Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?

Abstract

We observe a recent trend toward applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use would mean. We argue that it is important to reassert the central research focus of the field of information retrieval, because information access is not merely an application to be solved by the so-called ‘AI’ techniques du jour. Rather, it is a key human activity, with impacts on both individuals and society. As information scientists, we should be asking what do people and society want and need from information access systems and how do we design and build systems to meet those needs? With that goal, in this conceptual article we investigate fundamental questions concerning information access from user and societal viewpoints. We revisit foundational work related to information behavior, information seeking, information retrieval, information filtering, and information access to resurface what we know about these fundamental questions and what may be missing. We then provide our conceptual framing about how we could fill this gap, focusing on methods as well as experimental and evaluation frameworks. We consider the Web as an information ecosystem and explore the ways in which synthetic media, produced by LLMs and otherwise, endangers that ecosystem. The primary goal of this conceptual article is to shed light on what we still do not know about the potential impacts of LLM-based information access systems, how to advance our understanding of user behaviors, and where the next generations of students, scholars, and developers could fruitfully invest their energies.