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Case of Misinformation AI attacks

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AI-enabled disinformation attacks are a growing concern, as they can spread false narratives quickly and at scale
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. Here are some key points from the search results on how to deal with AI-enabled disinformation:
  • Algorithmic detection: AI solutions have been particularly effective in detecting and removing illegal or dubious content
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  • Flagging: Disinformation can be flagged either algorithmically or through manual reporting channels
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  • Policies for handling situations: There will need to be policies for handling situations in which an AI system makes exactly the wrong decision
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  • Limitations of AI solutions: AI systems come with their own set of limitations and challenges, such as the potential for algorithms to make exactly the wrong decision due to limited data available in the early stages of a rapid disinformation attack
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  • Injection attacks: Misinformation can flow into AI models as well as from them, meaning that at least some generative AI will be subject to "injection attacks," where malicious users teach lies to the programs, which then spread them
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  • Spread of misinformation: The threat posed by AI and so-called deepfakes always seemed a year or two away. However, synthetic images that emerged were often crude, unconvincing, and costly to produce. Generative AI can not only rapidly produce targeted campaign emails, texts or videos, it also could be used to mislead voters, impersonate candidates and undermine elections on a scale and at a speed not yet seen
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  • Counter-disinformation strategies: To truly address the spread of misinformation and stymie disinformation operations, whether AI-powered or not, society needs counter-disinformation strategies
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What are the limitations and challenges of using AI to combat disinformation

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Artificial Intelligence (AI) has been used to counter disinformation online, but it comes with its own set of limitations and challenges. One of the limitations is that AI models for detecting new pieces of disinformation have technical limitations, which means that AI-based detection remains largely limited to hybrid, human-machine approaches in which human fact-checkers identify a piece of disinformation and only thereafter is an AI model used to detect variations of disinformation. Another limitation is that algorithms can be influenced by a confirmation bias effect, leading to a boost in confidence in a wrong conclusion by selectively giving greater weight to inputs bolstering that conclusion. This could lead an algorithm to quickly converge on an incorrect conclusion that would need human intervention. Additionally, AI-enhanced disinformation operations will further exacerbate political polarization, erode citizen trust in societal institutions, and blur the lines between truth and lies. Therefore, while it is worth allocating resources to combating disinformation using AI, caution and transparency are necessary given the potential consequences
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