10 citations · 17 across the 17 of their papers we have counts for
6 papers · 1 filter
Auditing Google's AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy
Desheng Hu, Joachim Baumann, Aleksandra Urman +4
Google Search increasingly surfaces AI-generated content through features like AI Overviews (AIO) and Featured Snippets (FS), which users frequently rely on despite having no contr…
Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study
Joachim Baumann, Aleksandra Urman, Ulrich Leicht-Deobald +3
The rapid adoption of generative artificial intelligence (GenAI) technologies has led many organizations to integrate AI into their products and services, often without considering…
Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Joachim Baumann, Paul Röttger, Aleksandra Urman +4
Large language models are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outp…
On the Societal Impact of Machine Learning
Joachim Baumann
This PhD thesis investigates the societal impact of machine learning (ML). ML increasingly informs consequential decisions and recommendations, significantly affecting many aspects…
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Tiancheng Hu, Joachim Baumann, Lorenzo Lupo +3
Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human b…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…