Uitvoeringsdiensten gebruiken talloze ‘zwarte lijsten’ met potentiële fraudeurs. Dat kan leiden tot (indirecte) etnische profilering en nieuwe drama’s, na de toeslagenaffaire.
By Nani Jansen Reventlow for Volkskrant on July 15, 2021
Marginalized groups are often not represented in technology development. What we need is inclusive participation to centre on the concerns of these groups.
By Nani Jansen Reventlow for The World Economic Forum on July 8, 2021
Many of us assume that tech is neutral, and we have turned to tech as a way to root out racism, sexism, or other “isms” plaguing human decision-making. But as data-driven systems become a bigger and bigger part of our lives, we also notice more and more when they fail, and, more importantly, that they don’t fail on everyone equally. Glad You Asked host Joss Fong wants to know: Why do we think tech is neutral? How do algorithms become biased? And how can we fix these algorithms before they cause harm?
From YouTube on March 31, 2021
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.
Continue reading “Human-in-the-loop is not the magic bullet to fix AI harms”
De UvA kan het niet meer maken om proctoring in te zetten bij het afnemen van tentamens, nu duidelijk is dat de surveillance-software juist op mensen van kleur een negatieve impact heeft.
Continue reading “Oproep aan de UvA: stop het gebruik van racistische proctoringsoftware”
The feature associates “Africa” with the hut emoji and “China” with the dog emoji.
By Andrew Deck for Rest of World on June 15, 2021
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.
Continue reading “Long overdue: Google has improved its camera app to work better for Black people”
Women and people of colour are underrepresented and depicted with stereotypes.
From The Economist on June 5, 2021
Chicago’s predictive policing program told a man he would be involved with a shooting, but it couldn’t determine which side of the gun he would be on. Instead, it made him the victim of a violent crime.
By Matt Stroud for The Verge on May 24, 2021
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
Automated systems from Apple and Google label characters with dark skins “Animals”.
By Nicolas Kayser-Bril for AlgorithmWatch on May 14, 2021
Experience the world of face detection algorithms in this freaky test.
By Tijmen Schep for How Normal Am I
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.
Continue reading “Racist and classist predictive policing exists in Europe too”
From Siri, to Alexa, to Google Now, voice-based virtual assistants have increasingly become ubiquitous in our daily lives. So, it is unsurprising that yet another AI technology – speech recognition systems – has been reported to be biased against black people.
Continue reading “Racist Technology in Action: Speech recognition systems by major tech companies are biased”
This report investigates how algorithmic discrimination challenges the set of legal guarantees put in place in Europe to combat discrimination and ensure equal treatment. More specifically, it examines whether and how the current gender equality and non-discrimination legislative framework in place in the EU can adequately capture and redress algorithmic discrimination. It explores the gaps and weaknesses that emerge at both the EU and national levels from the interaction between, on the one hand, the specific types of discrimination that arise when algorithms are used in decision-making systems and, on the other, the particular material and personal scope of the existing legislative framework. This report also maps out the existing legal solutions, accompanying policy measures and good practice to address and redress algorithmic discrimination both at EU and national levels. Moreover, this report proposes its own integrated set of legal, knowledge-based and technological solutions to the problem of algorithmic discrimination.
By Janneke Gerards and Raphaële Xenidis for Publication Office of the European Union on March 10, 2021
Advances in artificial intelligence (AI) technology promise to revolutionize our approach to medicine, finance, business operations, media, and more.
From Federal Trade Commission on April 19, 2021
A secretive algorithm that’s constantly being tweaked can turn influencers’ accounts, and their prospects, upside down.
By Dara Kerr for The Markup on April 22, 2021
The company is considering how its use of machine learning may reinforce existing biases.
By Anna Kramer for Protocol on April 14, 2021
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.
Continue reading “Rotterdam’s use of algorithms could lead to ethnic profiling”
De algoritmes die de gemeente Rotterdam gebruikt om bijvoorbeeld uitkeringsfraude op te sporen kunnen leiden tot ‘vooringenomen uitkomsten’. Dit concludeert de Rekenkamer Rotterdam in een rapport dat donderdag verschijnt. Voorzitter Paul Hofstra legt uit wat er is misgegaan.
By Paul Hofstra and Rik Kuiper for Volkskrant on April 15, 2021
De gemeente Rotterdam maakt ter ondersteuning van haar besluitvorming gebruik van algoritmes. Hoewel er binnen de gemeente aandacht bestaat voor het ethisch gebruik van algoritmes, is het besef van de noodzaak hiervan nog niet heel wijdverbreid. Dit kan leiden tot weinig transparantie van algoritmes en vooringenomen uitkomsten, zoals bij een algoritme gericht op de bestrijding van uitkeringsfraude. Dit en meer concludeert de Rekenkamer Rotterdam in het rapport ‘Gekleurde technologie’.
From Rekenkamer Rotterdam on April 14, 2021
Immigrant rights campaigners bring legal challenge to Home Office on algorithm that streams visa applicants.
By Henry McDonald for The Guardian on October 29, 2019
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.
Continue reading “Online proctoring excludes and discriminates”
Upcoming rules on AI might make Europe’s race issues a tech problem too.
By Melissa Heikkilä for POLITICO on March 16, 2021
Dutch tax authorities used algorithms to automate an austere and punitive war on low-level fraud—the results were catastrophic.
By Gabriel Geiger for VICE on March 1, 2021
Dutch benefits scandal highlights need for EU scrutiny.
By Nani Jansen Reventlow for POLITICO on March 2, 2021
A growing industry wants to scrutinize the algorithms that govern our lives—but it needs teeth.
By Alfred Ng for The Markup on February 23, 2021
According to data from The Markup’s Citizen Browser project, there are major disparities in who is shown public health information about the pandemic.
By Corin Faife and Dara Kerr for The Markup on March 4, 2021
Cheating is not a technological problem, but a social and pedagogical problem. Technology is often blamed for creating the conditions in which cheating proliferates and is then offered as the solution to the problem it created; both claims are false.
By Shea Swauger for Hybrid Pedagogy on April 2, 2020
GitHub is where people build software. More than 56 million people use GitHub to discover, fork, and contribute to over 100 million projects.
By Klint Finley for GitHub on February 18, 2021
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.
Continue reading “The Dutch government’s love affair with ethnic profiling”
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”.
Continue reading “Racist technology in action: Gun, or electronic device?”
The human-centered approach that can combat algorithmic bias.
By Jessie Daniels for Quartz on April 3, 2019
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
Adolescents spend ever greater portions of their days online and are especially vulnerable to discrimination. That’s a worrying combination.
By Avriel Epps-Darling for The Atlantic on October 24, 2020
As many of us had our attention focused on the use of biometric surveillance technologies in managing the COVID-19 pandemic, in a new UN report prof. E. Tendayi Achiume forcefully puts the spotlight on the racial and discriminatory dimension of biometric surveillance technology in border enforcement.
Continue reading “Racism and “Smart Borders””
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.
Continue reading “Google fires AI researcher Timnit Gebru”
The Markup has published an overview of the ways in which algorithms have been given decisional powers in 2020 and have taken a wrong turn.
Continue reading “A year of algorithms behaving badly”
A Google service that automatically labels images produced starkly different results depending on skin tone on a given image. The company fixed the issue, but the problem is likely much broader.
By Nicolas Kayser-Bril for AlgorithmWatch on April 7, 2020
When we say that “an algorithm is biased” we usually mean, “biased people made an algorithm.” This explains why so much machine learning prediction turns into phrenology.
By Cory Doctorow for Pluralistic on January 15, 2021
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