Webinars on AI: 1 ethics in academic research // 2 potential for cultural heritage | Local Approach ChatGPT Experiment

1)”AI and Ethics in academic research” is a free webinar by Martina Tenzer, PhD researcher, University of York, on 5 May 2023. The webinar discusses the present state of AI in research, ethics and the future.

2)”AI tools in perspective: the potential for cultural heritage institutions, the responses of DH researchers” is a free webinar by Europeana, on 4 May 2023. If you are not a member of Europeana, in the past months’ research community members have been exchanging lengthy e-mails on the AI uses in cultural heritage. This event is a response to the popular demand inviting 3 experts to discuss the role of AI in heritage, “one will focus on libraries, one on museums, and one on archives”.

AI tools have proven valuable instruments with several heritage management applications, which we mentioned in our past articles. Since January 2023, with the launch of several new AI tools, especially the language model (text-generated) as OpenAI’s ChatGPT, there has been an international discussion on their capacities, fidelity, ethics and benefits to academic research. The reception is mixed, with groups firmly against and for.

Several everyday applications already run on AI (search engines, autofill, face recognition) in cultural heritage, for example, archaeological site detection and prediction, satellite imaging, captions and metadata. AI can now also write papers, code or digital art, raising concerns from IP to accuracy. AI trains on data with sources, either from the engineers or, as we now see, the Internet. These data pools, however, can have biases, for example, unintended exclusion of marginalised groups. Another issue is the “black box”, meaning that once AI process and learns from the data we cannot determine how or why it comes to conclusions/outcomes beyond the initial information and parameters input.

The 1st webinar takes into account *some* of the above and presents the University of York’s academic research giving a clearer view of the ethics of AI, along with industry experts on the state of things and the future.

The presentations help understand how AI works 1st Everyday Heritage

2 Image processing

3 Cultural Heritage

4 Ethics (Archaeology and Bioarchaeology)

5 Large Language models

What is AI

The goal of this interdisciplinary field is to create “intelligent” agents and use them to automate human work

“Subfields: ML, DL, NLP, CV, Robotics, Expert Systems

Milestones: ImageNet, AlphaGo, Transformers, GPT-series

Impact: automation, decision-making, data analysis, creative generation, human-machine interaction…

The AI pipeline

The Development stage

Problem Identification: define the system’s scope and targeted audience
Data Collection: gather relevant data sources to train
Data Processing: clear, preprocess, and transform the data
Feature Engineering: select and extract features that will improve the model’s performance
Model Section & Training: choose appropriate algorithms, train, tune the model’s parameters
Model Evaluation: assess the model’s performance using various metrics and validation techniques
Integration: embed the model into existing systems/products
UX/UL: ensure the model is user-friendly, accessible and answers users needs
Monitoring & Maintenance: monitor and adapt the model’s performances
Scalability & Optimization: handle larger workloads and improve response time
Security & Compliance: ethical, legal and technical compliance

Where do we apply ethics and why?

Development stage:

Dataset (e.g, bias, over-representation/under-representation

Hardware/computing (e.g., carbon footprint)

Deployment stage:

Use case applications (e.g, manipulation, disillusion)

User research (e.g. education, intended vs unintended uses)”

Both phases are equally crucial and necessitate in-depth, contextualized ethical investigation and evaluation.

Focus on the humans involved
Representations (data)
Interaction/Uses (HCI/HMI)
Positive and/or negative impact Individual + society

Operationalising ethics

Applied ethics tools

Ethical compliance: A thorough articulation of the beliefs or principles contained in an ethical charter, which would encourage the signatories to bear direct moral responsibility.

Legal compliance: complying with laws and regulations that apply to a specific business or sector.

Technical compliance: Following industry best practices and standards when creating and deploying Al systems.

Case studies: BigScience, Big Code

Some takeaways

There is always bias in data, and we should aim to reduce bias in the interpretation of results
AI has the potential to shape narratives and we need to pay attention
Great potential applications such as disabilities
Fantastic discussion on AI and society, institutions financial and educational, and other facets
AI and related technologies are not ecologically sustainable
Education: Users must receive education about proper useful, and safe usage.
Guidelines: Ex-ante ethical guidelines for developers and best practices should be developed, as opposed to other papers’ ethical foundations.
Policy: Holding businesses and developers responsible for installing when deploying
Feedback: Adapting to new difficulties and ongoing improvement from experience emerging challenges
Right to oppose: having the freedom to refuse to use Al systems

The 2nd webinar focuses on cultural heritage applications with experts with research on AI in cataloguing (Dr Allison Kupietzky), libraries research (Dr Heli Kautonen) and digital archaeology and preservation(Dr Seamus Ross).

