Tools like ChatGPT have changed our expectations for how search should work. Regardless of the current risks and realities of where the technology and rights are, more and more users now expect to be able to ask a question in natural human language and expect AI-powered machines to compose a thorough and correct response in little to no time. If the expectation is changing rapidly amongst adults now, imagine the young researcher 10 or 15 years from now who has been steeped in that reality for the majority of their lives.
So, how can information professionals approach the changing environment caused by the rapid-fire advancements in AI technology to raise the profile of the information center and provide value? Consider the following five areas where you can play a role:
1. Copyright questions about using AI in content
Information managers are often seen as copyright experts. Although you are likely not a copyright lawyer, you have a deep well of knowledge around copyright that can be employed to help your company reduce risk. I believe that is going to continue as we see more and more AI use cases.
Consider the risk when departments within the organization begin developing their own AI projects or are licensing tools that leverage AI and they do not know to ask the questions like “what rights do I have around the use of copyrighted content to train this algorithm,” “how do we accurately attribute OA content used to train an algorithm as well as in the output,” or “how is my own intellectual property protected in this tool?” It’s important before these projects come to fruition that you’re known within your organization as a leader and resource for accurate copyright information, so you can have a seat at the table and help reduce the risk that your company could incur.
Getting Started: CCC’s Intersection of AI and Copyright page serves as a resource for information on the responsible development and use of AI technologies with copyright-protected content.
2. Licensing content for AI
Information managers are often already in charge of licensing subscriptions for scientific literature and databases for use across the organization. Information managers are, therefore, uniquely positioned to also determine how best to license externally created copyrighted materials from publishers for use in AI projects. When you are a central hub of content licensing, information managers can evaluate the needs across the organization and license efficiently, removing the licensing burden from siloed groups around the organization that are only thinking about their individual AI project.
3. Company guidelines and strategic directives around technologies using AI
More and more, we are hearing from information managers that senior leadership within organizations are putting forth goals and setting expectations that technologies adopted by the organization leverage AI to improve efficiency and outcomes. This is perfectly reasonable given the landscape we’re in, where ChatGPT is being experimented with by everyone and most technologies, for better or worse, are putting some kind of LLM or generative AI into their tools to improve workflows. These directives may not consider the reality and limitations of where the technology actually is, however, nor the risks in using AI.
While AI holds great promise for R&D if used responsibly, AI systems also have the potential to generate bad science, make false or misleading conclusions, promote misinformation, and lead to harmful results. And by now we have all heard stories about hallucinations from Large Language Models (hallucinations being a fancy word for when the AI makes up facts). Many of these problems relate to the fundamental nature of generative AI as a text predicting tool, not as a system that has real knowledge. The quality, accuracy, or bias of the training data can affect the output (or more simply: garbage in, garbage out). Equally, the application of an LLM to a domain for which it is lacking training, can yield hallucinations. Techniques such as retrieval augmented generation, or RAG, are being explored to address these issues. This also means that a large amount of human validation of the results is required to use AI in the life sciences, which reduces the efficiency promised by AI. So, we’re seeing a healthy skepticism and caution alongside the optimism for an AI powered future.
In addition, we expect more and more organizations will adopt company guidelines on the use of AI, including what data you can (and more importantly cannot) use in projects, what you can do with the output, and the types of tools you can use. Many organizations are starting to develop cross-functional AI groups, where legal, IT, and other stakeholders evaluate proposed use cases before green-lighting their use internally.
Luckily, information managers are experts in information, certainly from a licensing and management side, but also in terms of searching, synthesizing, and validating results, and I have spoken to several information managers who demanded a seat at the table. We are in a key position to help evaluate the output of these tools to make sure they are delivering on the promise of AI. This is an opportunity to raise the profile of the information center by helping different functional areas assess what they need from a tool, to determine how to use data and content in a way that is copyright compliant and follows internal company guidelines. And, most importantly, help validate the output.
4. Budget ambiguity
We expect some budgeting issues around the use of content in AI. We’re seeing direct licenses being negotiated by individuals working on single projects or groups with a targeted need without consulting the information center. That means that multiple groups may be negotiating with the same publishers without knowing it and without other groups being able to take advantage. Partly this comes down to who holds the budget. As mentioned previously, if the information center holds the budget, then they can scan across the organization to see all of the need for a particular publisher and negotiate accordingly. But the budgets for information centers would need to increase to accommodate this.
I think this is where a tight partnership with data science and some executive sponsorship really comes into play. Simplistically speaking, this works best when the information center has the authority to manage the licensed content and licensing budget for AI. Alternatively, AI project negotiations can be successful when there are tight partnerships with data science and other functional areas specifically ones in which they bring the information center into negotiation processes and also support any necessary monetary investments with bill back processes.
Mary Ellen Bates, a highly respected thought leader and consultant in the information management industry, recently conducted a research project for CCC analyzing how information professionals can partner with data professionals to provide intelligence to their clients in an increasingly complex and interconnected information environment. Partnering with data science, both teams leveraging their unique strengths, we believe is a strategic and valuable path forward.
Check out our three-part series with Mary Ellen here:
- Bringing Data Science to the Information Center
- 5 Tips to Establish Collaborative Relationships Between Info Pros and Data Scientists
- 3 Ways to Become an Information Tour Guide for Data Scientists
5. Stay current
There are a lot of responsibilities for information managers in this fast-changing AI landscape – be the AI-copyright expert, license content in new and rapidly changing ways, join cross-functional teams, and advocate for appropriate budget by partnering with stakeholders. You need to stay on top of the rapidly changing advancements in AI-technologies so you can effectively evaluate vendors, the type of AI used, the use cases, and the potential risks. You essentially have to learn a whole new way of thinking and working, which has enormous possibilities. At a recent Pistoia Alliance conference in Boston, we heard the refrain several times from life science leaders that “your job isn’t going to be replaced by AI, but you will be replaced by someone willing to use AI.” While fear may be a partial motivator, I’ve talked to many informational professionals who are taking on these tasks because they know it will help them stay relevant and help their company to gain efficiency.
But you still have a day job! One of the most important things to recognize and advocate for is that you need the support and bandwidth to focus on how the information center can support the organization in its strategic AI goals. This is likely a tall order, especially for solo librarians and information centers already working at capacity. It will require selling the potential benefits internally to key stakeholders and leadership for the new and forward-thinking evolution of library services.