Application of Artificial Intelligence in Anthropology
AI is transforming anthropology not only as a computational research tool, but also as a new object of anthropological inquiry.
Artificial Intelligence is emerging both as a methodological tool and as an object of anthropological inquiry. Discuss its applications in anthropology and critically examine the methodological and ethical challenges associated with its use.
Artificial Intelligence (AI) is creating a new intersection between technology and anthropology. Its significance can be understood through two complementary dimensions.
First, AI functions as a methodological tool that assists anthropologists in processing, analysing and interpreting large ethnographic, archaeological and paleoanthropological datasets.
Second, AI itself has become an object of anthropological inquiry, with anthropologists examining its relationship with culture, labour, power, governance, inequality and human–machine interaction.
AI in Anthropology: The Basic Framework
Two ways of understanding AI in anthropology
→
Study humans & culture
+
AI as an OBJECT
→
Study technology & society
This distinction is fundamental for a Paper I answer. AI does not merely increase the analytical capacity of anthropology; it also creates new anthropological questions about how technological systems are produced, interpreted and governed.
AI as a Methodological Tool
Ethnographic Research
Machine Learning (ML) and Natural Language Processing (NLP) can be integrated into ethnographic workflows to process large cultural datasets, identify patterns in texts and images, and support the analysis of fieldnotes.
Applications include transcript segmentation, qualitative coding, thematic analysis and large-scale textual analysis.
Computational Fieldnote Analysis
Xu and Hernandez (2025) combined unsupervised topic modelling, S-BERT, GPT and ethnographic close-reading to examine children’s moral dramas in anthropological fieldnotes from rural Taiwan.
Human–AI hybrid approach
Multi-Agent Ethnography
Artz (2026) proposes multi-agent ethnography, in which LLM-based AI agents can function as configurable collaborators across different stages of research.
Codebook → Segmentation → Coding
Cultural Data Processing
Deep learning and NLP can be used to process cultural material such as ornaments, folklore narratives and social-media data.
This creates possibilities for analysing cultural patterns at a scale that would be difficult through manual processing alone.
Archaeological Archives
Machine Learning and Named Entity Recognition can help index and analyse large archaeological archives. The cited application reported identifying 30% more cremations through computational analysis.
Tenzer et al. (2024) — ~200K texts
Paleoanthropology
Machine-learning approaches can be applied to anthropological datasets relevant to human evolutionary research.
However, methodological quality is crucial because poorly designed ML applications can generate unreliable conclusions about human evolutionary history.
Case Study: The “Thick Machine”
A neural network was trained on approximately 175,000 Facebook comments to predict emoji reactions. It achieved an accuracy of 51%.
Rather than treating machine failure simply as an error, the researchers used failures to locate culturally ambiguous situations requiring deeper anthropological interpretation.
AI can identify a pattern, but the anthropologist must still explain its cultural meaning.
Computational analysis in service of anthropological explication
AI as an Object of Anthropological Study
Anthropology increasingly studies AI not merely as a technical instrument but as a cultural artifact shaped by social practices, power relations and ideological assumptions.
Srivastava (2026)
Identifies four major areas of anthropological inquiry:
- Material systems and human labour behind AI
- Algorithms and cultural worldviews
- Human–machine interaction
- Power and governance in AI decision-making
Sapignoli (2021)
Ethnographic approaches can trace the social life of AI systems in global governance and reveal how classification criteria embedded within datasets can reproduce structural inequalities.
Linguistic Anthropology and AI
A linguistic-anthropological approach examines how the features of Large Language Models encourage users to attribute personhood to chatbots.
The focus therefore moves beyond machine capability to questions of personhood, semiotic ideologies, human interpretation of machine communication, and the cultural meanings attached to AI.
AI, Humanness and Sociality
Banerji (2023)
Banerji describes an AI system developed using ethnographic fieldwork on nonverbal interaction.
Practitioners’ evaluations of the system’s “humanness” became an ethnographic site for examining the performative variability of human sociality.
Generative AI in Anthropology
Roe (2025)
GenAI outputs can be understood as culturally mediated artifacts. Anthropological theories of technology can therefore contribute to critical AI literacy.
Longkumer (2025)
Generative AI can act as a “collaborative textualiser” in anthropology teaching — simulated interviews, co-written fieldnotes and AI-mediated essay critique.
Concern: bias · authenticity · authorship
Methodological Challenges
Training & Testing
Inappropriate training–testing splits can generate unreliable model performance.
Outdated Algorithms
Use of unsuitable or outdated computational techniques can undermine research validity.
