Our research spans AI safety evaluation, governance and regulation, computational social science, cultural heritage preservation, and affective computing — always grounding technical methods in humanistic depth.
Syntactic framing vulnerabilities, threat modeling for multi-agent architectures, and evaluation frameworks for autonomous AI systems. Our co-founders' work on how language models process negation, prohibition, and persuasion informs safety evaluation through their role in the NIST US AI Safety Institute Consortium (CAISI) (NIST CAISI).
Comparative global AI regulation (EU, China, US). Open-source AI policy analysis. Behavioral prediction and ethical auditing of LLM decision-making systems. Co-authored policy paper with International Public AI. This work also includes cultural data governance, including provenance infrastructure and rights-aware AI training practices developed through the AI, IP & Culture Repository co-design process.
Multi-agent simulation of high-stakes human decisions including judicial recidivism prediction. Over 90 model/reasoning combinations benchmarked. Funded through Notre Dame–IBM Tech Ethics Lab.
Rescuing endangered New Orleans heritage archives using AI. Community-governed data sovereignty for historically marginalized populations. One of 23 teams selected worldwide for the Schmidt Sciences Humanities and AI Virtual Institute (HAVI) program.
Open-source methodology (code) for diachronic sentiment analysis in text and film. Created the first computational methodology for surfacing emotional arc in full-length literary narratives. Student research using the methodology has been downloaded more than 100,000 times from institutions in 198 countries.
Our 2023 paper "The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centered AI" (International Journal of Humanities and Arts Computing) established the intellectual framework for integrating computational methods with ethics, governance, and humanistic inquiry — and the evidence base for why this integration matters for AI safety and public benefit. (higher education)