English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning risks reinforcing biases present in data, and, as we arguein this work, in what is absent from data. In healthcare, biases have markedmedical history, leading to unequal care affecting marginalised groups.Patterns in missing data often reflect these group discrepancies, but thealgorithmic fairness implications of group-specific missingness are not wellunderstood. Despite its potential impact, imputation is often an overlookedpreprocessing step, with attention placed on the reduction of reconstructionerror and overall performance, ignoring how imputation can affect groupsdifferently. Our work studies how imputation choices affect reconstructionerrors across groups and algorithmic fairness properties of downstreampredictions.

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness