Editor’s Note: Today’s post is by Scholarly Kitchen Chef Todd Carpenter, Tasha Mellins-Cohen, and Monica Westin. Tasha is Executive Director at COUNTER and Founder of Mellins-Cohen Consulting. Monica is the Director of Open Policy Development at Cambridge University Press.
This is the second in a series of posts on AI systems, provenance tracking in generative artificial intelligence systems, and the implications on usage and assessment. The first post was published last week. The next piece in this series will cover forthcoming community work related to provenance tracking and usage.
As generative AI systems and agentic tools are adopted more broadly in scholarly research systems, we must retain a commitment to the core values of verifiability, of traceability, of trustworthiness and connecting elements of the scholarly record. The scholarly communications infrastructure needs to adapt and incorporate new standards that support provenance, attribution and various types of assessment in these new tools.
The very notion of “Artificial Intelligence” began with a workshop hosted at Dartmouth College in 1956, when computer science researchers John McCarthy, Claude Shannon, Nathaniel Rochester, and Marvin Minsky, co-organized the Dartmouth Summer Research Project on Artificial Intelligence. Since those early days, convenings to discuss technical issues related to various computer science challenges have been a regular occurrence as technologists seek to understand and improve various elements of the technology.
Last month, Cambridge University Press, COUNTER, and NISO co-hosted a workshop to frame the interrelated problems associated with attribution, provenance and usage metrics in research communications. Nearly three dozen experts joined in the discussion, each representing a different participant perspective,with libraries, AI tool developers, funders, societies, publishers, platforms, and other infrastructure service providers all represented.
A summary report of the meeting has been released and initial work to advance the practical outcomes is beginning.
Laying to groundwork in the workshop
Building on a set of ongoing conversations about the need for standardization of AI, this meeting set out to narrowly define a constrained set of potential projects that might improve the current state of the art. While ongoing conversations have been underway for some time, this meeting sought to move beyond the abstract concerns and toward concrete infrastructure changes that might be implementable over the next year. Noting that there is tension between the pace of consensus development and the pace of technological change, the meeting sought to scope new work that might help publishers, libraries, researchers, and AI providers work from common assumptions.
Foundationally, the meeting began with the recognition that traditional usage metrics, including downloads, views, searches, and requests, do not fully capture how AI systems interact with scholarly content. AI tools may retrieve passages, retrieve metadata, generate summaries, or synthesize findings across numerous papers without a researcher ever clicking through to the original article or book chapter. Many of the sources that are included in an output may not even be attributed or referenced, for that researcher to follow back if they wanted to.
Conversation in the meeting uncovered various complexities of even simple-seeming approaches in these areas. For example, a research output may be involved both in an explicit AI interaction at the time of retrieval (inference time), as well as an implicit AI interaction during the training of the underlying LLM (training time). Metrics for these two uses would need to be distinguished, though for the purposes of the workshop we focused on inference metrics. Versioning, corrections, and retractions represent additional challenges to clean metrics even for a single stable output. One frequent metaphor during the day was that of “supply chain” problems.
While some aspects of the meeting were exploratory and sought to expose concerns and issues, the main goals were more concrete. The workshop had a practical focus, namely, to identify specific, narrowly focused, and implementable areas for future work related provenance and usage. With these two related focal points, the group identified at the outset two separate potential workstreams, leaving aside questions of model development, citation accuracy and rationale for citation. Building upon the existing COUNTER AI best practice, the first workstream will include exploring how to extend the AI reporting structure to capture agentic usage, and third-party AI tool use of content – something that COUNTER’s AI working group is already tackling. The second workstream will focus on citation accuracy, provenance, and usage reporting as related but separate. This will include a pilot to test provenance models in research applications and AI retrieval systems.
Usage in AI Systems
It has been widely recognized that traditional usage metrics are insufficient for AI-driven usage, and COUNTER has published a preliminary best practice to start addressing the issue. The lack of visibility into AI usage is problematic both for institutional collection decisions and justifying subscription value, and for publishers seeking insights into the value of their content in an AI-first discovery environment.
