{"id":74773,"date":"2024-11-06T23:07:27","date_gmt":"2024-11-07T04:07:27","guid":{"rendered":"https:\/\/world.edu\/?p=74773"},"modified":"2024-11-06T23:07:27","modified_gmt":"2024-11-07T04:07:27","slug":"how-close-are-we-to-an-accurate-ai-fake-news-detector","status":"publish","type":"post","link":"https:\/\/world.edu\/how-close-are-we-to-an-accurate-ai-fake-news-detector\/","title":{"rendered":"How close are we to an accurate AI fake news detector?"},"content":{"rendered":"

\"\"In the ambitious pursuit to tackle the harms from false content on\u00a0social media<\/a>\u00a0and\u00a0news websites<\/a>, data scientists are getting creative.<\/p>\n

While still in their training wheels, the\u00a0large language models (LLMs)<\/a>\u00a0used to create chatbots like ChatGPT are being recruited to spot\u00a0fake news<\/a>. With better detection, AI fake news checking systems may be able to warn of, and ultimately counteract, serious harms from\u00a0deepfakes<\/a>,\u00a0propaganda<\/a>,\u00a0conspiracy theories<\/a>\u00a0and\u00a0misinformation<\/a>.<\/p>\n

The next level AI tools will personalise detection of false content as well as protecting us against it. For this ultimate leap into user-centered AI, data science needs to look to behavioural and neuroscience.<\/p>\n

Recent work suggests we might\u00a0not always consciously know<\/a>\u00a0that we are encountering fake news. Neuroscience is helping to discover what is going on unconsciously. Biomarkers such as\u00a0heart rate<\/a>,\u00a0eye movements<\/a>\u00a0and\u00a0brain activity<\/a>) appear to subtly change in response to fake and real content. In other words, these biomarkers may be \u201ctells\u201d that indicate if we have been taken in or not.<\/p>\n

For instance, when humans look at faces, eye-tracking data shows that we scan for rates of blinking and\u00a0changes in skin colour<\/a>\u00a0caused by blood flow. If such elements seem unnatural, it can help us decide that we\u2019re looking at a deepfake. This knowledge can give AI an edge \u2013 we can train it to mimic what humans look for, among other things.<\/p>\n

The personalisation of an AI fake news checker takes shape by using findings from\u00a0human eye movement data<\/a>\u00a0and\u00a0electrical brain activity<\/a>\u00a0that shows what types of false content has the greatest impact neurally, psychologically and emotionally,\u00a0and for whom<\/a>.<\/p>\n

Knowing our specific interests, personality and\u00a0emotional reactions<\/a>, an AI fact-checking system could detect and anticipate which content would trigger the most severe reaction in us. This could help establish when people are taken in and what sort of material fools people the easiest.<\/p>\n

Counteracting harms<\/strong><\/h4>\n

What comes next is customising the safeguards. Protecting us from the harms of fake news also requires building systems that could intervene \u2013 some sort of\u00a0digital countermeasure to fake news<\/a>. There are several ways to do this such as warning labels, links to expert-validated credible content and even asking people to try to consider different perspectives when they read something.<\/p>\n

Our own personalised AI fake news checker could be designed to give each of us one of these countermeasures\u00a0to cancel out the harms from false content online<\/a>.<\/p>\n

Such technology is already being trialled. Researchers in the US have studied how people interact with\u00a0a personalised AI fake news checker of social media posts<\/a>. It learned to reduce the number of posts in a news feed to those it deemed true.\u00a0As a proof of concept<\/a>, another study using social media posts tailored additional news content to each media post to encourage users to view alternative perspectives.<\/p>\n

Accurate detection of fake news<\/strong><\/h4>\n

But whether this all sounds impressive or dystopian, before we get carried away it might be worth asking some basic questions.<\/p>\n

Much, if not all, of the work on\u00a0fake news, deepfakes, disinformation<\/a>\u00a0and\u00a0misinformation <\/a>\u00a0highlights the same problem that any lie detector would face.<\/p>\n

There are many types of lie detectors, not just the polygraph test. Some exclusively depend on linguistic analysis. Others are systems designed to read people\u2019s faces to detect if they are leaking micro-emotions that give away that they are lying. By the same token, there are AI systems that are designed to detect if a face is genuine or a deep fake.<\/p>\n