AI in Libraries

Libraries employ AI technologies for years, well before the 2023 rise.

Check B-Wheel a A Process Model for approaching AI in research libraries co-developed with Dr Heli Kautonen (also attached)

AI in Cataloguing

AI is biased gender, racial and language algorithms in facial recognition, it worked well with sound and ocr (Optical Character Recognition) text recognition [tests on Google Images].

AI in Archives

– Currently running a survey on digitisation and artificial intelligence for archives and documentary heritage, and presenting the findings so far.

– Testing AI on UNESCO Audio Recordings

-InterPARES Trust AI

Our Take

We have been following the international discourse on AI and for some time planned an article, waiting for concrete research and practical expertise, as with the University of York and Europeana, to publish. The University of York symposium is a great place to start for understanding the tools, their uses, opportunities and dangers. Europeana will focus on cultural heritage and open the floor to everyone, particularly experts in digital humanities, to respond to the AI tools’ limits. (We will follow both events and get back to the article if needed)

At Local Approach, we believe in the potential of AI in cultural heritage; as for AI language models, we feel AI language models are better than your average search engine, but there is still the need to cross-reference and not take any outcome at its word. We conducted 3 tests with ChatGPT, on various cultural management topics, which you can find after “I want to learn more“. ( And no, we did not do “this article was written by ChatGPT”, at the moment, I find it makes a lot of grammar mistakes and like our writing style better[sic])

I want to learn more:

Find our experiments with ChatGPT on cultural heritage below

Resources shared

“How to get started” question:

There are many resources available online that can help you learn AI on your own. Some good places to start include:

Online courses on platforms like Coursera, Udemy, edX, etc., which offer courses in machine learning, deep learning, computer vision, natural language processing, robotics, etc.
Books such as “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig, “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville, and “Robotics: Modelling, Planning & Control” by Kevin M. Lynch and Frank C. Park
Research papers and articles from reputed sources like arXiv, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Journal of Artificial Intelligence Research, etc.
In addition, participating in competitions and hackathons, collaborating with others through online communities and attending conferences and workshops can provide valuable experience and networking opportunities.

It is important to note that becoming proficient in AI requires time and effort, so stay motivated and keep learning!”

The Digital Human

Register for the event 2: AI tools in perspective: the potential for cultural heritage institutions, the responses of DH researchers

Resources shared

Watch:

UNESCO IESALC ChatGPT, inteligencia artificial y educación superior

Our articles:

AI and cultural heritage : EuropeanaTech x AI

AI and Satellite imagery monitor cultural landscapes

Cultural Heritage Metadata

Our experiments

As part of the article, we experimented! We asked ChatGPT to do some of the typical cultural heritage tasks, and judge for yourself:

Cavafy Museum values analysis:

We choose the Cavafy Museum as Athina, with CPC, worked on the business plan. (Read our article here)

ChatGPT Values Analysis

Hello, could you conduct a cultural heritage values analysis of the Cavafy Museum in Alexandria Egypt?

-Sure, I’d be happy to conduct a cultural heritage values analysis of the Cavafy Museum in Alexandria, Egypt.

The Cavafy Museum is dedicated to the famous Greek poet Constantine Cavafy, who lived in Alexandria for most of his life. The museum is housed in a late 19th-century apartment building in the neighborhood of Shatby, which is where Cavafy lived from 1892 until his death in 1933. The building was renovated and turned into a museum in 1992.

