Sherrard Ewing Glaittli

I know have been radio silent online for much of the past few months. I have to be honest, I have been struggling with profound feelings of despair at watching the large scale attacks on civil society these past few months, with the knowledge that we have three-and-a-half years left of this. At least.

As a Librarian, watching the civic, institutional and diplomatic damage of the second Trump administration has been personally gutting. The breadth and deapth of the attacks on government institutions, knowledge, research and public service programs. It has been a non-stop shock-and-awe campaign against anyone and anything that benefits the public good.

The cascading chaos of DOGE, the Big Ugly Bill, and the United States reneging on its domestic and international commitments are the most visible actions that the public is seeing. All of that is outrageous enough. But that’s just the tip of the iceberg. What people are not seeing are the countless research projects that are silently being cut off. Some of these projects may find funding from other institutions, or other countries. Some, if not most, will probably just fade into obscurity. We are entering a research dark age.

I’m not speaking purely in hypotheticals, either. One of the most alarming examples are the defunding of incredibly valuable mRNA vaccine research grants. I could list countless others, across a myriad of scientific domains – all of them deserve more visibility advocacy on their own. However, as a Librarian with eye on Trustworthy AI policy, I have to limit my scope.

In particular, I want to highlight research projects that demonstrate tangible promise for implementing Trustworthy AI policies in the future. The bulk of these research project research projects were supported by the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST).

In 2023, the National Science Foundation was funding the National Artificial Intelligence Research Institutes. There were (are) seven research institutes decidated to various themes:

• Trustworthy AI (TRAILS)
• Intelligent Agents for Next-Generation Cybersecurity (ACTION)
• Climate Smart Agriculture and Forestry (AI-CLIMATE)
• Neural and Cognitive Foundations of Artificial Intelligence (ARNI)
• AI for Societal Decision Making (AI-SDM)
• AI-Augmented Learning to Expand Education Opportunities and Improve Outcomes (INVITE, AI4ExceptionalEd)

In 2024, NIST launched Trustworthy & Responsible Artificial Intelligence Resource Center (AIRC) and its NIST AI Risk Management Framework (AI RMF 1.0). As I learned about these technology and policy frameworks, I got really excited. I even had the pleasure to speak about it at IAC24 and DGIQW 2025.

But already, you can see erasure of of some of these research themes from reviewing changes to the NSF’s AI Research Institutes website. In listing its research themes you can see that Trustworthy AI and Climate Science have been de-emphasized. Most grant have been archived indefiniately.

Since then, the Trump Administration has released its AI Action Plan which I will maintain is intentionally vague. It also strategically de-emphasizes and omits the Trustworthy and Sustainability themes of AI research and policy.

From my prospective, this all looks bleak. After some grief, I have to remind myself that we’re not starting from zero:

• Most of the research and publications have not been taken down (yet). Much of it is being mirrored elsewhere in case
• Some of the research grants funding is still ongoing, others are finding other sources of funding
• All of the peole behind these projects and papers are still around
• While the US regresses in Trustworthy AI policy, the work of the European Commision, States and other regions moves foward
• This administration is not forever

The feelings of despair are real. They are the product of an intentional campaign. I said Shock and Awe approach and I meant it. DOGE, and everything else along with it, is meant to despirit those who believe in the public good in any way whatsoever. If you feel despair, you are not alone and you are not overreacting. This feeling of isolation and helpeless is the point.

As time goes on, I find my despair shifting to anger. I return to the Internet after a Summer hiatus with a sense of intention. I want to find the people, organizations, and papers that are cointinuing to do good work out there. And by good work I mean work that is making search, AI or any information experience on the Internet more Trustworthy.

Voices like Gary Marcus and Ed Zitron have done great work drawing visibility on the larger problems with the current AI hype bubble. I would like to supplement their work by trying to draw more attention to specific technical mechanisms and legal definitions proposed for making generative AI more trustworthy, secure and reliable than it currently is.

Another person whose work I appreciate is Helen Toner, who brings a cybersecurity perspective to analyzing AI technologies. Work from Simon Willison, Kurt Cagle and Jorge Arango have helped me to get hands on experience with large language models that has provided understanding I would not have gathered otherwise. A lot of amazing people are still doing great work out there. I want to stand on the shoulders of giants to inspire mental models for Trustworthy AI as infrastructure, as policy goals, as a work intended for the public good.

Don’t listen to the AI intimidation-mongers. You can understand how a Generative AI model works just as you can understand a crystal radio set. This technology is not “above our heads.” It’s weird, but it’s not magic. I’ve menioned how I feel that a lot of the public messaging about generative AI intentionally obfuscates how the technology works. My frustration (and anger) with this intentional obfuscation sparked a desire to write articles and give presentations as understandable as possible.

In the age of generative AI, I am of the mind that previously arcane issues of Information Science, Information Behavior Theory and Information Literacy have never ever been more culturally important or directly relatable. I aim to tie Trustworthy AI research and policy discussions to real-life, human examples with tangible benefits to the public and our daily lives.

