Measuring Similarity: Computationally Reproducing the Scholar's Interests
This work addresses the lack of transparency in algorithmic decisions for scholars and users of tailored search systems, though it is incremental in focusing on translation rather than new methods.
The paper tackles the problem of opaque computational text classification used in personalized services by translating classification procedures into human-understandable terms, enabling expert critique and improvement.
Computerized document classification already orders the news articles that Apple's "News" app or Google's "personalized search" feature groups together to match a reader's interests. The invisible and therefore illegible decisions that go into these tailored searches have been the subject of a critique by scholars who emphasize that our intelligence about documents is only as good as our ability to understand the criteria of search. This article will attempt to unpack the procedures used in computational classification of texts, translating them into term legible to humanists, and examining opportunities to render the computational text classification process subject to expert critique and improvement.