Im Jahr 2024 werden weltweit rund 70 Wahlen abgehalten. Künstliche Intelligenz (KI) beginnt bereits jetzt, Wahlkampfstrategien zu revolutionieren, die Automatisierung von Wahlverfahren zu beschleunigen und die Erstellung von Inhalten und Desinformationen im großen Stil zu ermöglichen, die die Stimmung der Wähler beeinflussen. Insbesondere die EU-Parlamentswahl im Juni und die US-Präsidentschaftswahl im November werden für dieses „KI-Wahljahr“ von großer Bedeutung sein. Angesichts der schnellen und zunehmenden Integration von KI in politische Kampagnen und Wahlprozesse sowie der Verbreitung von Social Bots und Deepfakes ist es von entscheidender Bedeutung, sowohl die Risiken als auch die Chancen zu erkennen, die KI für die Integrität demokratischer Wahlen darstellt.
Die KI/Demokratie-Initiative (AI/Democracy Initiative) der DGAP konzentriert sich auf die Untersuchung der Auswirkungen von KI auf globale Wahlprozesse im Superwahljahr 2024. Ihr Ziel ist es, eine öffentliche Debatte über die potenziellen Risiken und Vorteile dieser Technologie anzuregen, um das Vertrauen zu stärken, das für den Schutz der Demokratie und die Legitimität von Wahlprozessen unerlässlich ist.
Mithilfe einer Kombination aus quantitativer und qualitativer Forschung zu den diesjährigen Wahlen in den USA, der EU, in Brandenburg, Sachsen und Thüringen sowie in Mexiko, Ghana, Südafrika und Indien will die KI/Demokratie-Initiative eine solide Grundlage für ein differenziertes Verständnis der Auswirkungen von KI auf Wahlprozesse schaffen. Die Forschungsergebnisse sollen dafür genutzt werden, um mit Blick auf die Bundestagswahl 2025 evidenzbasierte Best Practices für eine widerstandsfähigere Demokratie zu empfehlen. Darüber hinaus möchte die Initiative die wichtigsten Akteure durch kontinuierliche Kommunikation und Veranstaltungen befähigen, die Auswirkungen von KI-Eingriffen zu verstehen und zu verhindern.
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Project Methodology: A Data-Driven Approach
To draw conclusions about the role AI played in India, South Africa, France, Mexico, Germany, and the United States, the first step involved was to identify AI incidents using a data-driven, quantitative approach. The primary aim of this analysis was to examine how frequently AI incidents occurred during elections and the nature of the threats they posed, if they did. These insights were used to identify trends and vulnerabilities that countries faced in 2024 concerning AI and elections.
One of the central challenges of this data-driven approach lies in the nondisclosure of AI use in election campaigns. Political actors often lack incentives to reveal their use of AI technologies, and the absence of robust platform regulations mandating the labeling of AI-generated content further complicates reliable identification. These limitations make it difficult to ensure a comprehensive dataset of AI-related incidents.
To address these challenges and enable an exploratory quantitative analysis, the media monitoring tool Cision was employed. This facilitated the extraction of AI-related news coverage in the periods leading up to the respective elections and ensured a robust comparability between varying contexts.
- Data Collection
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A targeted search using keywords related to artificial intelligence and elections was conducted for each country within a defined timeframe. The number of articles retrieved varied significantly by country, with the United States yielding the highest volume of relevant articles and South Africa the lowest. Country-specific prompts were developed for the searches, maintaining a consistent structure focused on AI, social media platforms, and prominent political parties and candidates. These prompts were tailored to each country's language and localized context to ensure relevance.
- Incident Identification
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The collected articles were manually filtered to identify specific AI incidents. Table X provides an overview of the total number of incidents identified in each country, highlighting that the United States had the highest number of recorded incidents, while South Africa had the fewest. It should be noted that some incidents were widely reported, with multiple articles or headlines referencing the same event. To avoid duplication, each incident was incorporated only once into the analysis.
- Classification of Incidents
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The identified incidents were categorized into six threat levels based on the DISARM framework outlined in the European External Action Service (EEAS) report. While the EEAS framework includes five threat categories, an additional category was introduced during the analysis. This sixth category accounted for cases where AI usage had political implications but did not align with the predefined EEAS threat categories. For India, South Africa, and Mexico, local project partners conducted the categorization to ensure contextual accuracy. All other case studies were conducted by the DGAP.
Limitations and Considerations
The reliance on newspaper articles for identifying AI incidents inherently introduces a selection bias. Only incidents reported in the media were included, meaning that the dataset likely captures larger, more publicized incidents while overlooking smaller-scale or less-publicized events. This limitation is particularly pronounced for incidents occurring exclusively on social media platforms, which may not have been reported by mainstream media.
It is essential to emphasize that the resulting list of AI-related incidents is not exhaustive. Instead, this study seeks to move beyond a qualitative focus on selectively known cases by employing an exploratory quantitative approach. While this methodology enables the identification of AI-related incidents on a broader scale, it cannot fully capture all political AI incidents preceding the elections. Nevertheless, the incidents covered by the media are arguably the most impactful and relevant to the public discourse, offering a critical starting point for developing a comparative framework for analyzing AI incidents globally.
Interactive Map
Explore the Findings
This interactive map offers a comprehensive overview of the AI/Democracy Initiative’s findings. Discover global trends and country-specific insights to gain a nuanced understanding of the challenges AI brings to electoral contexts worldwide. Navigate using the world map below or dive into detailed country-specific data.
For inquiries about the map, please contact our Data Analyst, Julian Schön, who developed it: schoen@junge.dgap.org
Country Studies
Publikationen
Partners
The Friedrich Naumann Foundation and the German Council on Foreign Relations have collaborated on this project.
