Bert Lyons in “Preserving & Interpreting Born-Digital Collections” Panel

22 April 2013

On Monday, April 22, 2013, the Library of Congress National Digital Information Infrastructure and Preservation Program co-hosted the Rosenzweig Forum on Technology and the Humanities: Preserving and Interpreting Born-Digital Collections, as a special event in celebration of ALA’s Preservation Week 2013. The forum hosted four speakers to talk about how their institutions are addressing the acquisition and preservation of born-digital collections and a discussion of scholarly and research use of these unique collections.

Protecting The Personal Narrative: An Assessment Of Archival Practice’s Place In Personal Digital Archiving

26 February 2013

The archival community struggles to fit in the private process of personal digital archiving. A common recommendation is to begin preservation far upstream, introducing archival practices early into the act of personal collection. But what may the archives best intentions introduce into the act of personal collection? Entering too early into the process may place undue influence on the decisions of the collector, the what gets kept and why?

Active preservation of digital personal archives is necessary for ensuring the longevity of materials, but the archives community must be aware that this may alter the personal narratives that personal archives represent. From the Personal Digital Archiving 2013 Conference, Seth Anderson’s presentation.

Where Do Humans Need To Be In The AI Loop? AVP Case Study

30 October 2021

In 2020 the Library of Congress Labs began the Humans in the Loop experiment to explore ways to responsibly combine crowdsourcing experiences and machine learning workflows. Through a public selection process, AVP was chosen as a project partner to collaboratively develop a framework for ethically, engagingly, and usefully incorporating human feedback via crowdsourcing into training data for machine learning processes. Machine learning’s reliance on pattern recognition and training decisions made by human annotators makes it really good at predicting past classifications. But complexities emerge especially when it comes to the potential to replicate and even proliferate bias and harmful effects. So where do humans need to be in the AI loop? 

The project outcomes will provide structure and context for everyone in the machine-learning and libraries communities to better evaluate potential issues that arise through automated, AI-powered metadata enrichment processes. The project is also creating training data constructed with ethical guidelines that can be used by any organization using machine learning to enrich collections description and access. The Humans in the Loop experiment builds directly on LC Labs’ sustained exploration of machine learning in cultural heritage for tasks such as pre-processing, segmentation, classification, clustering, transcription, and extraction.

The team chosen historical Yellow Pages telephone directories that have been digitized for three interactive workflows experiments. Each workflow asked users to draw boxes around ads and text and transcribe highlighted text. The goal was to “teach” the computer how to parse out information from a single digitized page and understand different content types like ads or a directory listing. 

Hear some of the Humans in the Loop team discuss how they see the project within an ethical, engaging, and useful context in the video embedded below or stream it here

VIDEO: Project Discussion: Where Do Humans Need To Be In The AI Loop?

  • Dr Meghan Ferriter, Senior Innovation Specialist with the National Digital Initiatives at the Library of Congress
  • Natalie Burclaff, Business Reference Specialist, Library of Congress
  • Shawn Averkamp, Senior Consultant, AVP
  • Kerri Willette, Senior Consultant, AVP

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