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

Algorithmic decision-making in societal contexts, such as retail pricing,loan administration, recommendations on online platforms, etc., often involvesexperimentation with decisions for the sake of learning, which results inperceptions of unfairness among people impacted by these decisions. It is hencenecessary to embed appropriate notions of fairness in such decision-makingprocesses. The goal of this paper is to highlight the rich interface betweentemporal notions of fairness and online decision-making through a novelmeta-objective of ensuring fairness at the time of decision. Given somearbitrary comparative fairness notion for static decision-making (e.g.,students should pay at most 90% of the general adult price), a correspondingonline decision-making algorithm satisfies fairness at the time of decision ifthe said notion of fairness is satisfied for any entity receiving a decision incomparison to all the past decisions. We show that this basic requirementintroduces new methodological challenges in online decision-making. Weillustrate the novel approaches necessary to address these challenges in thecontext of stochastic convex optimization with bandit feedback under acomparative fairness constraint that imposes lower bounds on the decisionsreceived by entities depending on the decisions received by everyone in thepast. The paper showcases novel research opportunities in onlinedecision-making stemming from temporal fairness concerns.

Algorithmic Challenges in Ensuring Fairness at the Time of Decision