"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.
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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."
"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."
"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."
"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."
"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."
" "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.""