Many online platforms, like social media websites, news websites and entertainment websites, use AI algorithms to automatically edit, curate, promote and even create content. Many of these algorithms are trained on data which may be biased in some way: it might not be diverse enough to represent the true cultural and ethnic diversity of society, or it could be consciously or unconsciously designed based on stereotyped or politically and socially biased ideas.
The AI algorithms then reflect those biases when performing their task, amplifying social prejudice across the digital and social media platforms that people use everyday. When a biased AI tool is choosing what articles people see in their newsfeed, or editing an image automatically to fit a certain format, it means people are only seeing a biased view of the world.
Earlier this month, in the aftermath of a decisive yet contested election, MediaJustice, in partnership with MediaJustice Network member WITNESS, brought together nearly 30 civil society groups, researchers, journalists, and organizers to discuss the impacts visual disinformation has had on institutions and information systems. Deepfakes and disinformation can be used in used in racialised disinformation […]
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The present experiment examined how people adjust their judgment after they learn that crucial information on which their initial evaluation was based is incorrect. In line with our expectations, the results showed that people generally do adjust their attitudes, but the degree to which they correct their assessment depends on their cognitive ability. A study […]
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Research paper about responsible AI Toxic and abusive language threaten the integrity of public dialogue and democracy. In response, governments worldwide have enacted strong laws against abusive language that leads to hatred, violence and criminal offences against a particular group. The responsible (i.e. effective, fair and unbiased) moderation of abusive language carries significant challenges. Our […]
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AI researchers say scientific publishers help perpetuate racist algorithms The news: An open letter from a growing coalition of AI researchers is calling out scientific publisher Springer Nature for a conference paper it reportedly planned to include in its forthcoming book Transactions on Computational Science & Computational Intelligence. The paper, titled “A Deep Neural… Crime […]
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A Google-funded report examines the relationship between white supremacists and the internet, but it makes scant reference—all of it positive—to YouTube, the company’s platform that many experts blame more than any other for driving people to extremism. YouTube’s algorithm has been found to direct users to extreme content, sucking them into violent ideologies.
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After recently announcing Equity and Inclusion teams to investigate racial bias across their platforms, and undergoing a global advertising boycott over alleged racial discrimination, is Facebook doing enough to tackle racial bias? Disinformation driven via bots that game the AI systems of social media platforms to reinforce racial myths and attitudes as well as the […]
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Yeah, great start after sacking human hacks: Microsoft’s AI-powered news portal mixes up photos of women-of-color in article about racism. Blame Reg Bot 9000 for any blunders in this story, by the way News media is being automated and generated by AI which can incur bias from data sets for text generation.
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Another reminder that bias, testing, diversity is needed in machine learning: Twitter’s image-crop AI may favour white men, women’s chests Digital imagery favours white people in framing and de-emphasises the visibility of non-white people. Strange, it didn’t show up during development, says social network
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Google Cloud’s AI recog code ‘biased’ against black people – and more from ML land Including: Yes, that nightmare smart toilet that photographs you mid… er, process Digital imagery tagging provides negative context for non white people.
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Once again, racial biases show up in AI image databases, this time turning Barack Obama white Researchers used a pre-trained off-the-shelf model from Nvidia. Digital imagery tagging provides negative context for non white people.
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