NISO Plus Baltimore 2025

Efforts by disability rights advocates have significantly advanced the development of accessibility metadata both in publisher and library-based standards. These concerted efforts have brought the stars into closer alignment, allowing us to more fully imagine how information about accessibility features may flow from creators into library discovery services. This alignment is crucial for ensuring that individuals with disabilities can access the information they need from libraries without unnecessary barriers.
 
This panel invites thought leaders in the area of accessibility metadata to imagine the potential for beneficiary communities, the risks of inaction, and what steps we can take to make the future we want. By bringing together experts from the field, we aim to explore the transformative potential of well-implemented accessibility metadata. Without continued progress and commitments, we risk perpetuating the exclusion of disabled individuals from vital information resources – affecting education, employment, and overall quality of life. To mitigate these risks, the panel will outline actionable steps that stakeholders can take to ensure a more inclusive future. This includes advocating for the adoption of standardized accessibility metadata, fostering collaboration between publishers and libraries, and promoting awareness of the importance of accessibility in emerging practices.

Collaborative Notes 

The rise of openly available and organizationally licensed generative artificial intelligence (GenAI) tools have signalled a paradigm shift in higher education and scholarly publishing. Over the past two years, the consistent point of needing to disclose the use of GenAI, but largely leaving guidance on how to do so in a consistent manner to the discretion of the individual.

This session addresses the current state of recommendation and policy around generative artificial intelligence disclosure in learning and research contexts and introduces the Artificial Intelligence Disclosure (AID) Framework. The AID Framework is a tool that provides a transparent, consistent, and targeted approach to attribute the use of artificial intelligence in teaching and research work. In addition to the introduction of the AID Framework itself, participants will have the opportunity to review sample AID statements, discuss where such disclosure is best integrated in learning and research tasks, and brainstorm potential missing or future criteria for the AID Framework.

Collaborative Notes

Join us for to hear updates from Internet2 and InCommon pertaining to the Research & Education Federated Identity Community.

We will present progress and recommendations from several working groups active during 2024, including the SAML Subject Identifier Deployment Guidance Working Group and Federation Proxies Working Group. Each of these initiatives was convened to facilitate the refinement and adoption of various standards related to data privacy, user experience and trust. Additionally, we will encourage an interactive discussion on driving standards adoption within the R&E Federated Identity community.

Collaborative Notes