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

Most existing notions of algorithmic fairness are one-shot: they ensure someform of allocative equality at the time of decision making, but do not accountfor the adverse impact of the algorithmic decisions today on the long-termwelfare and prosperity of certain segments of the population. We take a broaderperspective on algorithmic fairness. We propose an effort-based measure offairness and present a data-driven framework for characterizing the long-termimpact of algorithmic policies on reshaping the underlying population.Motivated by the psychological literature on \emph{social learning} and theeconomic literature on equality of opportunity, we propose a micro-scale modelof how individuals may respond to decision-making algorithms. We employexisting measures of segregation from sociology and economics to quantify theresulting macro-scale population-level change. Importantly, we observe thatdifferent models may shift the group-conditional distribution of qualificationsin different directions. Our findings raise a number of important questionsregarding the formalization of fairness for decision-making models.

On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning