How is AI helping us in the Fight Against fake news?
Information spreads rapidly in the digital world, but inaccurate information spreads like wildfire. Because of the ease of communication, fake news can spread quickly, and the phenomenon has grown to the size of a pandemic – an information virus outbreak – or epidemic.
Every year, artificial intelligence improves its ability to generate strikingly human-like content. GPT-3 language models, for example, can write entire articles on their own using only a single-line prompt as input. Deep learning networks are frequently used to generate bogus images or videos known as deep fakes. Previously, doctoring videos was a time-consuming and costly process that necessitated a high level of technical expertise. The technology has become more accessible thanks to open-source software such as FaceSwap and DeepFaceLab. Deepfakes can now be easily created by anyone with limited expertise using a computer or a mobile phone.
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For millions and millions of people, the Online world has become the primary source of information. What we learn and see on the internet shapes our perspectives and worldview. Information access is critical for democracy. Fake news is bleeding democratic institutions through a thousand cuts by relentlessly hacking the truth.
Artificial intelligence (AI) and fake news appear to be inextricably linked. On the one hand, critics of emerging technologies claim that AI and automation processes have played a role in unleashing an apocalypse of blatantly false storylines on the helpless public. And on the other hand, in their never-ending pursuit of truth, some of the world's best scientific minds have already been developing new AI-powered alternatives that can detect deceptive stories.
Will they be Equal to the Challenge?
Generations after the gruelling fake news battles fueled by politicians on the international level, a new wave of huge manipulation of real truths and facts were witnessed during the 2020 and 2021 epidemics. Many of the online world's biggest players, such as Facebook and Google, are at the forefront of the fight against disinformation campaigns. They previously stated that they would be implementing powerful machine learning software to filter out misleading content on their platforms.
One of the primary reasons fake news has quickly become an epidemic is that it is presented more appealingly or engaging to readers/viewers. Some artificial intelligence is based on this assumption, and their machine learning algorithms have been trained for years by combating spam and phishing emails.
This method was tested in 2017 by the Fake News Challenge, a group of experts who volunteered in the fight against fake news. Their AI was based on stance detection, which estimates the comparative perspective (or stance) of an article's body text compared to the headline. Because of its text-analysis abilities, AI can determine whether a real human or a spambot wrote the message by evaluating the current content with the headline. But that was just the start.
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On May 18, 2021, Google explained how using natural language computation and search modelling techniques could pave the way for variation algorithms that can truly understand what people say. LaMDA (language model for dialogue applications) and MUM (Multitask Unified Model) are two of their most recent AI technologies. MUM is an AI model that can recognise complex human questions by cross-referencing text with pictures.
Such advanced technologies, which are used to understand and analyse linguistic cues, could also distinguish between fact and fiction and detect the most complicated human linguistic patterns used to write lies and hoaxes.
Another method is to perform an automated quick comparison of all similar news stories published across multiple media outlets to see how the facts portrayed differ. For example, the news outlet's credibility could be assessed by examining parameters such as the sources' dependability, writing and correction policies, and morality standards.
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If a particular website spreads fake news, it should be flagged as an untrustworthy source by groups in charge of monitoring the integrity of news sites, such as The Trust Project, and removed from news feeds. Google News is likely to use this method, as it has been announced that it will attract content from unspecified "trusted news sources." Thus, people will be directed away from extreme content, as happened on YouTube with flat-Earthers, or towards properly delineated "authoritative sources."
Journalism Some other global project led by the London School of Economics is AI, which aims to improve the dialogue between journalists and news organisations to fully utilise the potential of AI. AI in journalism could reduce inequalities faced by journalist communities in underserved areas and improve overall information quality in underserved communities.
Finally, simpler algorithms could be used to analyse a text and search for obvious grammar, commas, and spelling errors, detect phoney or fabricated images and cross-check the dissected semantic components of an article against credible sources.