  There is a bidirectional relationship between culture and AI; AI models are
increasingly used to analyse culture, thereby shaping our understanding of
culture. On the other hand, the models are trained on collections of cultural
artifacts thereby implicitly, and not always correctly, encoding expressions of
culture. This creates a tension that both limits the use of AI for analysing
culture and leads to problems in AI with respect to cultural complex issues
such as bias.
  One approach to overcome this tension is to more extensively take into
account the intricacies and complexities of culture. We structure our
discussion using four concepts that guide humanistic inquiry into culture:
subjectivity, scalability, contextuality, and temporality. We focus on these
concepts because they have not yet been sufficiently represented in AI
research. We believe that possible implementations of these aspects into AI
research leads to AI that better captures the complexities of culture. In what
follows, we briefly describe these four concepts and their absence in AI
research. For each concept, we define possible research challenges.
