领域：学术论文 · 应用学科

1. Introduction
Digital technology has drastically changed the possibilities for the curation and display of cultural heritage collections (CHCs). The physical and conceptual boundaries of such collections continue to expand, creating new opportunities for audiences to access and to engage with artefacts and cultural heritage [1]. During cultural heritage collection digitalisation processes the nuances of past heritage contexts need to be considered to ensure that cultures and diverse social groups are presented in an inclusive manner [2]. Heritage institutions traditionally use methods such as cataloguing and labelling to describe artefacts and to communicate such histories and cultures to the public. The fact that many collections were established through ‘finds’, excavations, expeditions, and bought or seized by colonisers means that narratives related to colonisation and oppression are inevitably part of analogue cultural records even if they are not made explicit within them. Cultural heritage is increasingly negotiated as a past practice that is (re)constructed in the present [3] (p. 3), [4,5] (pp. 32, 165), [6] (pp. 4–8). Different dimensions of CHCs such as acquisition histories, museum history, ownership, location, the items themselves, and curatorial guidance are all intertwined in creating an interactive system between people and information [7]. Critical heritage studies examine the nexus of people, heritage, and societal power in its challenge to conventional heritage discourses [3] (p. 281), [8] (p. 4). Heritage is thus a process ‘understood as being produced through socio-political processes reflecting society’s power structures’ [9] (p. 569).
In digitising CHCs, the question-and-answer protocol of new technologies such as ChatGPT or the production of synthetic images with DALL-E, MidJourney, or Stable Diffusion immediately creates a situation in which human and machine exist in a cognitively productive relationship; the human describes and the machine renders. Generative AI, also known as GenAI or GAI, is an artificial intelligence technology that can generate text, images, or other data using generative models, often in response to prompts. It learns the patterns and structure of input training data to generate new data with similar characteristics. GenAI and the synthetic data it produces has been examined from a number of perspectives. The aesthetics of AI and its impact on visual cultural practices have been extensively discussed [10,11]. Understanding such computationally aided creativity, there is a need for a deeper investigation of the socio-material complexity in implementing GenAI for cultural dissemination [12,13].
The research question that underpins this paper is whether and how a machine can interpret and classify human memory and its artefacts in retrospect in an inclusive manner. In this, we recognise that compromises have to be reached between historical fidelity and inclusivity; we think that this requires careful annotation to explicate diverse positions over time regarding particular subject matters but also that it is paramount to make heritage collections relevant to contemporary audiences [14]. Given the inevitable presence of bias in CHCs and in their digitised versions [15,16] (pp. 607–640), [17] (pp. 815–825), this article aims to discuss the challenges that automation brings as well as provide solutions from beyond the cultural heritage sector. CHCs are normally quite diverse unless they are following some metadata standards as digitised historical collections are the result of legacy digitalisation. Further, there is a lack of interconnectivity/interoperability of digitised collections: not everything is online, or well annotated, or using the same software, and that may be picked via a GenAI or an aggregator such as, for example, Google Arts and Culture.
3. Results and Discussion
3.1. What Is Bias and How Does It Leak into Heritage Datasets?
Bias as a concept is accompanied by ideas of prejudice, unfairness, distortion, and violation, including a systematic distortion of a statistical result due to a factor not allowed for in its derivation [18]. Bias may be as simple as an excluding description related to issues of race and ethnicity, age, gender, LGBTQIA+ communities, and ability. The use of particular data such as post codes in the construction of algorithms can end up amplifying existing skewing such as where supposedly credit-worthy citizens reside, with detrimental impacts on those living elsewhere [19]. All CHCs involve selection, a form of bias in itself. Such selection is frequently accompanied by outdated descriptions of their artefacts that entail inclusion of some segments of society and exclusion of others, conforming to descriptions of a world very different from the contemporary. Dominant historical, national narratives, and organisational legacies dictate what may be included and articulated in a collection [3,20]. While technology can, at least in theory, revolutionise how we understand the human contexts that CHCs carry and CHCs’ ‘democratisation’ [1,21], practice proves otherwise with the risk of carrying through biases of a not-so-distant past to the present and hence the future [2,22,23]. As recently discussed in relation to newspaper archival collections, ‘bias exists prior to any sampling. . .unbiased data—even as an idea—is essentially ahistorical data’ [24] (p. 5).
Research into digital cultural data demonstrates how bias transitions from collections to datasets and then to platforms. AI can amplify bias and hinder effective AI implemen- tation due to a lack of well-annotated datasets and structured metadata in CHCs. Biases within museum collections can manifest in datasets, databases, and aggregators that in- creasingly employ AI technologies such as machine learning [16,25]. Bias in CHCs is then transferred, and it entails issues of digital cultural colonialism and otherness, reflecting tensions between contrasting structures such as European/western versus other, North versus South, and centre versus periphery [2,26–29]. This also extends to gender. Kizhner et al. (2021) examine how bias in the cultural heritage platform Google Arts and Culture is amplified with AI noting that the choices behind digitisation, publication, aggregation, and promotion often obscure institutional, social, and political circumscriptions. These perpetuate the status quo at scale [16]. Kizhner et al. advocate making these epistemic choices transparent, documented, and interpretable. Davis et al. (2021) succinctly state that algorithms are animated by data, data come from people, people make up society, and society is unequal. Davis et al. (2021) [30] discuss algorithmic reparation and intersection- ality as frameworks to combat structural inequalities reflected and amplified by machine learning outcomes.
