Programa del congreso

There is tremendous promise in using artificial intellegence, and specifically machine learning techniques to help curators, collections managers and users to understand, use, steward and preserve web archives. Libraries, archives, museums and other public cultural heritage organizations who manage web archives have shared challenges in operationalizing AI technologies and unique requirements for managing digital heritage collections at a very large scale. Through research, experimentation and collaboration the LC Labs team has developed a set of tools to document, analyze, prioritize and assess AI technologies in a LAM context. This framework is in draft form and in need of additional use cases and perspectives, especially web archives use cases. The facilitators will introduce the framework and ask participants to use the proposed framework to evaluate their own proposed or in process ML or AI use case that increases understanding of and access to web archivies.

Sharing the framework elements, gathering feedback, and documenting web archives use cases are the goals of the workshop.
Sample Elements and Prompts from the framework:
- Organizational Profile: How will or does your organization want to use AI or Machine learning?

- Define the Problem you are trying to solve.

- Write a user story about the AI/ML task or system your are planning/doing

- Risks and Benefits: What are the benefits and risks to users, staff and the organization when an AI/ML technology is/will be used?

- What systems or policies will/do the AI/ML task or system impact or touch?

- What are the limitations of future use of any training, target, validation or derived data?
- Data Processing Plan: What documentation are/will you require when using AI or ML technologies - What existing open source or commercial platforms offer
pathways into use of AI?

- What are the success metrics and measures for the AI/ML task?

- What are the quality benchmarks for the AI/ML output?

- What could come next?