AI and the 2024 Presidential Election - Policy and Ethics Research Project
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About the Project
Project Overview
This research project examined the multifaceted role of artificial intelligence in shaping the 2024 U.S. Presidential Election. Our team analyzed how AI technologies were used across the political landscape, from campaign strategies and voter targeting to misinformation and deepfakes, treating the election less as a single event and more as a live case study in how fast AI capability was outpacing any existing regulatory or ethical framework built to handle it. What made 2024 distinct wasn't that AI was present in a campaign for the first time, it was the scale and speed at which synthetic content could now be produced, tested, and distributed without any equivalent infrastructure for catching or labeling it.
We evaluated existing and proposed policies through multiple ethical frameworks, including utilitarianism, deontology, and virtue ethics, deliberately refusing to settle on a single lens. A policy that looks defensible under a utilitarian cost-benefit analysis can look very different once evaluated against a deontological standard of individual rights, and we wanted our recommendations to hold up under more than one kind of ethical scrutiny before treating them as sound. Virtue ethics added a third dimension neither of the other two frameworks fully captured, asking not just whether an action produced good outcomes or respected rights, but what kind of character and intention was actually driving AI development and deployment in a political context.
As a three-person team, the work split across survey research, policy evaluation, and case study analysis, converging into a single paper that treated AI's role in the election as a genuinely multi-dimensional problem, technical, political, and ethical at once, rather than one that any single lens could fully explain on its own. That division of labor let each piece of the research get real depth, the survey work could focus entirely on capturing genuine public sentiment while the policy analysis worked through competing ethical frameworks in parallel, rather than one person trying to do a shallow version of everything.
The Issue
AI technologies were being deployed at unprecedented scale in the 2024 election cycle, from AI-generated campaign content and targeted advertising to deepfakes and synthetic media, with minimal regulatory oversight or transparency requirements. The infrastructure for detecting and disclosing AI-generated political content simply hadn't caught up to how easily that content could now be produced and distributed, meaning a synthetic video or a microtargeted ad generated by AI could reach voters with essentially the same credibility as content that was genuinely human-made, with no label distinguishing the two.
The public lacked awareness of how AI was shaping the information they consumed, and policymakers had limited frameworks for evaluating the ethical implications of AI in democratic processes. That combination, low public awareness plus underdeveloped policy frameworks, meant the systems meant to protect electoral integrity were largely reactive, built to respond after harm had already occurred rather than to prevent it. By the time a piece of AI-generated misinformation was identified and addressed, it had often already circulated widely enough that the correction reached a fraction of the audience the original content did.
Key Objectives
Analyze stakeholder sentiment and public survey data on AI in elections.
Evaluate existing AI policies using multiple ethical frameworks.
Examine the impact of AI-generated misinformation and deepfakes.
Develop transparency and disclosure recommendations for policymakers.
Apply utilitarianism, deontology, and virtue ethics to policy analysis.
Formulate actionable recommendations for protecting democratic integrity.
My Role & Impact
Stakeholder Sentiment Analysis
Analyzed public survey data and stakeholder perspectives on AI in elections, identifying patterns in how differently the public and policymakers understood the same technology.
Policy Evaluation
Evaluated existing AI policies using ethical frameworks including utilitarianism, deontology, and virtue ethics, testing whether a policy that looked sound under one lens still held up under another.
Misinformation Analysis
Examined AI-generated content including deepfakes and synthetic media in campaigns, grounding the ethical analysis in real 2024 cycle case studies rather than hypothetical scenarios.
Policy Recommendations
Developed proposals for AI disclosure requirements and content labeling standards, formulating actionable recommendations aimed at protecting democratic integrity going forward.
The Process
- Conducted a comprehensive literature review on AI use in political campaigns and elections, then designed and distributed surveys to capture public sentiment on AI in electoral processes, grounding the project in both existing research and original data rather than relying on one or the other alone. The literature review surfaced the theoretical and regulatory landscape, while the survey work tested whether that landscape actually matched how ordinary voters understood and experienced AI's presence in the election.
- Analyzed survey data using statistical methods to identify trends and perception patterns, looking specifically for where public understanding of AI's role in elections diverged from what was actually happening technically and politically. Those gaps between perception and reality turned out to be some of the most useful findings in the entire project, since a policy built only around what the public already understood would miss exactly the blind spots that made AI-driven misinformation effective in the first place.
- Applied three ethical frameworks, utilitarianism, deontology, and virtue ethics, to evaluate existing policies, deliberately testing each policy against more than one standard of ethical reasoning rather than settling on whichever framework happened to support a predetermined conclusion. Running the same policy through all three lenses consistently surfaced tradeoffs that a single-framework analysis would have missed entirely, a policy that maximized aggregate benefit under a utilitarian view sometimes failed a basic rights test under a deontological one.
- Examined real-world case studies of AI-generated misinformation and deepfakes from the 2024 cycle, then synthesized all of it, literature review, survey data, ethical analysis, and case studies, into a comprehensive paper with actionable policy recommendations. The case study work kept the ethical analysis honest, grounding abstract framework comparisons in specific, documented instances of what AI-generated content in a real election actually looked like and how it actually spread.
The Solution
The research produced a comprehensive analysis demonstrating that transparency is essential, clear disclosure requirements for AI-generated political content are crucial for informed voting, and that no single stakeholder group can solve this alone. A multi-stakeholder approach involving technologists, ethicists, and the public is necessary for effective AI policy, since technical solutions without ethical grounding, or ethical frameworks without technical understanding, both fall short of what the problem actually requires. Neither group alone has the full picture, technologists understand what's possible to build and detect, ethicists understand what tradeoffs a policy is actually making, and the public's own understanding shapes whether any resulting policy actually functions in practice.
Key recommendations include proactive regulation, since waiting for harm before regulating may be too late, mandatory AI content labeling in political advertising, and the application of multiple ethical lenses to fully evaluate AI's impact on democratic integrity. That last point mattered as much as any specific policy recommendation, since a single ethical framework applied in isolation tends to miss exactly the kind of tradeoff that made this issue difficult in the first place, and a policy that only satisfies one ethical standard is likely to face exactly the kind of legitimate challenge that undermines its effectiveness once implemented.
Common Questions
Frequently Asked Questions
A team research project asking what happens to democratic integrity when AI can generate a campaign's content faster than anyone can verify it.