Before the detection begins, we all need to agree on what a lie looks like if we are to spot it. In fact, in\u00a0deception research<\/a>\u00a0shows it can be easier because you can instruct people when to lie and when tell the truth. And so you have some way of knowing the ground truth before you\u00a0train a human<\/a>\u00a0or a\u00a0machine<\/a>\u00a0to tell the difference, because they are provided with examples on which to base their judgements.<\/p>\n

Knowing how good an expert lie detector is depends on how often they call out a lie when there was one (hit). But also, that they don\u2019t frequently mistake someone as telling the truth when they were in fact lying (miss). This means they need to know what the truth is when they see it (correct rejection) and don\u2019t accuse someone of lying when they were telling the truth (false alarm). What this refers to is signal detection, and the same logic applies to\u00a0fake news detection<\/a>\u00a0which you can see in the diagram below.<\/p>\n

\"\"

If an AI was designed to detect fake news, here are the ways it could be right (hit, correct rejection) as well as how it could go wrong (miss, false alarm)<\/p><\/div>\n

For an AI system detecting fake news, to be super accurate, the hits need to be really high (say 90%) and so the misses will be very low (say 10%), and the false alarms need to stay low (say 10%) which means real news isn\u2019t called fake. If an AI fact-checking system, or a human one is recommended to us, based on signal detection, we can better understand how good it is.<\/p>\n

There are likely to be cases, as has been reported in a recent\u00a0survey<\/a>, where the news content may not be completely false or completely true, but partially accurate. We know this because the speed of news cycles means that what is considered accurate at one time, may later\u00a0be found to be inaccurate,<\/a>\u00a0or vice versa. So, a fake news checking system has its work cut out.<\/p>\n

If we knew in advance what was faked and what was real news, how accurate are biomarkers at indicating unconsciously which is which? The answer is not very. Neural activity\u00a0is most often the same<\/a>\u00a0when we come across real and fake news articles.<\/p>\n

When it comes to eye-tracking studies, it is worth knowing that there are different types of data collected from eye-tracking techniques (for example the length of time our eye fix on an object, the frequency that our eye moves across a visual scene).<\/p>\n

So depending on what is analysed, some studies show that\u00a0we direct more attention<\/a>\u00a0when viewing false content, while others show the\u00a0opposite<\/a>.<\/p>\n

Are we there yet?<\/strong><\/h4>\n

AI fake news detection systems on the market are already using insights from behavioural science to help\u00a0flag and warn us against fake news <\/a>\u00a0content. So it won\u2019t be a stretch for the same AI systems to start appearing in our news feeds with customised protections for our unique user profile. The problem with all this is we still have a lot of basic ground to cover in knowing what is working, but also checking\u00a0whether we want this<\/a>.<\/p>\n

In the worst case scenario, we only see fake news as a problem online as an excuse to solve it using\u00a0AI<\/a>. But false and inaccurate content is everywhere, and gets discussed\u00a0offline<\/a>. Not only that, we don\u2019t by default believe all fake news, some times we use it in discussions to\u00a0illustrate bad ideas<\/a>.<\/p>\n

In an imagined best case scenario, data science and behavioural science is confident about the scale of the various harms fake news might cause. But, even here, AI applications combined with scientific wizardry might still be very poor substitutes for less sophisticated but more effective solutions.<\/p>\n

Author Bio:<\/strong> Magda Osman is a Professor of Policy Impact at the University of Leeds<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In the ambitious pursuit to tackle the harms from false content on\u00a0social media\u00a0and\u00a0news websites, data scientists are getting creative. While still in their training wheels, the\u00a0large language models (LLMs)\u00a0used to create chatbots like ChatGPT are being recruited to spot\u00a0fake news. With better detection, AI fake news checking systems may be able to warn of, and […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[33324],"coauthors":[11733],"class_list":["post-74773","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-fake-news"],"acf":[],"yoast_head":"\nHow close are we to an accurate AI fake news detector? - World leading higher education information and services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/world.edu\/how-close-are-we-to-an-accurate-ai-fake-news-detector\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"How close are we to an accurate AI fake news detector? - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"In the ambitious pursuit to tackle the harms from false content on\u00a0social media\u00a0and\u00a0news websites, data scientists are getting creative. 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