The relationship between culture and AI is bidirectional, with AI models being increasingly used to analyze culture while shaping our understanding of it. To overcome the tension that arises from these models being trained on collections of cultural artifacts that may not always correctly encode expressions of culture, four concepts must be incorporated into AI research to do justice to the complexity of human culture: subjectivity, scalability, contextuality, and temporality. Subjectivity refers to the individual nature of cultural experiences and interpretations; scalability involves considering different scales at which cultural phenomena occur; contextuality emphasizes the importance of situating cultural artifacts within their historical and social contexts; and temporality recognizes the crucial role time plays in shaping cultural meaning formation. Working with archival data requires expertise in determining what is meaningful beyond metadata such as where, when, and by whom an artifact originated. Computational models must be developed that can contextualize data while distinguishing between incidental and meaningful information. Additionally, capturing how meaning evolves over time through longitudinal data requires encoding how meanings are retained or forgotten over time. In conclusion, this paper invites productive engagement and collaboration between humanistic scholars and AI researchers to develop more accurate models that better capture the intricacies of culture while avoiding issues such as bias.
- The relationship between culture and AI is bidirectional
- AI models are used to analyze culture while shaping our understanding of it
- Four concepts must be incorporated into AI research to do justice to the complexity of human culture:
- Subjectivity (individual nature of cultural experiences and interpretations)
- Scalability (considering different scales at which cultural phenomena occur)
- Contextuality (situating cultural artifacts within their historical and social contexts)
- Temporality (recognizing the crucial role time plays in shaping cultural meaning formation)
- Working with archival data requires expertise in determining meaningful information beyond metadata
- Computational models must contextualize data while distinguishing between incidental and meaningful information
- Longitudinal data is required to capture how meaning evolves over time by encoding how meanings are retained or forgotten over time
- Collaboration between humanistic scholars and AI researchers can develop more accurate models that better capture the intricacies of culture while avoiding issues such as bias.
Culture and AI (Artificial Intelligence) have a relationship where they both affect each other. AI is used to study culture, but it also shapes our understanding of it. There are four important ideas that need to be included in AI research to understand human culture better: subjectivity, scalability, contextuality, and temporality. When working with old data, experts need to find the essential information beyond just basic facts. Computational models must understand the context of data while separating what is important from what is not. Longitudinal data means studying how meanings change over time by looking at how people remember or forget things over time. Working together with humanistic scholars can help create more accurate models that avoid bias and capture the complexities of culture better.
Definitions
- Culture: The beliefs, behaviors, and customs shared by a group of people.
- Artificial Intelligence (AI): The ability for machines or computers to perform tasks that would typically require human intelligence.
- Subjectivity: How personal experiences shape an individual's interpretation of something.
- Scalability: Considering different scales at which cultural phenomena occur means taking into account different levels of detail or size.
- Contextuality: Understanding cultural artifacts within their historical and social contexts means considering the circumstances surrounding them when they were created or used.
- Temporality: Recognizing the role time plays in shaping cultural meaning formation means understanding how meanings change over time.
- Archival data: Information stored in archives such as libraries or museums that may be old but still relevant for research
Exploring the Relationship Between Culture and AI
AI is increasingly being used to analyze culture, while at the same time shaping our understanding of it. This creates a tension between AI models that are trained on collections of cultural artifacts which may not always accurately encode expressions of culture. In order to overcome this tension and do justice to the complexity of human culture, four concepts must be incorporated into AI research: subjectivity, scalability, contextuality, and temporality.
Subjectivity
Subjectivity refers to the individual nature of cultural experiences and interpretations. It acknowledges that different people can have different interpretations of an artifact or event based on their own personal experiences and perspectives. For example, two people could look at a painting from two completely different angles – one person might see it as a representation of beauty while another might view it as an expression of despair. AI models need to take into account these subjective interpretations in order for them to be accurate representations of culture.
Scalability
Scalability involves considering different scales at which cultural phenomena occur – from global trends down to local customs or even individual preferences. A model that only takes into account large-scale trends would miss out on important nuances that exist within smaller communities or groups. On the other hand, focusing too much on small-scale details can lead to oversimplification when trying to make sense of larger patterns or movements in culture over time.
Contextuality
Contextuality emphasizes the importance of situating cultural artifacts within their historical and social contexts in order for them to be properly understood by AI models. Historical events such as wars or political upheavals can have profound effects on how cultures evolve over time; likewise, changes in technology can also shape how we interact with each other and express ourselves through art or literature. By taking into account contextual factors such as these, AI models will be able to better capture how meaning is formed within cultures over time.
Temporality
Finally, temporality recognizes the crucial role time plays in shaping cultural meaning formation – both across generations (such as passing down traditions) as well as within individuals (as memories fade away). Capturing how meanings evolve over time requires encoding how meanings are retained or forgotten over time – something which traditional archival data may not always provide enough information about due its focus on metadata such as where/when/by whom an artifact originated rather than what it actually means for those who experience it firsthand today (or experienced it in years past).
Conclusion
In conclusion, this paper invites productive engagement between humanistic scholars and AI researchers so that more accurate models can be developed which better capture the intricacies of culture while avoiding issues such as bias. By incorporating concepts like subjectivity, scalability, contextuality, and temporality into their research designs they will be able create more nuanced representations which reflect real-world complexities found in human cultures around the world today – something essential for any meaningful analysis going forward!
