What are the key pitfalls of using AI-driven approaches for combating election misinformation, as discussed in the chapter?

The chapter identifies several pitfalls in using AI to combat election misinformation: algorithmic bias, poor data quality, data scalability issues, infrastructure challenges, and digital literacy or infrastructure limitations. These problems can undermine the fairness and accuracy of AI-driven electoral solutions.

The key pitfalls discussed in the chapter are algorithmic bias, data quality problems, data scalability issues, and infrastructure challenges. Algorithmic bias can make detection or flagging unfair across groups or narratives. Data quality issues mean the AI may rely on incomplete, unrepresentative, or corrupt datasets. Data scalability issues prevent tools from handling the volume and velocity of election-related content. Infrastructure challenges further limit deployment, especially in regions lacking robust digital infrastructure. Digital literacy gaps compound these problems by limiting public understanding and trust in AI-based countermeasures.