Some topics I want to explore, for example:

• Knowledge graphs, why they’re incredibly important (i.e. keeping generative AI models honest and up-to-date), and who is building knowledge graphs as infrastructure for information retrieval of the future.
• The tactics and motivations behind the intentional obfuscation of generative AI technologies in the public discource
• Exploring new technologies trees outside of traditional generative AI stacks, static and dynamic prompting, RAG and model routing (this is currently a gap in my understanding)
• What a post-platform Internet looks like (Protocols Not Platforms!)
• Those documenting the damage to insitutions and preparing for a post-MAGA rebuilding.

As I return from time offline, I look forward to connecting with others and taking part in the conversations to come.

As we fight these attacks on information access and the public good, I will leave off on two music-related notes. I will paraphrase Gord Downie, a personal hero of mine, when I say we should not tolerate these attacks on our institutions, collective knowledge and understanding with any patience, tolerance or restraint.

The destruction is overwhelming, but it is not forever. This song by Descartes a Kant, has been keeping me going. There will be creation after destruction. You’ll see.

Let’s find each other. Share solidarity. Let’s organize and pick up the pieces from these fucking vandals.

With my colleague, Erik Lee, I had the great privilege to speak at the Information Architecture Conference in Seattle (at the beautiful Seattle Public Central Library) in April of last year. The topic of the presentation, titled “Beware of Glorbo: A Case Study and Survey of the Fight Against Misinformation” was about AI Data Poisoning (now also known as Prompt or Context Injection), but there was a section where I summarized the state of AI Data Policy, as I understood it then. People told me that the mental models I provided were helpful for getting bearings on the specific terms surrounding AI policy.

In light of this feedback, I thought it would be good to revisit this talk ahead of an update I’m giving later this year. But first, let’s view that state of AI policy terms in April of 2024:

My deck showed the nebulous state of popular AI policy terms that were being thrown around. The term names are not intuitively descriptive and the relationships between them is unclear, especially when sloppy marketing jargon would obscure their meanings as technical terms of art.

We start by setting definitions. Terms that were conceptually identical have been grouped.

• Explainable/Transparent AI – AI that can explain the reasoning behind its output

• Robust AI – AI that is technically robust: (consistent, accurate and secure)

• Ethical/Responsible AI – AI that is inclusive, non-discriminatory, fair – may even have environmental considerations

• Trustworthy AI – AI that encompasses the above principles: safe, secure, consistent and accountable to enable trust in the AI output

Strong AI – AI that is aware of concepts, its own reasoning and itself as an independent agent

Using these definitions, I drew a diagram to help people visualize the state of these terms.

In the diagram, I placed Trustworthy AI as a superset concept that includes each of the other AI policy concepts (explainable/transparent AI, robust AI, ethical/responsible AI) within it. Strong AI (now more commonly referred to as Advanced General Intelligence (or AGI) is disconnected since it is only theoretical.

This model is imperfect as these policies often overlap and share goals, definitions and desired outcomes. I found, however, thinking of each of these policies as contributing to the larger goal of Trustworthy AI to be a helpful way of understanding each of these policies and how the relate to each other.

In addition to defining and contextualizing these AI policies to one another, I also profiled the organizations making the most waves in these spaces and what had been published and legislated up to that point.

The heavy hitters that I had found were:

• European Commission High-level expert group on artificial intelligence – founded in 2018, published first ethics guidelines on Trustworthy AI in 2019
• International Telecommunication Union (ITU) a UN agency – published their Trustworthy AI Framework in 2020 as part of the UN’s AI for Good program.
• NIST (National Institute of Standards and Technology) – Published Trustworthy AI: Managing the Risks of Artificial Intelligence in 2022
• NSF (National Science Foundation) – Launched the Trustworthy AI Initiative in 2023

Additionally, I noted some movement in the Executive and Legislative branches of the United States government at that time.

• Select White House Committee on Artificial Intelligence Released the NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT STRATEGIC PLAN 2023 UPDATE in May 2023
  • Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence October 2023
• United States House Bipartisan Task Force on Artificial Intelligence in February 2024
  • The United State Senate had exploratory hearings on AI back in the summer of 2023.

Now, nearly a year later what has changed? A lot, as you can all imagine.

I will speak about this at DGIQ West 2025 in a talk titled “Catching Up with Glorbo: Combatting AI Data Poisoning with RAG Frameworks“. You won’t have to wait until May, as I plan to write about this in Part 2 ahead of the conference. In the meantime, here are some highlights include:

• The proliferation of “Open” AI models and frameworks via repositories like HuggingFace and Ollama
• The impact of model distillation techniques (such as DeepSeek) on AI policy, particularly Ethical/Responsible AI policy
• How the ongoing evisceration of United States Federal agencies by the second Trump Administration is stuntinRg US leadership on AI policy
• The continuing forward movement of AI policy in places such as the EU

Thank you to everyone who has encouraged me to continue write and speak about this subject. Stay tuned for part two. Please don’t hesitate to reach out to me with helpful feedback (that includes corrections). 🙂 See you soon in Part 2.