Thammasat University students interested in art, tourism, media and communications studies, museology, and related subjects may find a new book useful.
AI in Museums: Reflections, Perspectives and Applications is an Open Access book available for free download at this link:
https://www.transcript-publishing.com/978-3-8376-6710-3/
The Thammasat University Library collection includes several books about different aspects of artificial intelligence (AI) and its applications in museums.
The publisher’s description notes:
Artificial intelligence is becoming an increasingly important topic in the cultural sector. While museums have long focused on building digital object databases, the existing data can now become a field of application for machine learning, deep learning and foundation model approaches. This goes hand in hand with new artistic practices, curation tools, visitor analytics, chatbots, automatic translations and tailor-made text generation. With a decidedly interdisciplinary approach, the volume brings together a wide range of critical reflections, practical perspectives and concrete applications of artificial intelligence in museums, and provides an overview of the current state of the debate.
In 2022 in the United Kingdom (UK), the Museums + Artificial Intelligence Network published AI: A Museum Planning Toolkit, a free resource designed to help non-specialists get started.
The text, available online for free download, notes:
A number of AI tools can be used for free, or cheaply (often through a freemium model), these range from IBM Watson a natural language processing tool that allows you to analyse vast amounts of text based data, such as visitor feedback, quickly and cheaply.
Or, machine vision tools such as Google Cloud Vision API, or Microsoft Azure which allow you to create metadata tags for images, something that can be useful when it comes to managing vast digitised collections. These ‘off the shelf’ tools are likely to become more sophisticated in the coming years, and as such more commonly used. However in order for museums to engage with such technologies in a manner that aligns with their mission, they need to be conscious of quality assurance and bias management.
Quality Assurance Process
– Human Augmentation
When using any computational decision making tool it is important to have human quality assurance processes in place. Exploring what this process may look like, will help you to reflect upon the data created through AI tools, and how that data will be used internally, but also externally. Will the data be visitor facing? What are the implications of creating visitor facing data?
Bias management
Machines much like museums are inherently biased, as such whilst machine learning tools may provide valuable metadata for your online collection it could also create bias squared (museum bias x machine bias).
As such understanding the training data used to teach the machine, and the algorithms used to make decisions are crucial to ensure the integrity of any application of these technologies within museums.
Brandwashing
Technology companies are keen to work with museums, particularly large museums with strong national and international brands. This can provide museums with access to cutting edge technology, custom built solutions (which can be much more effective than off the shelf tools), and support in kind from technology professionals.
However, museums need to think about such partnerships in the same way that they do fundraising. What are the ethical implications of brand affiliation with a specific tech company?
How does that relationship align with the mission of the museum?
What are the potential unintended consequences of such a partnership?
Critical Technology Discourse
While some issues raised in this toolkit may sound problematic, these technologies are increasingly being used in wider society. Museums have an opportunity to critically engage with these technologies and the impact they have, by being open and accountable about what technologies they are using, and through public programs and contemporary collecting to develop visitor literacy around AI and Machine Learning Technologies.
The Photographers Gallery in London has a strong critical technology discourse theme across much of its public programming, while the V&A has begun to collect AI technologies and associated art, such as Anatomy of an AI System, by Kate Crawford and Vladan Joler (2018).
The link between what happens in the digital team, public programs, and collecting could become more reflective and engaged through organisation wide transparency, dialogue and development.
Founded in 1869, the American Museum of Natural History (AMNH) is the largest natural history museum in the world. The museum is located on the Upper West Side in New York City, and welcomes tourists visiting the city, along with local residents, educators, researchers, and school groups. The museum hosts approximately 5 million visitors annually.
Visitor surveys and comments analysis
The AMNH is reviewed by visitors on a range of different online platforms during and after their visit to the museum, one of the challenges for the museum is analysing these reviews in a timely and cost effective manner.
The museum emails a survey two days after a visit (using the contact details provided when the visitor was purchasing an admission ticket). The survey asks a quantitative question
‘How likely are you to recommend the American Museum of Natural History?’ and visitors are invited to select a numerical value between 1 and 10. The data created in response to this question is straightforward to analyse on mass, however, the follow up question which generates a qualitative response is more challenging: “What is the most important reason for your score?’’ As a cost-effective way to explore if NLP could provide new insights into visitor feedback, AMNH decided to use an off the shelf Natural Language Processing (NLP) and sentiment analysis service created by a commercial vendor.
IBM Watson is a platform that allows users to identify and analyse a range of concepts, categories, relationships, emotion and sentiment within vast quantities of qualitative data. Its NLP suite of services can be used to generate sentiment analysis reporting on a specific subset of words, or entities. For example, AMNH used commonly mentioned words in NPS survey comments to more closely examine the sentiment associated with those words.
The above sentiment and entity analysis is a proof of concept, developed with six months of NPS survey comments data. The free tier of IBM Watson’s sentiment analysis service was used to analyze these entities and associated sentiment scores.
Google Cloud and TripAdvisor Google’s Cloud Natural Language Processing
API provides a similar service to the IBM Watson NLP sentiment services, which AMNH used to analyze reviews shared on the TripAdvisor platform. Tens of thousands of reviews were used as input data to Google’s service, and sentiment analysis scores and popular named entities were included in the service output.
A general sentiment score, ranging from -1 (very negative) to +1 (very positive) was generated for the top 7 museums (by attendance) in the USA. With a score of 0.5, the National Air and Space Museum and the Natural Gallery of Art scored highest, followed by museums with a score of 0.4: American Museum of Natural History, the Metropolitan Museum of Art, National Museum of American History, National Museum of Natural History, and 9/11 Memorial Museum.
Additionally, AMNH used Google’s NLP service to extract the most popular named entities within TripAdvisor reviews to gain insights into what visitors mentioned most frequently within reviews.
Potential challenges
Whilst AI provides a valuable starting point when it comes to engaging with large amounts of qualitative data, providing appropriate context and interpretation is still the job of humans.
Those working in museums and with visitors can provide a valuable layer of analysis that can add to the validity and operational insights provided by these commercial services.
What can we learn from this case study?
An off the shelf cloud service such as IBM Watson or Google Cloud Natural Language Processing can be a costeffective way to analyse large amounts of qualitative data and extract focused insights on visitor sentiment and aspects of the visitor experience.
(All images courtesy of Wikimedia Commons)