The United States is entering a broader debate over AI-driven deception in political advertising as the midterm cycle intensifies. A growing constellation of state laws, a cautious federal stance, and high-profile campaign examples are shaping how campaigns may use or be constrained by AI-generated content in the near term. Industry observers and watchdogs say the moment highlights a fundamental tension: AI can expand reach and persuasion for campaigns, but it also risks undermining trust if audiences cannot readily verify authenticity. Several developments this year illuminate where the policy terrain stands and where it might move next. Key takeaways State-level patchwork: Roughly 28 states have disclosure requirements for political ads, with many penalties civil in nature; Minnesota’s 2023 law also contemplates criminal penalties for certain deepfake disclosures as elections approach. High-profile Minnesota case: An AI-generated ad targeting a Minnesota political race raised questio...
A scholarly framework from Stevens Institute of Technology argues for a measured approach to enforcing insider trading rules in prediction markets, rather than pursuing an outright ban. The work suggests that price accuracy in these markets responds to enforcement intensity in a non-linear way, and that policy should aim for a calibrated middle ground to maintain both market integrity and participation. The paper, released on June 2 by Balbinder Singh Gill, assistant professor of finance, develops a formal economic model to explore how strictly insider trading in prediction markets should be policed. According to Cointelegraph, the model reveals that prediction-market price accuracy varies in a “hump-shaped” fashion with enforcement intensity: too little enforcement invites insiders to crowd out participants, while too much enforcement suppresses the insider’s informative contribution. Gill explains that tougher enforcement can actually enhance participation by limiting insider-driven ...