Group items matching

in title, tags, annotations or url

"Twitter's image cropping algorithm prefers younger, slimmer faces with lighter skin, an investigation into algorithmic bias at the company has found. The finding, while embarrassing for the company, which had previously apologised to users after reports of bias, marks the successful conclusion of Twitter's first ever "algorithmic bug bounty"."

"Algorithms and the data that drive them are designed and created by people, which means those systems can carry biases based on who builds them and how they're ultimately deployed. Safiya Umoja Noble, author of Algorithms of Oppression: How Search Engines Reinforce Racism, offers a curated reading list exploring how technology can replicate and reinforce racist and sexist beliefs, how that bias can affect everything from health outcomes to financial credit to criminal justice, and why data discrimination is a major 21st century challenge."

"On inbuilt bias in algorithms, Sharkey said: "There are so many biases happening now, from job interviews to welfare to determining who should get bail and who should go to jail. It is quite clear that we really have to stop using decision algorithms, and I am someone who has always been very light on regulation and always believed that it stifles innovation."

"How that effects systems of governance has yet to be fully explored, but there are cautions. "Algorithms are only as good as the data on which they are based, and the problem with current AI is that it was trained on data that was incomplete or unrepresentative and the risk of bias or unfairness is quite substantial," says West. The fairness and equity of algorithms are only as good as the data-programming that underlie them. "For the last few decades we've allowed the tech companies to decide, so we need better guardrails and to make sure the algorithms respect human values," West says. "We need more oversight.""

"There's an increasingly popular solution to this problem: why not let an intelligent algorithm make hiring decisions for you? Surely, the thinking goes, a computer is more able to be impartial than a person, and can simply look at the relevant data vectors to select the most qualified people from a heap of applications, removing human bias and making the process more efficient to boot."

"But users began to spot flaws in the feature over the weekend. The first to highlight the issue was PhD student Colin Madland, who discovered the issue while highlighting a different racial bias in the video-conference software Zoom. When Madland, who is white, posted an image of himself and a black colleague who had been erased from a Zoom call after its algorithm failed to recognise his face, Twitter automatically cropped the image to only show Madland."

"Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making."

"Facebook is mired in a series of controversies about the curation of its news feed, from its broadcasting live killings, to editing out an iconic photo of the Vietnam war, to accusations of political bias. It recently tried to smooth the process out by firing its human editors … only to find the news feed degenerated into a mass of fake and controversial news stories."

"Twitter has admitted it amplifies more tweets from rightwing politicians and news outlets than content from leftwing sources. The social media platform examined tweets from elected officials in seven countries - the UK, US, Canada, France, Germany, Spain and Japan. It also studied whether political content from news organisations was amplified on Twitter, focusing primarily on US news sources such as Fox News, the New York Times and BuzzFeed."

"And that was a genuine first - the only time I can recall when an algorithmic decision had been challenged in public protests that were powerful enough to prompt a government climbdown. In a world increasingly - and invisibly - regulated by computer code, this uprising might look like a promising precedent."

The ensuing controversy has sparked renewed debates about the ways in which algorithms can perpetuate biases, yielding unintended and often offensive results.

"A simple Google image search highlighted on Twitter has been said to highlight the pervasiveness of racial bias and media profiling. "Three black teenagers" was a trending search on Google on Thursday after a US high school student pointed out the stark difference in results for "three black teenagers" and "three white teenagers"."

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"The paper, co-authored by researchers inside and outside Google, contended that technology companies could do more to ensure AI systems aimed at mimicking human writing and speech do not exacerbate historical gender biases and use of offensive language, according to a draft copy seen by Reuters."

"At the heart of the problem that troubles Ming is the training that computer engineers receive and their uncritical faith in AI. Too often, she says, their approach to a problem is to train a neural network on a mass of data and expect the result to work fine. She berates companies for failing to engage with the problem first - applying what is already known about good employees and successful students, for example - before applying the AI."

" "Why isn't my face being detected? We have to look at how we give machines sight," she said in a TED Talk late last year. "Computer vision uses machine-learning techniques to do facial recognition. You create a training set with examples of faces. However, if the training sets aren't really that diverse, any face that deviates too much from the established norm will be harder to detect.""

"A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care. That presents a potential risk to patient confidentiality, according to Dr Caroline Green, an early career research fellow at the Institute for Ethics in AI at Oxford, who surveyed care organisations for the study. "If you put any type of personal data into [a generative AI chatbot], that data is used to train the language model," Green said. "That personal data could be generated and revealed to somebody else." She said carers might act on faulty or biased information and inadvertently cause harm, and an AI-generated care plan might be substandard."