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"The results are unfortunately not surprising - countless studies have shown that facial recognition is susceptible to bias. A paper last fall by University of Colorado, Boulder researchers demonstrated that AI from Amazon, Clarifai, Microsoft, and others maintained accuracy rates above 95% for cisgender men and women but misidentified trans men as women 38% of the time."

"Can AI ever be unbiased? As AI systems become more integrated into our daily lives, it's crucial that we understand the complexities of bias and how it impacts these technologies. From chatbots to hiring algorithms, the potential for AI to perpetuate and even amplify existing biases is a genuine concern. "

"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."

"However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself."

"Facebook will rely on users to report fake news despite evidence that suggests users have a difficult time assessing or identifying fake news. Teens seem to be especially vulnerable to fake news. A recent study by researchers at Stanford found that middle and high school students have a difficult time detecting fake news from real news, or detecting bias in tweets and Facebook statuses."

"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."

"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."

"This way of thinking about search results is wrong. Recent studies suggest that search engines, rather than providing a neutral way to find information, may actually play a major role in shaping public opinion on political issues and candidates. Some research has even argued that search results can affect the outcomes of close elections. In a study aptly titled In Google We Trust participants heavily prioritized the first page of search results, and the order of the results on that page, and continued to do so even when researchers reversed the order of the actual results."

"Under the proposal, online platforms would have to spend hundreds of millions of euros on algorithmic copyright filters that would compare everything users tried to post with a database of supposedly copyrighted works, which anyone could add anything to, and block any suspected matches. This would snuff out all the small EU competitors to America's Big Tech giants, and put all Europeans' communications under threat of arbitrary censorship by balky, unaccountable, easily abused algorithms."

"They found that despite the 40% "ad-tech" premium charged by behavioral ad companies, the ads only added about 4% the media companies that published them, meaning that behavioral advertising is a losing proposition. "

" "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.""

"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 battle over algorithms in healthcare has come into full view since last fall. The debate only intensified in the wake of the coronavirus pandemic, which has disproportionately devastated Black and Latino communities. In October, Science published a study that found one hospital unintentionally directed more white patients than Black patients to a high-risk care management program because it used an algorithm to predict the patients' future healthcare costs as a key indicator of personal health. Optum, the company that sells the software product, told Mashable that the hospital used the tool incorrectly. "

"More algorithmic decision making and decision augmenting systems will be used in the coming years. Unlike the approach taken for A-levels, future systems may include opaque AI-led decision making. Despite such risks there remain no clear picture of how public sector bodies - government, local councils, police forces and more - are using algorithmic systems for decision making."