2025-04-25 –, Helium3
In general elections, voters often face the challenge of navigating complex political landscapes and extensive party manifestos. To address this, we developed Electify, an interactive application that utilizes Retrieval-Augmented Generation (RAG) to provide concise summaries of political party positions based on individual user queries. During its first roll-out for the European Election 2024, Electify attracted more than 6,000 active users. This talk will explore its development and deployment. It will focus on its technical architecture, the integration of data from party manifestos and parliamentary speeches, and the challenges of ensuring political neutrality and providing accurate replies. Additionally, we will discuss user feedback and ethical considerations, focusing on how generative AI can enhance voter information systems.
In general elections, voters often face the challenge of navigating complex political landscapes. These challenges include understanding the differences between nuanced policy positions, comparing extensive party manifestos, and reconciling conflicting information from various sources. The sheer volume of information and the high frequency of elections can lead to voter fatigue and disengagement [1]. Existing tools like Wahlomat are helpful for voters but don’t adapt well to individual preferences or specific questions. To address these issues, we developed Electify—an interactive application designed to empower voters by addressing these pain points.
Using Retrieval-Augmented Generation (RAG), Electify simplifies the decision-making process by enabling users to access concise and relevant summaries of political party positions tailored to their individual queries. Our user interface provides the possibility to fact-check the generated responses by directly showing the original sources. Additionally, we included a blinding feature to combat confirmation bias: users can hide party names and read summaries of their positions before unblinding. This talk will explore the technical development and deployment of Electify, covering its architecture, integration of data from party manifestos and parliamentary speeches, and strategies to maintain political neutrality and accuracy in responses. In particular, we will discuss our efforts to use reranking to improve context relevancy and LLM-as-a-judge evaluation for parameter optimization. We identify a trade-off between factual accuracy and the frequency of denied responses, which we think is highly relevant for generative AI systems that operate within sensitive areas like voter information [2].
During its first roll-out for the European Election 2024, Electify received significant attention, attracting 6,000 active users who leveraged the platform to make more informed and confident voting decisions. We will address the lessons learned from user feedback and discuss the ethical considerations involved, emphasizing the potential of generative AI to enhance voter information systems and promote political engagement.
Contributors: Christian Liedl, Anna Neifer, Joshua Nowak
Github Repository
[1] Kostelka et al. "Election frequency and voter turnout." Comparative Political Studies 56.14 (2023)
[2] Cao, Lang. "Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism." arXiv:2311.01041 (2023).
Intermediate
Expected audience expertise: Python:Novice
Public link to supporting material, e.g. videos, Github, etc.:
Christian hat einige Jahre als Physiker auf dem Gebiet der experimentellen Quantenoptik geforscht und sich seit 2024 auf Data Science und Künstliche Intelligenz spezialisiert.