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Please note this is a comparison between Version 1 by Adriaan De Man and Version 2 by Camila Xu.
Artificial Intelligence (AI) in heritage management refers to the use of computational systems for tasks conventionally associated with human intelligence. These range from pattern recognition to prediction and language processing, among other dimensions such as image analysis or decision support, across the cultural and creative industries. AI applications are commonly applied in daily settings, namely for site management and visitor experience enhancement. Beyond its multiple technical functions, AI acts as a mediating infrastructure that influences how heritage knowledge is produced. Consequently, AI raises epistemological, ontological, ethical, and political questions for the future of heritage management. This entry takes into account the interconnected concepts of authenticity, authority, representation, and participation.
- heritage theory
- digital culture
- posthumanism
- cultural preservation
- synthetic intellect
Artificial Intelligence is rapidly affecting the process through which cultural assets are documented and shared with the public, which has become a topic of intense scholarly interest [1][2][3][1,2,3]. The idea of managing cultural resources evolved from an originally conservation-centric notion to a broad interdisciplinary field [4] that brings together intangible expressions, community outreach, economic representation, and many other social dimensions [5]. This progression is parallel to an academic movement that takes heritage as constructed and everchanging, rather than a passively inherited element of the past [6].
The term “community” used in this entry refers to the entirety of stakeholder groups involved in the production and consumption of cultural heritage. Within the wide collective of local residents, one may look specifically at source communities (e.g., indigenous peoples, descendant communities, ethnic minorities, etc. ) in a given functional relation with museum or site audiences, practitioners, and heritage professionals of different types. Such groups express diverse interests and values, forcing community participation into a continuous negotiation on concepts such as meaning and ownership, hence the interpretation of heritage resources.
A dynamic perception of heritage allows for integrated possibilities in terms of AI, namely through complex data acquisition and processing, from sites to cultural performances and archives [7][8][9][7,8,9]. Yet its qualitative nature also inhibits mere technological deployments and requires human investment in the production, validation, and consumption of heritage. Communities, both local and visiting, provide a cultural meaning to components of everyday life, and this is not spontaneously captured by AI solutions alone [10].
The underlying paradigms and their ongoing shifts are conceptually analyzed below. A growing use of AI in heritage management raises important questions about how cultural knowledge is mediated. In short, how is cultural knowledge to be processed, and what are the ethical and socio-political implications of AI instruments in heritage management? Progressively integrating AI into cultural heritage domains also means that, apart from operational advantages, the very conceptualization of collective memory is transformed [11]. Contemporary scholarship acknowledges that mediation through technology is moving beyond a function of interpretation and transforms heritage [12][13][12,13]. In other words, historical consciousness becomes partly shaped by algorithms that manipulate cultural data in ways that remain opaque to end users.
As a result, societal memory is processed mediated through predictive digital systems. Most daily life environments are unproblematically data-driven, but tensions do arise when communities are forced to negotiate between their very specific emotional memory and representations optimized by AI [14]. The challenge lies not in categorizing and optimizing heritage preservation with computational support [15][16][15,16], but in determining the sort of cultural translation that ensures convenient heritage representations. These originate in experiential, non-digital meanings and their dataset encoding results in abstractions contrary to perceived authenticity.
With these premises in mind, AI and heritage management are examined below through a practical and theoretical lens. The discussion takes heritage as a socially constructed process before exploring the several implications of AI on cultural authority, which in turn affects knowledge production and interpretation. This part is followed by an overview of key AI applications in heritage management: computer vision, machine learning, natural language processing, and digital reconstruction. The entry then considers the ramifications for responsible governance.