‘Race-blind’ content moderation disadvantages Black users

Over the past months a slew of leaks from the Facebook whistleblower, Frances Haugen, has exposed how the company was aware of the disparate and harmful impact of its content moderation practices. Most damning is that in the majority of instances, Facebook failed to address these harms. In this Washington Post piece, one of the latest of such revelations is discussed in detail: Even though Facebook knew it would come at the expense of Black users, its algorithm to detect and remove hate speech was programmed to be ‘race-blind’.

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Dutch Scientific Council knows: AI is neither neutral nor always rational

AI should be seen as a new system technology, according to The Netherlands Scientific Council for Government Policy, meaning that its impact is large, affects the whole of society, and is hard to predict. In their new Mission AI report, the Council lists five challenges for successfully embedding system technologies in society, leading to ten recommendations for governments.

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Amnesty’s grim warning against another ‘Toeslagenaffaire’

In its report of the 25 of October, Amnesty slams the Dutch government’s use of discriminatory algorithms in the child benefits schandal (toeslagenaffaire) and warns that the likelihood of such a scandal occurring again is very high. The report is aptly titled ‘Xenophobic machines – Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal’ and it conducts a human rights analysis of a specific sub-element of the scandal: the use of algorithms and risk models. The report is based on the report of the Dutch data protection authority and several other government reports.

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Digital Rights for All: harmed communities should be front and centre

Earlier this month, Digital Freedom Fund kicked off a series of online workshops of the ‘Digital Rights for All’ programme. In this post, Laurence Meyer details the reasons for this initiative with the fundamental aim of addressing why individuals and communities most affected by the harms of technologies are not centred in the advocacy, policy, and strategic litigation work on digital rights in Europe, and how to tackle challenges around funding, sustainable collaborations and language barriers.

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Racist Technology in Action: Facebook labels black men as ‘primates’

In the reckoning of the Black Lives Matter movement in summer 2020, a video that featured black men in altercation with the police and white civilians was posted by the Daily Mail, a British tabloid. In the New York Times, Ryan Mac reports how Facebook users who watched that video, saw an automated prompt that asked if they would like to “keep seeing videos about Primates,” despite there being no relatedness to primates or monkeys.

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Big Tech is propped up by a globally exploited workforce

Behind the promise of automation, advances of machine learning and AI, often paraded by tech companies like Amazon, Google, Facebook and Tesla, lies a deeply exploitative industry of cheap, human labour. In an excerpt published on Rest of the World from his forthcoming book, “Work Without the Worker: Labour in the Age of Platform Capitalism,” Phil Jones illustrates how the hidden labour of automation is outsourced to marginalised, racialised and disenfranchised populations within the Global North, as well as in the Global South.

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Photo filters are keeping colorism alive

Many people use filters on social media to ‘beautify’ their pictures. In this article, Tate Ryan-Mosley discusses how these beauty filters can perpetuate colorism. Colorism has a long and complicated history, but can be summarised as a preference for whiter skin as opposed to darker skin. Ryan-Mosley explains that “though related to racism, it’s distinct in that it can affect people regardless of their race, and can have different effects on people of the same background.” The harmful effects of colorism, ranging from discrimination to mental health issues or the use of toxic skin-lightening products, are found across races and cultures.

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Racist Technology in Action: White preference in mortage-approval algorithms

A very clear example of racist technology was exposed by Emmanuel Martinez and Lauren Kirchner in an article for the Markup. Algorithms used by a variety of American banks and lenders to automatically assess or advice on mortgages display clear racial disparity. In national data from the United States in 2019 they found that “loan applicants of color were 40%–80% more likely to be denied than their White counterparts. In certain metro areas, the disparity was greater than 250%.”

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Government: Stop using discriminatory algorithms

In her Volkskrant opinion piece Nani Jansen Reventlow makes a forceful argument for the government to stop using algorithms that lead to discrimination and exclusion. Reventlow, director of the Digital Freedom Fund, employs a myriad of examples to show how disregarding the social nature of technological systems can lead to reproducing existing social injustices such as racism or discrimination. The automatic fraud detection system SyRI that was ruled in violation of fundamental rights (and its dangerous successor Super SyRI) is discussed, as well as the racist proctoring software we wrote about earlier.

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Uber-racist: Racial discrimination in dynamic pricing algorithms

Racial discrimination in dynamic pricing algorithms is neither surprising nor new. VentureBeat writes about another recent study that supports these findings, in the context of dynamic pricing algorithms used by ride-hailing companies such as Uber, Lyft and other apps. Neighbourhoods that were poorer and with larger non-white populations were significantly associated with higher fare prices. A similar issue was discovered in Airbnb’s ‘Smart Pricing’ feature which aims to help hosts secure more bookings. It turned out to be detrimental to black hosts leading to greater social inequality (even if unintentional).

