Category: Artificial Intelligence

­Data Work and its Layers of (In)visibility

No technology has seemingly steam-rolled through every industry and over every community the way artificial intelligence (AI) has in the past decade. Many speak of the inevitable crisis that AI will bring. Others sing its praises as a new Messiah that will save us from the ails of society. What the public and mainstream media hardly ever discuss is that AI is a technology that takes its cues from humans. Any present or future harms caused by AI are a direct result of deliberate human decisions, with companies prioritizing record profits, in an attempt to concentrate power by convincing the world that technology is the only solution to societal problems.

By Adrienne Williams and Milagros Miceli for Just Tech on September 6, 2023

Vooral vrouwen van kleur klagen de vooroordelen van AI aan

Wat je in zelflerende AI-systemen stopt, krijg je terug. Technologie, veelal ontwikkeld door witte mannen, versterkt en verbergt daardoor de vooroordelen. Met name vrouwen (van kleur) luiden de alarmbel.

By Marieke Rotman, Nani Jansen Reventlow, Oumaima Hajri and Tanya O’Carroll for De Groene Amsterdammer on July 12, 2023

Racist Technology in Action: Stable Diffusion exacerbates and amplifies racial and gender disparities

Many different faces of “A color photograph of a housekeeper” generated by Stable Diffusion, showing mostly women of colour.

Bloomberg’s researchers used Stable Diffusion to gauge the magnitude of biases in generative AI. Through an analysis of more than 5,000 images created by Stable Diffusion, they have found that it takes racial and gender disparities to extremes. The results are worse than those found in the real world.

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Attempts to eliminate bias through diversifying datasets? A distraction from the root of the problem

Nine different images of Hito Steyerls as found via haveibeentrained.com

In this eloquent and haunting piece by Hito Steyerl, she weaves the ongoing narratives of the eugenicist history of statistics with its integration into machine learning. She elaborates why the attempts to eliminate bias in facial recognition technology through diversifying datasets obscures the root of the problem: machine learning and automation are fundamentally reliant on extracting and exploiting human labour.

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Racist Technology in Action: Image recognition is still not capable of differentiating gorillas from Black people

our rows of ten photos showing different animals (many apes) and people at a zoo

If this title feels like a deja-vu it is because you most likely have, in fact, seen this before (perhaps even in our newsletter). It was back in 2015 that the controversy first arose when Google released image recognition software that kept mislabelling Black people as gorillas (read here and here).

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On Race, AI, and Representation Or, Why Democracy Now Needs To Redo Its June 1 Segment

On June 1, Democracy Now featured a roundtable discussion hosted by Amy Goodman and Nermeen Shaikh, with three experts on Artificial Intelligence (AI), about their views on AI in the world. They included Yoshua Bengio, a computer scientist at the Université de Montréal, long considered a “godfather of AI,” Tawana Petty, an organiser and Director of Policy at the Algorithmic Justice League (AJL), and Max Tegmark, a physicist at the Massachusetts Institute of Technology. Recently, the Future of Life Institute, of which Tegmark is president, issued an open letter “on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.” Bengio is a signatory on the letter (as is Elon Musk). The AJL has been around since 2016, and has (along with other organisations) been calling for a public interrogation of racialised surveillance technology, the use of police robots, and other ways in which AI can be directly responsible for bodily harm and even death.

By Yasmin Nair for Yasmin Nair on June 3, 2023

GPT detectors are biased against non-native English writers

The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse.

By Eric Wu, James Zou, Mert Yuksekgonul, Weixin Liang and Yining Mao for arXiv.org on April 18, 2023

Consensus and subjectivity of skin tone annotation for ML fairness

Skin tone is an observable characteristic that is subjective, perceived differently by individuals (e.g., depending on their location or culture) and thus is complicated to annotate. That said, the ability to reliably and accurately annotate skin tone is highly important in computer vision. This became apparent in 2018, when the Gender Shades study highlighted that computer vision systems struggled to detect people with darker skin tones, and performed particularly poorly for women with darker skin tones. The study highlights the importance for computer researchers and practitioners to evaluate their technologies across the full range of skin tones and at intersections of identities. Beyond evaluating model performance on skin tone, skin tone annotations enable researchers to measure diversity and representation in image retrieval systems, dataset collection, and image generation. For all of these applications, a collection of meaningful and inclusive skin tone annotations is key.

By Candice Schumann and Gbolahan O. Olanubi for Google AI Blog on May 15, 2023

Mean Images

An artist considers a new form of machinic representation: the statistical rendering of large datasets, indexed to the probable rather than the real of photography; to the uncanny composite rather than the abstraction of the graph.

By Hito Steyerl for New Left Review on April 28, 2023

Governments’ use of automated decision-making systems reflects systemic issues of injustice and inequality

In 2019, former UN Special Rapporteur Philip Alston said he was worried we were “stumbling zombie-like into a digital welfare dystopia.” He had been researching how government agencies around the world were turning to automated decision-making systems (ADS) to cut costs, increase efficiency and target resources. ADS are technical systems designed to help or replace human decision-making using algorithms.

By Joanna Redden for Parental social licence for data linkage for service intervention on October 5, 2022

Hoe AI stigma’s van moslima’s versterkt en verspreidt

Je kunt tegenwoordig niet meer om AI heen. Of het nu om chatGPT gaat of om de app Lensa AI, wie zich in het digitale veld begeeft komt er vroeg of laat mee in aanraking. De balans opmaken op de vraag ‘is AI goed of slecht?’ is lastig, zeker omdat het nog niet zo wijdverbreid gebruikt wordt. Maar als we de experts mogen geloven, gaat dat in de toekomst anders zijn. De hoogste tijd voor de prijswinnende fotograaf Cigdem Yuksel om te onderzoeken wat het gebruik van AI betekent voor de beeldvorming van moslima’s. Lilith Magazine sprak met Yuksel en met Laurens Vreekamp, schrijver van the Art of AI.

By Aimée Dabekaussen, Cigdem Yuksel and Laurens Vreekamp for Lilith on April 6, 2023

What problems are AI-systems even solving? “Apparently, too few people ask that question”

A portrait photo of Felienne Hermans

In this interview with Felienne Hermans, Professor Computer Science at the Vrije Universiteit Amsterdam, she discusses the sore lack of divesity in the white male-dominated world of programming, the importance of teaching people how to code and, the problematic uses of AI-systems.

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