Bringing Human Centricity to Generative AI

Leveraging Post-Structural Anthropology for Consumer Research

Artificial Intelligence (AI) has taken over almost every industry and domain, including research. Generative AI, in particular, has revolutionized the field of consumer research, making it faster and more efficient than ever before. However, relying solely on generative AI for research purposes does not necessarily guarantee human-centricity in the process. To achieve true human-centricity, we need to look beyond the technical aspects of AI and tap into the power of post-structural anthropology.

Post-structuralism is a philosophical movement that started in the 1950s and gained momentum in the 1960s and 1970s. It is associated with the works of the French philosopher Jacques Derrida, who argued that language and knowledge are inherently unstable and cannot be relied on as objective or neutral. According to Derrida, language is a system of signs and symbols that have multiple meanings and interpretations, depending on the context in which they are used. This perspective is essential to understanding how language is used in different contexts and how it influences human behavior.

Social Anthropology or Cultural Anthropology specifically deals with the study of human societies and cultures and their development. Anthropologists use contextual language as a critical source of input in this type of study. This is where post-structuralism comes into play - the study of language in context to decode meaning.

Traditionally, the job of an anthropologist is to make sense of the contextual language around a topic of interest. With years of experience and data on how an anthropologist interprets language patterns in context to identify human needs and wants, we now have a training ground for generative AI. By teaching AI how to do what humans do, we can make generative AI proficient at human-centric analysis.

In other words, post-structural anthropology can help us develop a map of contextual language around a particular topic. This map can then be used by a trained generative AI model to interpret and extract meaning. The result is a more human-centric approach to research, where AI can process vast amounts of data and generate insights grounded in the human experience. By leveraging the power of post-structural anthropology, we can decode and achieve a pure human-centric perspective.

It is still early days, and there is more to learn and discover in this field. As anthropologists working with contextual (big) data, generative AI is a blessing and an opportunity to expand our mission to bring true human-centric thinking to all organizations.

PS: I’m doing a webinar on this topic with Greenbook on May 18. Link 👇.