An anthropologist among AI experts
Researching AI systems that show not only what they know, but what they cannot know.
I work at the intersection of anthropology, Digital Humanities, cultural heritage, and responsible AI, researching methods to make sources, uncertainty, absence, bias, and interpretive limits visible.
Integrating anthropological rigor with responsible computing practices.
Research on Mesoamerican archaeology, cultural archives, digital heritage, and the interpretation of incomplete records.
Research on AI-assisted knowledge systems that show sources, uncertainty, absence, bias, and interpretive limits instead of producing fluent answers without context.
Workshops, writing, and public-facing projects that help people use AI critically, practically, and responsibly.
An AI-assisted research system for specialised cultural heritage archives.
Heritage Lens links answers to sources and includes an epistemic transparency layer showing what the system does not know, and why.
Rather than producing fluent answers without context, it surfaces the shape of the underlying corpus, tracing source biases and knowledge absences directly from metadata.
I am a Digital Humanities researcher and anthropologist researching transparent and accountable AI-assisted knowledge systems, with a focus on cultural heritage, epistemic accountability, and human-AI collaboration.
My strength lies in the anthropological perspective I bring to research on AI-assisted knowledge systems. I focus on context, interpretation, absence, uncertainty, evidence, cultural memory, and the limits of knowledge.
Currently based in London, my research bridges Mesoamerican archaeological records with responsible computing, showing that understanding what a system *cannot* know is just as critical as showing what it does.
"Researching AI systems that respect the interpretive limits of evidence."
Presenting research on Digital Heritage, AI ethics, and Americanist studies.
Presenter / Demo
Presented research on computational transparency in cultural heritage and conducted an interactive demonstration of the Heritage Lens prototype, exploring epistemic accountability in AI-assisted information retrieval.
Presenter · University of Sheffield
Paper presentation on epistemological limits, source provenance, and the representation of archival absence in AI systems for Digital Humanities research.
Workshop Leader
Led the workshop "Working Sustainably with AI in Digital Heritage Research." Focused on designing long-term digital archives, evidentiary transparency, and the integration of accountable retrieval systems in cultural archives.
Presenter / Interactive Demo · University of Nottingham Ningbo China
Presented research on epistemic limits in cultural heritage data and delivered an interactive demonstration of the Heritage Lens research prototype with Mesoamerican archaeological case studies.
Presenter & Session Coordinator
Coordinated session panels and presented archaeological findings investigating Mesoamerican symbol systems, material culture, and the challenges of incomplete archaeological records.
Presenter / Demo
Demonstrated the Heritage Lens research prototype to AI developers and practitioners, introducing anthropological frameworks to make uncertainty, absence, and interpretive boundaries visible in generative retrieval systems.
Speaker / Participant
Invited speaker at the Global Women Inventors & Innovators Network Summit, discussing interdisciplinary innovation, cultural heritage preservation, and bringing an anthropological perspective to responsible computing.
Presenter · University of Nottingham
Presented research on archaeological databases, archival provenance, and computational challenges in tracking historical resource provenance and interpreting fragmented evidence.
Exploring Mesoamerican materiality, digital curation, and computational limits.
An extensive anthropological investigation analysing urbanisation processes, obsidian exchange routes, and ritual materiality in Olmec and early Mesoamerican societies.
Read Thesis (PDF)A conceptual framework outlining how Large Language Models can represent contested or missing history through metadata annotations instead of generic disclaimer boilerplate.
I welcome collaborations with researchers, museums, universities, public institutions, and AI organisations interested in transparent AI, digital heritage, epistemic accountability, and responsible knowledge systems.
I am particularly interested in research collaborations, pilot projects, invited talks, workshops, and interdisciplinary partnerships exploring how AI can better communicate evidence, uncertainty, and interpretive limits.