different recognition scores, white men are recognised better than black women

An already infamous example of racist technology is Amazon’s facial recognition system ‘Rekognition’ that had an enormous racial and gender bias. Researcher and founder of the Algorithmic Justice League Joy Buolawini (the ‘poet of code‘), together with Deborah Raji, meticulously reconstructed how accurate Rekognition was in identifying different types of faces. Buolawini and Raji’s study has been extremely consequencial in laying bare the racism and sexism in these facial recognition systems and was featured in the popular Coded Bias documentary.

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Seeing infrastructure: race, facial recognition and the politics of data

Facial recognition technology (FRT) has been widely studied and criticized for its racialising impacts and its role in the overpolicing of minoritised communities. However, a key aspect of facial recognition technologies is the dataset of faces used for training and testing. In this article, we situate FRT as an infrastructural assemblage and focus on the history of four facial recognition datasets: the original dataset created by W.W. Bledsoe and his team at the Panoramic Research Institute in 1963; the FERET dataset collected by the Army Research Laboratory in 1995; MEDS-I (2009) and MEDS-II (2011), the datasets containing dead arrestees, curated by the MITRE Corporation; and the Diversity in Faces dataset, created in 2019 by IBM. Through these four exemplary datasets, we suggest that the politics of race in facial recognition are about far more than simply representation, raising questions about the potential side-effects and limitations of efforts to simply ‘de-bias’ data.

By Nikki Stevens and Os Keyes for Taylor & Francis Online on March 26, 2021

This is the EU’s chance to stop racism in artificial intelligence

As the European Commission prepares its legislative proposal on artificial intelligence, human rights groups are watching closely for clear rules to limit discriminatory AI. In practice, this means a ban on biometric mass surveillance practices and red lines (legal limits) to stop harmful uses of AI-powered technologies.

By Sarah Chander for European Digital Rights (EDRi) on March 16, 2021

Online proctoring excludes and discriminates

The use of software to automatically detect cheating on online exams – online proctoring – has been the go-to solution for many schools and universities in response to the COVID-19 pandemic. In this article, Shea Swauger addresses some of the potential discriminatory, privacy and security harms that can impact groups of students across class, gender, race, and disability lines. Swauger provides a critique on how technologies encode “normal” bodies – cisgender, white, able-bodied, neurotypical, male – as the standard and how students who do not (or cannot) conform, are punished by it.

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Decode the Default

Technology has never been colorblind. It’s time to abolish notions of “universal” users of software.

From The Internet Health Report 2020 on January 1, 2021

Hoe Nederland A.I. inzet voor etnisch profileren

China dat kunstmatige intelligentie inzet om Oeigoeren te onderdrukken: klinkt als een ver-van-je-bed-show? Ook Nederland (ver)volgt specifieke bevolkingsgroepen met algoritmes. Zoals in Roermond, waar camera’s alarm slaan bij auto’s met een Oost-Europees nummerbord.

By Florentijn van Rootselaar for OneWorld on January 14, 2021

Programmed Racism – Global Digital Cultures

This episode is part of the GDC Webinar series that took place on september 2020. How do digital technologies mediate racism? It is increasingly clear that digital technologies, including auto-complete function, facial recognition, and profiling tools are not neutral but racialized in specific ways. This webinar focuses on the different modes of programmed racism. We present historical and contemporary examples of racial bias in computational systems and learn about the potential of Civic AI. We discuss the need for a global perspective and postcolonial approaches to computation and discrimination. What research agenda is needed to address current problems and inequalities? Chair: Lonneke van der Velden, University of Amsterdam Speakers: Sennay Ghebreab,  Associate Professor of informatics, University of Amsterdam and Scientific Director of the Civic AI Lab, for civic-centered and community minded design, development and development of AI Linnet Taylor, Associate Professor at the Tilburg Institute for Law, Technology, and Society (TILT), PI of the ERC-funded Global Data Justice Project. Payal Arora, Professor and Chair in Technology, Values, and Global Media Cultures at the Erasmus School of Philosophy, Erasmus University Rotterdam and Author of the ‘Next Billion Users’ with Harvard Press.

From Spotify on November 24, 2020

Designed to Deceive: Do These People Look Real to You?

The people in this story may look familiar, like ones you’ve seen on Facebook or Twitter or Tinder. But they don’t exist. They were born from the mind of a computer, and the technology behind them is improving at a startling pace.

By Kashmir Hill for The New York Times on November 21, 2020

Why Can’t This Soap Dispenser Identify Dark Skin?

On Wednesday, a Facebook employee in Nigeria shared footage of a minor inconvenience that he says speaks to tech’s larger diversity problem. In the video, a white man and a dark-skinned black man both try to get soap from a soap dispenser. The soap dispenses for the white man, but not the darker skinned man. After a bit of laughter, a person can be overheard chucking, “too black!”

By Sidney Fussell for Gizmodo on August 17, 2017

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