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Picture this. You are researching a topic of interest. You receive article suggestions remarkably relevant to your search as you browse websites like Elicit. Later, while in the field taking photos, videos, and audio recordings on your smartphone, the software automatically adjusts the settings to capture everything with the highest possible quality. Returning to your office, you use an automated service like Otter.ai to accurately transcribe the interviews. Then, as you begin writing up your findings, Grammarly presents you with intelligent writing suggestions.
At every step, you have used artificial intelligence (AI), the field of computer science focused on developing systems that can perform tasks that typically require human intelligence. But AI is not just a set of tools for anthropologists; it is the discipline’s future. AI will transform anthropology in unimaginable ways, challenging our theories and methods and pushing the frontier of what we thought possible.
The future of anthropology is AI. We should embrace it and help shape its development, or risk being left behind.
As an anthropologist, designer, and technologist working in new product development, here are my 10 speculative predictions for how AI will disrupt our discipline in the coming years.
1. All five fields will be disrupted: AI will profoundly impact all branches of anthropology, including applied anthropology. Its integration with archaeology will enable enhanced artifact analysis, reconstruction of ancient environments, and the identification of undiscovered sites. Biological anthropology will benefit from accelerated complex genetic data analysis and reconstructions of early humans. Meanwhile, linguistic anthropology will realize new opportunities for studying, reclaiming, and teaching endangered languages. It will also help cultural and applied anthropologists reveal hidden cultural patterns and spot emerging trends, leading to a better understanding of our past and more attuned interventions in the future.
2. AI as a collaborative partner: AI will soon be foundational to our work practice. We will engage AIs in discussions and creative brainstorming, leveraging their unique strengths to complement and scale our abilities. With the help of AI, anthropologists will be able to gain broader perspectives, leading to richer insights and increased problem-solving abilities. For the savvy researcher, AI will not replace the human anthropologist but will provide a tool to enrich every aspect of the anthropological process.
3. Transforming ethnography: AI is poised to revolutionize ethnography by fundamentally altering how researchers conduct their work. AI-assisted ethnography will support researchers in collecting, analyzing, and interpreting data at scale. Techniques such as web scraping, natural language processing (NLP), and computer vision will make this possible and unveil new insights and patterns. With the added data and complementary analysis, anthropologists will enhance their practice through new modes of triangulation, ultimately leading to more robust interpretations.
4. Enhancing public engagement: AI-generated visualizations, videos, interactive data representations, and immersive digital experiences will help anthropologists to convey complex research findings and narratives in appealing, relatable, and accessible ways to wider audiences. This enhanced multimodal storytelling will capture the attention of more diverse groups and make it more likely that the general public will consume the information.
We will engage AIs in discussions and creative brainstorming, leveraging their unique strengths to complement and scale our abilities.
5. Automated digital ethnography (ADE): ADE enhances traditional ethnographic methods by automating the research process within digital field sites. By deploying programmed ADE agents, researchers can tap into the vast amounts of unstructured data available on the internet, such as social media posts, forum discussions, and blog entries. As these agents continuously collect and analyze data in real time, they act as ever-present partners in the field, providing researchers with valuable and up-to-date insights. This real-time engagement will assist anthropologists in quickly identifying emergent human behavior and cultural patterns, leading to more agile and timely research.
6. AI multimodal analysis: Integrating AI into ethnographic research will revolutionize the scale at which anthropologists conduct multimodal analysis. Handling multimodal data can be extremely time consuming, often requiring researchers to meticulously sort through and piece together various forms of information. But with AI we can automate the process of sifting through and analyzing diverse data types, such as text, images, audio, and video, significantly reducing the time and effort required. This efficiency will allow anthropologists to focus on more complex and nuanced aspects of the research process.
7. Anthropology-specific AI: With the increasing integration of AI into anthropology, we can expect to see specialized tools designed to address the unique challenges and complexities inherent in studying the human experience, going beyond the capabilities of general AI models. One example may be a fine-tuned large language model (LLM) that extends general knowledge LLMs such as OpenAI’s GPT-4. LLMs are AI models trained on large bodies of text, using statistical methods to make predictions and generate outputs. With these anthro-specific tools, anthropologists will benefit from more contextually relevant insights than the current crop of models can offer.
8. Advancing research with knowledge graphs: Anthropological knowledge graphs (AKGs) will revolutionize how anthropologists store and access information by creating specialized knowledge repositories that interlink entities—people, organizations, concepts, historical events, methods, disciplines, publications, and more—within a contextual framework. As these AKGs develop, they will empower researchers to better comprehend human social, cultural, biological, and linguistic diversity, paving the way for a web-scale model that accurately represents the complexity of human experience. Creating such graphs will likely necessitate a mixed-methods approach, combining the power of anthropologically fine-tuned LLMs with the precision of ethnographic semantic data modelling. With such knowledge graphs, our field can inch closer to machine knowing instead of being grounded in machine learning-based AI approaches.
9. New models of anthropological entrepreneurship: Historically, anthropological entrepreneurs typically set up research practices. While that is not going to change, and it may even be accelerated by the incorporation of AI, the new model of anthropological entrepreneurship will take the form of founding tech companies that combine the wisdom, empathy, and ethics of anthropology with computer and data science to innovative businesses models and products. By bringing an anthropological perspective to the technology industry, these entrepreneurs can ensure that AI applications are developed with a deep understanding of the complexities of human societies and the potential consequences of technology. This will create products and services that prioritize ethical considerations, minimize adverse impacts, and contribute positively to global communities.
10. Productize anthropology: My vision for the future of anthropological entrepreneurship includes an AaaS platform. I imagine a subscription-based service that is accessible to anyone, regardless of their background. This platform would harness anthropology-specific AI tools for data collection, analysis, and insights generation, democratizing anthropological knowledge and promoting innovation. By making anthropological insights widely accessible, an anthropology as a service (AaaS) platform could help to propel the discipline forward, ensuring its ongoing relevance and impact in our increasingly digitized world.
As we embrace the exciting possibilities that digital innovation brings to anthropology, we must remain vigilant and committed to addressing the ethical challenges that come along with it. By critically examining issues such as bias, fairness, transparency, privacy, and the potential impact on job markets, we can work towards a future where AI is a force for good within our discipline.
We are uniquely positioned to contribute to these conversations and ensure that anthropological insights inform the evolution of AI technologies.
Illustrator bio: An Pan is a multimedia designer, illustrator, and culture lover. He is currently a designer-accessory to Chinese consumerism but works with a big dream of decolonizing design. He enjoys traveling and doll collecting.