The AI Committee of the American Society for Indexing is pleased to announce the release of our third white paper, “AI and Book Indexing: Trajectory Data.”
Click here to download the white paper in full.
Read the abstract below.
Abstract
While AI can be a highly useful tool in some situations and with some highly-defined problem spaces, book indexing does not appear to be one of them. This paper supplements our previously-published white papers by comparing the effectiveness of large language model AI chatbots, across multiple generations, at indexing a book or book chapter. Our earlier white papers indicated that AI-generated indexes fail to reflect subtopics and related topics, preventing readers from having appropriate access to all indexable material; that AIs under-index, failing to pick up on significantly discussed terms; at the same time they also over-index, cluttering the index with irrelevant entries and redundant subheadings; and that AI-generated indexes also fail a standard copyeditor’s test for accuracy. The current paper indicates that AIs have not improved significantly on any measure.