COUNTER’s work so far has focused on meaningful use, defined as content that is specifically incorporated into the final AI-generated answer, rather than content merely retrieved or considered. There are several future metrics questions that are likely to need to measure a range of computational activities. First, as noted is the need for component-level tracking. Measurement should extend beyond the COUNTER Item (article/chapter) to the component level. While mechanisms exist for component reporting and for rolling that usage up to an item or title level, further work is needed to incorporate this into work on AI systems. A broader examination of holistic assessment questions is also necessary to develop metrics that capture usage of all content assessed by an AI system, in addition to the existing meaningful use metrics.
Crucially, the resulting metrics must roll up to the item (article/chapter) level to maintain value for library collection decisions. A top priority for COUNTER is determining how to bring external AI providers and third-party tools into a shared reporting infrastructure.
Provenance in AI systems
Other issues of provenance and attribution are complex and particularly challenging regarding different types of AI systems as outlined last week, without digging too deeply in the technical weeds. One important distinction that was highlighted during the workshop was distinguishing between attribution inside a foundation LLM model and attribution in systems built around retrieval, APIs, agents, and publisher-controlled content delivery. Model-level attribution is both a technically challenging endeavor and is less tractable in the near term, therefore less actionable. Realistically as well, the entire scholarly communications industry, including publishers, libraries and research institutions, combined has very limited influence on the decisions surrounding foundation model development.
A more practical path, therefore, is to improve provenance at the point where content is retrieved, supplied, transformed, cited, or reused. A standards opportunity exists around developing a minimum provenance payload specifically for research applications and contexts. Such a model might have broader application but is critical for the research applications the scholarly publishing industry serves. This would include information such as persistent identifiers, source location, timestamps, content hashes, digital signatures, version and copyright information, component identifiers, and relationships between source objects and AI outputs. Such work might also create standard display approaches so that researchers using these systems might immediately be able to recognize a reference in generated outputs, in the same way such references are recognizable in research literature. This approach would also need to capture, recognize and output to the user—or possibly exclude from outputting—questionable research outputs, such as retractions and other expressions of concern regarding research outputs. The timeline of content would need to be incorporated to recognize that the state of scholarly knowledge is ever evolving and what might have been accurate at the time of publication, might not be recognized as the state of the art today.
This framework would be equally applicable for component parts as it would be with complete works. AI systems do not always capture entire works, such as complete articles, books, or reports, in their outputs. A robust attribution model must also be able to capture component-level information and extend and link back to parent content objects. AI systems are often operating using sections, figures, tables, images, datasets, snippets, or semantic chunks and a robust system for provenance and attribution needs to include these linkages, as well as rights and citation information. Such component usage also needs to be captured for assessment purposes, which the current standards ecosystem doesn’t yet support.
Practical Next Steps
Last week, in the first post of this series we discussed how verifiable, and trackable provenance representation in generative AI systems is core to trust and retaining the system of credit that underpins the scholarly exchange system. Regardless of whether content distribution is open access or subscription-based, it is vital that we ensure content retains its source information while it is processed by and incorporated in results of generative AI tools. Earlier this year, the STM Association outlined its focus on the values of publishing and how it relates to generative AI tools. This was important work, but it needs to be backed up with guidance on how to implement that vision. Based on the discussions during the Cambridge workshop, there is a robust and open conversation about how these values can work in practical terms.
There have been many robust legal arguments about how AI content is replicated in generative AI outputs. There have also been discussions about the removal of provenance information and copyright information during the process of content ingestion for AI systems. These legal issues are certainly important and will have significant impacts on the business models of both publishers and generative AI system developers. These issues certainly have a place in discussions around how AI tool providers interact with scholarly publishers. More fundamentally though, these concerns around provenance, usage, citation, attribution and trustworthiness are essential to what it means to disseminate research. Any system that seeks to serve the research communities needs to take these concerns seriously. The developers and companies that offer these systems should be expected to work to ensure these values are honored and implemented in their services. The meeting in Cambridge is a clear signal that many are interested and willing to engage. It pointed toward practical next steps that included potential pilot projects, consensus development on standards alignment, and how we can support collaboration among publishers, libraries, infrastructure providers and AI developers. The next step will be to turn that engagement into action.