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Covid-19 data: making racialised inequality in the Netherlands invisible

The CBS, the Dutch national statistics authority, issued a report in March showing that someone’s social economic status is a clear risk factor for dying of Covid-19. In an insightful piece, researchers Linnet Taylor and Tineke Broer criticise this report and show that the way in which the CBS collects and aggragates data on Covid-19 cases and deaths obfuscates the full extent of racialised or ethnic inequality in the impact of the pandemic.

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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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Racist Technology in Action: Proctoring software disadvantaging students of colour in the Netherlands

In an opinion piece in Parool, The Racism and Technology Center wrote about how Dutch universities use proctoring software that uses facial recognition technology that systematically disadvantages students of colour (see the English translation of the opinion piece). Earlier the center has written on the racial bias of these systems, leading to black students being excluded from exams or being labeled as frauds because the software did not properly recognise their faces as a face. Despite the clear proof that Procorio disadvantages students of colour, the University of Amsterdam has still used Proctorio extensively in this June’s exam weeks.

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Long overdue: Google has improved its camera app to work better for Black people

The following short video by Vox shows how white skin has always been the norm in photography. Black people didn’t start to look good on film until in the 1970s furniture makers complained to Kodak that their film didn’t render the difference between dark and light grained wood, and chocolate companies were upset that you couldn’t see the difference between dark and light chocolate.

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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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Why Europe needs a new vocabulary to talk about race

In this article for Algorithm Watch, Nicolas Kayser-Bril highlights an important issue facing Europe in the fight against racist technologies: we lack the words to talk about racism. He shows why Europeans need a new vocabulary and discourse to understand and discuss racist AI systems. For example, concepts such as ‘Racial Justice’ have no part in the EU’s anti-discrimination agenda and ‘ethnicity’ is not recognised as a proxy for race in a digital context. The lack of this vocabulary greatly harms our current ability to challenge and dismantle these systems and, crucially, the root of racism.

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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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Racist and classist predictive policing exists in Europe too

The enduring idea that technology will be able to solve many of the existing problems in society continues to permeate across governments. For the EUObserver, Fieke Jansen and Sarah Chander illustrate some of the problematic and harmful uses of ‘predictive’ algorithmic systems by states and public authorities across the UK and Europe.

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The right to repair our devices is also a social justice issue

Over the past couple of years, devices like our phones have become much harder to repair, and unauthorized repair often leads to a loss of warranty. This is partially driven by our manufactured need for devices that are slimmer and slicker, but is mostly an explicit strategy to make us throw away our old devices and have us buy new ones.

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At the mercy of the TikTok algorithm?

In this article for the Markup, Dara Kerr offers an interesting insight in the plight of TikTok’ers who try to earn a living on the platform. TikTok’s algorithm, or how it decides what content gets a lot of exposure, is notoriously vague. With ever changing policies and metrics, Kerr recounts how difficult it is to build up and retain a following on the platform. This vagueness does not only create difficulty for creators trying to monetize their content, but also leaves more room for TikTok to suppress or spread content at will.

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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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Google blocks advertisers from targeting Black Lives Matter

In this piece for Markup, Leon Yin and Aaron Sankin expose how Google bans advertisers from targeting terms such as “Black lives matter”, “antifascist” or “Muslim fashion”. At the same time, keywords such as “White lives matter” or “Christian fashion” are not banned. When they raised this striking discrepancy with Google, its response was to fix the discrepancies between religions and races by blocking all such terms, as well as by blocking even more social justice related keywords such as “I can’t breathe” or “LGBTQ”. Blocking these terms for ad placement can reduce the revenue for YouTuber’s fighting for these causes. Yin and Sankin place this policy in stark contrast to Google’s support for the Black Lives Matter movement.

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Online hate and harassment continue to proliferate

A recent report by ADL, an anti-hate organisation in the US, has shown that social media platforms have consistently failed to prevent online hate and harassment. Despite the self-regulatory efforts made by social media companies, results from ADL’s annual survey shows that the level of online hate and harassment has barely shifted in the past three years. These online experiences disproportionately harm marginalised groups, with LGBTQI+, Asian-American, Jewish and African-American respondents reporting higher rates of various forms of harassment. Many of these problems are intrinsic to the ways in which the business models of social media platforms are optimised for maximum engagement, further exacerbating existing issues in society.

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

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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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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