Bias in book recommendation

Books occupy a significant cultural role in human societies, and libraries have long positioned themselves as institutions committed to equitable access to information and promotion of reading. As libraries increasingly adopt AI-driven tools, it becomes important to examine the potential for bias introduced by these systems. This thesis investigates bias in book recommender systems, with an emphasis on the public library sector. Daniil approaches this topic from three perspectives: understanding the current ethical considerations around book recommendation, examining the challenges of measuring statistical bias, and studying the societal implications of statistical bias in book recommendation. In the first part of the thesis, teh author maps the existing landscape. She presents the first systematic literature review of bias in book recommender systems, surveying 40 papers from computer science venues. She finds that existing research is fragmented, often treats books as interchangeable with other media items, and rarely engages with the unique characteristics of the book domain.