In computer vision, too, biases related to digital cultural colonialism and dominant epistemologies persist, leading to biased knowledge representations [31,32]. To avoid merely replicating biases, AI technology must evolve to embrace complex, non-binary, and non-dominant interpretations. The contribution of humanities expertise in generative AI platforms is at best unclear. This can lead to biased interpretations and classifications. Critical perspectives from the humanities and social sciences play a vital role in highlighting these issues relevant to more inclusive and equitable AI development practices. These perspectives emphasise the need for ethical AI development (see [33]) that addresses racial and gender discrimination, among other socio-ethical concerns.
Bias, especially racial and gender bias, extends across both technical and epistemo- logical domains, with the gender binary serving as a deeply racialised tool of colonial control. The concept of auto-essentialisation, recently introduced [34], describes how automated technologies reinforce identity distinctions rooted in colonial practices. The concept of auto-essentialisation is explored through historical gender practices, particularly the establishment of the European gender binary via 19th- and 20th-century disciplines such as sexology, physiognomy, and phrenology. These historical practices are viewed as predecessors to today’s automated facial analysis technologies in computer vision. This connection underscores the necessity for a critical reassessment of AI/ML applications in image recognition, as they may represent modern iterations of longstanding technologically mediated ideologies [13,34].
Bias might be mitigated by the enhanced interconnectivity and interoperability of digitised collections through collections ‘speaking to’ each other and correcting misattri- butions, etc. This is particularly important where one deals with rare objects and small special collections, not least if they are located in countries such as Sweden with relatively few collections that cannot provide large datasets to train AI on and are therefore prone to acquire software off-the-peg and not necessarily trained on relevant data. However, calls for such interconnectivity and interoperability which require transnational cooperation are still recent and require political will (see e.g., [35]) and negotiated resource provision.
3.2. GenAI: An Illustration of Biased Synthesis
Wasielewski (2023b) [36] examines the challenges faced by GenAI text-to-image gen- erators such as DALL·E and Stable Diffusion, focusing on their struggles with hand rep- resentation and object counting. While these tools have democratised AI-driven image creation, leading to a surge in creative outputs, they also exhibit significant limitations because they are mechanistic in their depiction of the objects, relying on pattern replication rather than contextual knowledge. This results in images that may appear superficially correct but lack nuanced understanding. The rise of generative AI models like ChatGPT and DALL-E has captured the public imagination; cultural and creative sectors increasingly turn to predictive models for analysing and categorising their materials [37].
The opportunities GenAI affords are significantly structured by the CH sector that underlies them. As Griffin et al. (2023) have shown in the context of Sweden, a geographi- cally large country with a small population (around 10.5 million) and a correspondingly small CH sector that is also quite fragmented, factors such as limited budgets, lack of AI expertise among CH staff, lack of professional mobility and of continuing professional training among CH staff, small collections, and no overarching national policy on the matter, can lead to scenarios where these factors are replayed in how AI is engaged with. This means that individual CHCs may acquire off-the-peg software solutions not trained on the data they are actually applied to or solutions that also lack interconnectivity and interoperability with software and systems in ‘sister’ CHCs, or they may simply not (be able to) afford themselves of what AI and GenAI have to offer, thus isolating those CHCs both nationally and internationally.
The interconnectivity and interoperability of heritage datasets significantly aid AI implementation in the cultural heritage sector by enhancing data access, integration, and analysis capabilities. These characteristics enable AI systems to cross-reference information across multiple collections and institutions, providing a more comprehensive view of cultural heritage. For instance, AI can link artifacts from various museums to reconstruct historical contexts or identify patterns across diverse collections. Interoperability also facilitates the standardisation of catalogue data, making it easier for AI to process and understand information from different sources. This standardisation improves searchability and compatibility with new technologies, as demonstrated by the National Museum of the Royal Navy and the University of Southampton’s pilot project on standardising catalogue data for image collections (see for example https://www.heritagefund.org.uk/about/ insight/research/artificial-intelligence-digital-heritage-leadership-briefing, accessed on 28 October 2024). Interoperable datasets provide AI systems with richer, more diverse training data, leading to improved accuracy and better generalisation across different types of cultural heritage materials and contexts. This enhanced performance is crucial for developing robust AI applications in the heritage sector. Furthermore, interconnectivity and interoperability support collaborative AI implementation by enabling knowledge sharing and facilitating multi-institutional projects (see [38] for practical solutions for enhancing interconnectivity).
Cultural heritage institutions can share expertise, resources, and best practices more easily when working with compatible datasets, potentially leading to groundbreaking discoveries or innovative applications. Many cultural heritage institutions face resource constraints when implementing AI. Interoperability helps address this challenge by al- lowing institutions to pool resources and share AI tools, expertise, and computational resources. This collaboration makes AI implementation more accessible to smaller organi- sations and reduces duplication of effort, as standardised, interoperable datasets prevent institutions from having to reinvent the wheel when implementing AI solutions. Inter- connected and interoperable datasets also enhance the discoverability and accessibility of cultural heritage materials through AI-powered tools. AI systems can leverage these datasets to provide more sophisticated search capabilities across multiple collections and in- stitutions, as well as offer more accurate and personalised recommendations to researchers and visitors. This improved functionality enhances user experience and engagement with cultural heritage materials.

翻译考点：
考点1. cultural heritage collections (CHCs)必须译为：文化遗产藏品
考点2. expeditions推荐译为：探险
考点3. gender binary必须译为：性别二元论
考点4. auto-essentialisation必须译为：自动本质化
考点5. ‘sister’ CHCs必须译为：姊妹文化遗产藏品
考点6. Interoperable datasets推荐译为：互操性的数据集/可互操作的数据集
考点7. reinvent the wheel推荐译为：重复性工作