Data Quality
Missing or inadequate datasets can distort anthropological conclusions.
Transparency
Lack of transparency can make computational findings difficult to evaluate or reproduce.
Contextual Reductionism
Statistical pattern recognition cannot automatically capture cultural meaning, ambiguity and context.
Interdisciplinary Expertise
Anthropologists require computational expertise or collaboration with ML specialists.
Ethical Challenges
Algorithmic Bias
AI systems learn from datasets that may contain existing social inequalities. Computational classifications can therefore reproduce or amplify those inequalities.
Authorship & Authority
When AI contributes to coding, interpretation or textualisation, questions arise about authorship and ethnographic authority.
Authenticity
AI-assisted fieldnotes and textual production raise questions about the authenticity of ethnographic knowledge.
Human Costs
AI applications in archaeology and other fields also require attention to their implications for human labour, heritage and institutional practice.
AI vs Thick Description?
Krause-Jensen & Hau (2025)
The increasing use of AI in anthropological analysis raises questions about authorship and ethnographic authority. One important question is what distinguishes “inventive prompting” from anthropological “thick description”.
Thick description is fundamentally concerned with contextual, interpretive understanding of social action. AI-assisted pattern generation can support this process, but should not automatically be treated as equivalent to ethnographic interpretation.
Why AI Cannot Simply Replace the Anthropologist
Pedersen (2023) cautions against treating computers as inherently superior analytical agents.
What AI does well
Speed
Pattern recognition
Data processing
What anthropology adds
Meaning
Reflexivity
Thick description
The appropriate model is therefore not technological substitution but responsible human–AI collaboration.
Way Forward
The productive integration of AI into anthropology requires methodological rigour, ethical reflexivity and interdisciplinary collaboration.
Computational side
- Transparent methodologies
- Appropriate model selection
- Data quality control
- Reproducible research
- Explainable AI
Anthropological side
- Ethnographic context
- Thick description
- Participant relationships
- Reflexivity
- Ethical judgement
Robust ethnography of AI requires:
- Committed fieldwork
- Trusting relationships with participants
- Attention to subtle and ambiguous data that technological systems may overlook
How to Structure This as a 20-Marker
A strong Paper I answer should not become a generic essay on Artificial Intelligence. Keep the answer explicitly anthropological throughout.
AI may identify the pattern, but anthropology explains its cultural meaning.
Conclusion
AI represents a nascent but rapidly expanding intersection with anthropology. Its applications extend across computational ethnography, cultural-data processing, archaeology, paleoanthropology and the emerging anthropology of AI.
However, methodological rigour, ethical reflexivity and interdisciplinary collaboration remain essential for its productive integration.
The future is not AI versus Anthropology, but AI with Anthropology.
Key Scholars & Studies to Quote
- Calder, J. et al. (2022). Use and Misuse of Machine Learning in Anthropology. IEEE BITS the Information Theory Magazine, 2, 102–115.
- Munk, A., Olesen, A. G. & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9.
- Pedersen, M. (2023). Editorial introduction: Towards a machinic anthropology. Big Data & Society, 10.
- Banerji, R. (2023). Artificial Intelligence, Humanness, and Nonverbal Sociality. Anthropology in Action.
- Sapignoli, M. (2021). Anthropology and the AI-Turn in Global Governance. AJIL Unbound, 115, 294–298.
- Tenzer, M. et al. (2024). Debating AI in Archaeology: Applications, implications, and ethical considerations. Internet Archaeology, 67.
- Van Voorst, R. & Ahlin, T. (2024). Key points for an ethnography of AI: an approach towards crucial data. Humanities and Social Sciences Communications, 11.
- Roe, J. (2025). Generative AI as Cultural Artifact: Applying Anthropological Methods to AI Literacy. Postdigital Science and Education, 7, 1107–1124.
- Longkumer, T. (2025). Generative AI in Anthropology: Redefining Fieldwork, Textualisation, and Collaborative Knowledge Production. Teaching Anthropology.
- Xu, J. & Hernandez, J. (2025). Reading children’s moral dramas in anthropological fieldnotes: A human–AI hybrid approach. Cambridge Forum on AI: Culture and Society.
- Keane, W. & Nakassis, C. (2026). Towards a linguistic anthropology of AI. Journal of the Royal Anthropological Institute.
- Srivastava, S. (2026). Anthropology of Artificial Intelligence (AI): Making sense of machines. International Journal of Modern Anthropology.
- Artz, M. (2026). Multi-Agent Ethnography: Post-Conventional Anthropological Practice Through Human–AI Collaboration. Anthropological Forum.
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