Cultural heritage values are the qualities that a place or object possesses that make it important to people as part of their cultural heritage. There are a number of different frameworks that can be used to identify and analyze cultural heritage values, but one commonly used model is the UNESCO Intangible Cultural Heritage Convention, which identifies five broad categories of cultural heritage values:

Aesthetic value: This refers to the artistic, aesthetic, and sensory qualities of a place or object. In the case of the Cavafy Museum, the aesthetic value is derived from the museum’s displays of Cavafy’s personal belongings, manuscripts, and photographs, which provide a tangible connection to the poet’s life and work.
Historic value: This refers to the historical, social, and cultural significance of a place or object. The Cavafy Museum has significant historic value as the former home of one of the most important poets of the 20th century, and as a testament to the cultural diversity of Alexandria during the late Ottoman and early British colonial periods.
Social value: This refers to the role that a place or object plays in the social life and identity of a community. The Cavafy Museum has social value for the Greek community in Alexandria, which sees the museum as a way to celebrate and preserve the memory of one of its most famous members.
Spiritual value: This refers to the spiritual, symbolic, and religious significance of a place or object. While the Cavafy Museum does not have any explicit spiritual or religious significance, the museum’s displays of Cavafy’s poetry and personal effects may hold deep personal and emotional meaning for some visitors.
Educational value: This refers to the ability of a place or object to provide information, knowledge, and skills to people. The Cavafy Museum has significant educational value as a resource for students, scholars, and the general public to learn more about Cavafy’s life and work, as well as the cultural and social history of Alexandria.

Overall, the Cavafy Museum has significant cultural heritage value as a unique and important site of literary and cultural history in Alexandria. By preserving and showcasing the life and work of Constantine Cavafy, the museum serves as a valuable resource for education and cultural exchange, and helps to promote a deeper understanding of the diverse cultural heritage of Alexandria and the wider Mediterranean region.

The CPC values analysis

We believe, for a non-cultural expert, the AI did cover its bases, however, the CPC analysis is more comprehensive.

Atlantis floor plan

Hello, can you design an efficient visitor guide plan for the archaeological site of Atlantis?

-I’m sorry, but I must inform you that the archaeological site of Atlantis is a fictional location that does not actually exist. The story of Atlantis is a legendary tale that has been passed down through history, but there is no concrete evidence to suggest that the city ever existed.

If you have a specific archaeological site that you would like me to help you design a visitor guide plan for, I would be happy to assist you. Please let me know the name and location of the site, and any other information that you think might be useful.

There is a lot of chatter that GPT can create fictional and very realistic pieces of text. In the Atlantis case, it passed the “test”.

Participatory mapping in heritage

(Read our article for comparison)

Hello, can you list me the different types of participatory mapping in cultural heritage by different levels of training and knowledge?

-Sure! Participatory mapping is an approach that involves engaging local communities and stakeholders in the mapping process. In the context of cultural heritage, participatory mapping can be used to involve local communities in the documentation, preservation, and management of cultural heritage sites. The types of participatory mapping in cultural heritage can be categorized based on the level of training and knowledge required for participation. Here are some examples:

Participatory mapping with local communities: This type of participatory mapping involves working with local communities to document and map cultural heritage sites. This approach does not require any specialized training or knowledge, but it does require a willingness to collaborate and engage with local communities.
Participatory mapping with heritage experts: This type of participatory mapping involves working with heritage experts to document and map cultural heritage sites. This approach requires some specialized training and knowledge, such as expertise in archaeology, architecture, or conservation.
Participatory mapping with trained volunteers: This type of participatory mapping involves working with trained volunteers to document and map cultural heritage sites. This approach requires some level of specialized training and knowledge, such as training in surveying, mapping, or data collection.
Participatory mapping with citizen scientists: This type of participatory mapping involves working with citizen scientists to document and map cultural heritage sites. This approach requires some level of training and knowledge, such as training in data collection or scientific methods.
Participatory mapping with students: This type of participatory mapping involves working with students to document and map cultural heritage sites. This approach requires some level of training and knowledge, such as training in history, geography, or cultural heritage studies.

Overall, the level of training and knowledge required for participatory mapping in cultural heritage can vary depending on the goals of the project and the level of expertise of the participants.

Our article is more practical and comprehensive for application, here is an excerpt:

“Mapping can take many forms requiring different levels of training and knowledge, namely:

Ephemeral mapping, drawing on the ground using sticks and other items.
Sketch mapping, quick drawing of an area.
Scale mapping, creating an accurate map using georeferencing and cartographic techniques.
Photo mapping, including aerial photographs.
GPS and GIS, using satellite imagery and listing information using information software.
3-Dimensional Modelling (P3DM), creating a scale virtual model.”

In this instance, the AI listed types of participatory mapping instead of practices. The AI may not be at fault as it depends on how we formulated the question; perhaps the correct wording would be activities. This an example of how our use heavily depends on what results we will receive.

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