SynthIR: The First Workshop on Synthetic Content in Information Retrieval Ecosystems
Abstract
The proliferation of AI-generated content is fundamentally altering the information ecosystems in which retrieval systems operate. Search engines, recommender systems, and retrieval-augmented generation pipelines increasingly function in mixed information environments where synthetic and human-authored content are tightly interwoven, raising system-level challenges for information retrieval. Key issues include limitations in evaluation validity, as traditional metrics designed for human-authored corpora fail to capture the distinctive properties of AI-generated content; shifts in retrieval behavior and ranking dynamics, as systems may inadvertently favor procedurally generated but weakly grounded information; and challenges to user trust, as assumptions about the provenance and reliability of retrieved human content become more difficult to distinguish from generated content. Rather than focusing only on model-centric performance comparisons, this workshop aims to provide a forum to analyze these implications with an emphasis on reflection, evaluation, and human-centered system design, and to foster community-driven discussion that may inform future evaluation efforts, including potential shared tasks or tracks in venues such as TREC, CLEF, FIRE, or NTCIR.
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