Category: Artificial Intelligence

Human-in-the-loop is not the magic bullet to fix AI harms

In many discussions and policy proposals related to addressing and fixing the harms of AI and algorithmic decision-making, much attention and hope has been placed on human oversight as a solution. This article by Ben Green and Amba Kak urges us to question the limits of human oversight, rather than seeing it as a magic bullet. For example, calling for ‘meaningful’ oversight sounds better in theory than practice. Humans can also be prone to automation bias, struggle with evaluating and making decisions based on the results of the algorithm, or exhibit racial biases in response to algorithms. Consequentially, these effects can have racist outcomes. This has already been proven in areas such as policing and housing.

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AI and its hidden costs

In a recent interview with The Guardian, Kate Crawford discusses her new book, Atlas AI, that delves into the broader landscape of how AI systems work by canvassing the structures of production and material realities. One example is ImageNet, a massive training dataset created by researchers from Stanford, that is used to test whether object recognition algorithms are efficient. It was made by scraping photos and images across the web and hiring crowd workers to label them according to an outdated lexical database created in the 1980s.

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Racist Technology in Action: Predicting future criminals with a bias against Black people

In 2016, ProPublica investigated the fairness of COMPAS, a system used by the courts in the United States to assess the likelihood of a defendant committing another crime. COMPAS uses a risk assessment form to assess this risk of a defendant offending again. Judges are expected to take this risk prediction into account when they decide on sentencing.

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Sentenced by Algorithm

Computer programs used to predict recidivism and determine prison terms have a high error rate, a secret design, and a demonstrable racial bias.

By Jed S. Rakoff for The New York Review of Books on June 10, 2021

Why EU needs to be wary that AI will increase racial profiling

This week the EU announces new regulations on artificial intelligence. It needs to set clear limits on the most harmful uses of AI, including predictive policing, biometric mass surveillance, and applications that exacerbate historic patterns of racist policing.

By Fieke Jansen and Sarah Chander for EUobserver on April 19, 2021

Rotterdam’s use of algorithms could lead to ethnic profiling

The Rekenkamer Rotterdam (a Court of Audit) looked at how the city of Rotterdam is using predictive algorithms and whether that use could lead to ethical problems. In their report, they describe how the city lacks a proper overview of the algorithms that it is using, how there is no coordination and thus no one takes responsibility when things go wrong, and how sensitive data (like nationality) were not used by one particular fraud detection algorithm, but that so-called proxy variables for ethnicity – like low literacy, which might correlate with ethnicity – were still part of the calculations. According to the Rekenkamer this could lead to unfair treatment, or as we would call it: ethnic profiling.

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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

The Dutch government’s love affair with ethnic profiling

In his article for One World, Florentijn van Rootselaar shows how the Dutch government uses automated systems to profile certain groups based on their ethnicity. He uses several examples to expose how, even though Western countries are often quick to denounce China’s use of technology to surveil, profile and oppress the Uighurs, the same states themselves use or contribute to the development of similar technologies.

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Racist technology in action: Gun, or electronic device?

The answer to that question depends on your skin colour, apparently. An AlgorithmWatch reporter, Nicholas Kayser-Bril, conducted an experiment that went viral on Twitter, showing that Google Vision Cloud (a service which is based on a subset of AI known as “computer vision” that focuses on automated image labelling), labelled an image of a dark-skinned individual holding a thermometer with the word “gun”, whilst a lighter skinned individual was labelled holding an “electronic device”.

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Google fires AI researcher Timnit Gebru

Google has fired AI researcher and ethicist Timnit Gebru after she wrote an email criticising Google’s policies around diversity while she struggled with her leadership to get a critical paper on AI published. This angered thousands of her former colleagues and academics. They pointed at the unequal treatment that Gebru received as a black woman and they were worried about the integrity of Google’s research.

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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

Discriminating Systems: Gender, Race, and Power in AI

The diversity crisis in AI is well-documented and wide-reaching. It can be seen in unequal workplaces throughout industry and in academia, in the disparities in hiring and promotion, in the AI technologies that reflect and amplify biased stereotypes, and in the resurfacing of biological determinism in automated systems.

By Kate Crawford, Meredith Whittaker and Sarah Myers West for AI Now Institute on April 1, 2019

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

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