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

The algorithmic fairness of predictive analytic tools in the public sectorhas increasingly become a topic of rigorous exploration. While instrumentspertaining to criminal recidivism and academic admissions, for example, havegarnered much attention, the predictive instruments of Child Welfarejurisdictions have received considerably less attention. This is in partbecause comparatively few such instruments exist and because even fewer havebeen scrutinized through the lens of algorithmic fairness. In this work, weseek to address both of these gaps. To this end, a novel classificationalgorithm for predicting reunification success within Oregon Child Welfare ispresented, including all of the relevant details associated with building suchan instrument. The purpose of this tool is to maximize the number of stablereunifications and identify potentially unstable reunifications which mayrequire additional resources and scrutiny. Additionally, because thealgorithmic fairness of the resulting tool, if left unaltered, isunquestionably lacking, the utilized procedure for mitigating such unfairnessis presented, along with the rationale behind each difficult and unavoidablechoice. This procedure, though similar to other post-processing group-specificthresholding methods, is novel in its use of a penalized optimizer andcontextually requisite subsampling. These novel methodological components yielda rich and informative empirical understanding of the trade-off continuumbetween fairness and accuracy. As the developed procedure is generalizableacross a variety of group-level definitions of algorithmic fairness, as well asacross an arbitrary number of protected attribute levels and risk thresholds,the approach is broadly applicable both within and beyond Child Welfare.

The Pursuit of Algorithmic Fairness: On "Correcting" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier