Better Images of AI

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From Better Images of AI

Racist Technology in Action: AI-generated image tools amplify harmful stereotypes

Deep learning models that allow you to make images from simple textual ‘prompts’ have recently become available for the general public. Having been trained on a world full of visual representations of social stereotypes, it comes as no surprise that these tools perpetuate a lot of biased and harmful imagery.

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AI innovation for whom, and at whose expense?

This fantastic article by Williams, Miceli and Gebru, describes how the methodological shift of AI systems to deep-learning-based models has required enormous amounts of “data” for models to learn from. Large volumes of time-consuming work, such as labelling millions of images, can now be broken down into smaller tasks and outsourced to data labourers across the globe. These data labourers have terribly low wagen, often working in dire working conditions.

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A Primer on AI in/from the Majority World

A resource by Sareeta Amrute, Ranjit Singh, and Rigoberto Lara Guzmán exploring the presence of artificial intelligence and technology in the Majority World. 160 thematic works, available in English and Spanish.

By Ranjit Singh, Rigoberto Lara Guzmán and Sareeta Amrute for Data & Society on September 14, 2022

Defective Altruism

Socialism is the most effective altruism. Who needs anything else? The repugnant philosophy of “Effective Altruism” offers nothing to movements for global justice.

By Nathan J. Robinson for Current Affairs on September 19, 2022

Met kunstmatige intelligentie kun je ook iets goeds doen.

Je kunt al snel denken dat kunstmatige intelligentie alleen maar iets is om voor op te passen. Een machtig wapen in handen van de overheid of van techbedrijven die zich schuldig maken aan privacyschending, discriminatie of onterechte straffen. Maar we kunnen met algoritmen juist problemen oplossen en werken aan een rechtvaardiger wereld, zegt informaticus Sennay Ghebreab van het Civic AI Lab tegen Kustaw Bessems. Dan moeten we wel de basis een beetje snappen én er meer over te zeggen hebben.

By Kustaw Bessems and Sennay Ghebreab for Volkskrant on September 11, 2022

AI-trained robots bring algorithmic biases into robotics

A recent study in robotics has drawn attention from news media such as The Washington Post and VICE. In this study, researchers programmed virtual robots with popular artificial intelligence algorithms. Then, these robots were asked to scan blocks containing pictures of people’s faces and make decisions to put some blocks into a virtual “box” according to an open-ended instruction. In the experiments, researchers quickly found out that these robots repeatedly picked women and people of color to be put in the “box” when they were asked to respond to words such as “criminal”, “homemaker”, and “janitor”. The behaviors of these robots showed that sexist and racist baises coded in AI algorithms have leaked into the field of robotics.

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Racist Technology in Action: Turning a Black person, White

An example of racial bias in machine learning strikes again, this time by a program called PULSE, as reported by The Verge. Input a low resolution image of Barack Obama – or another person of colour such as Alexandra Ocasio-Cortez or Lucy Liu – and the resulting AI-generated output of a high resolution image, is distinctively a white person.

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Racist Techology in Action: Beauty is in the eye of the AI

Where people’s notion of beauty is often steeped in cultural preferences or plain prejudice, the objectivity of an AI-system would surely allow it to access a more universal conception of beauty – or so thought the developers of Beauty.AI. Alex Zhavoronkov, who consulted in the development of the Beaut.AI-system, described the dystopian motivation behind the system clearly: “Humans are generally biased and there needs to be a robot to provide an impartial opinion. Beauty.AI is the first step in a much larger story, in which a mobile app trained to evaluate perception of human appearance will evolve into a caring personal assistant to help users look their best and retain their youthful looks.”

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AI recognition of patient race in medical imaging: a modelling study

Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person’s race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient’s racial identity from medical images.

By Ananth Reddy Bhimireddy, Ayis T Pyrros, Brandon J. Price, Chima Okechukwu, Haoran Zhang, Hari Trivedi, Imon Banerjee, John L Burns, Judy Wawira Gichoya, Laleh Seyyed-Kalantari, Lauren Oakden-Rayner, Leo Anthony Celi, Li-Ching Chen, Lyle J. Palmer, Marzyeh Ghassemi, Matthew P Lungren, Natalie Dullerud, Ramon Correa, Ryan Wang, Saptarshi Purkayastha, Shih-Cheng Huang Po-Chih Kuo and Zachary Zaiman for The Lancet on May 11, 2022

Don’t miss this 4-part journalism series on ‘AI Colonialism’

The MIT Technology Review has written a four-part series on how the impact of AI is “repeating the patterns of colonial history.” The Review is careful not to directly compare the current situation with the colonialist capturing of land, extraction of resources, and exploitation of people. Yet, they clearly show that AI does further enrich the wealthy at the tremendous expense of the poor.

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Exploitative labour is central to the infrastructure of AI

In this piece, Julian Posada writes about a family of five in Venezuela, who synchronise their routines so that there will always be two people at the computer working for a crowdsourcing platform to make a living. They earn a few cents per task in a cryptocurrency and are only allowed to cash out once they’ve made at least the equivalent of USD 10. On average they earn about USD 20 per week, but their earnings can be erratic, resulting in extreme stress and precarity.

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Massive Predpol leak confirms that it drives racist policing

When you or I seek out evidence to back up our existing beliefs and ignore the evidence that shows we’re wrong, it’s called “confirmation bias.” It’s a well-understood phenomenon that none of us are immune to, and thoughtful people put a lot of effort into countering it in themselves.

By Cory Doctorow for Pluralistic on December 2, 2021

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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Opinion: Biden must act to get racism out of automated decision-making

Despite Biden’s announced commitment to advancing racial justice, not a single appointee to the task force has focused experience on civil rights and liberties in the development and use of AI. That has to change. Artificial intelligence, invisible but pervasive, affects vast swaths of American society and will affect many more. Biden must ensure that racial equity is prioritized in AI development.

By ReNika Moore for Washington Post on August 9, 2021

Discriminating Data

How big data and machine learning encode discrimination and create agitated clusters of comforting rage.

By Wendy Hui Kyong Chun for The MIT Press on November 1, 2021

Raziye Buse Çetin: ‘The absence of marginalised people in AI policymaking’

Creating welcoming and safe spaces for racialised people in policymaking is essential for addressing AI harms. Since the beginning of my career as an AI policy researcher, I’ve witnessed many important instances where people of color were almost totally absent from AI policy conversations. I remember very well the feeling of discomfort I had experienced when I was stopped at the entrance of a launch event for a report on algorithmic bias. The person who was tasked with ushering people into the meeting room was convinced that I was not “in the right place”. Following a completely avoidable policing situation; I was in the room, but the room didn’t seem right to me. Although the topic was algorithmic bias and discrimination, I couldn’t spot one racialised person there — people who are most likely to experience algorithmic harm.

By Raziye Buse Çetin for Who Writes The Rules on March 11, 2019

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