日本語
 
User Manual Privacy Policy ポリシー/免責事項 連絡先
  詳細検索ブラウズ

アイテム詳細


公開

成果報告書

Equity of Attention: Amortizing Individual Fairness in Rankings

MPS-Authors
/persons/resource/persons79330

Biega,  Asia J.
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

URL
There are no locators available
フルテキスト (公開)

arXiv:1805.01788.pdf
(プレプリント), 2MB

付随資料 (公開)
There is no public supplementary material available
引用

Biega, A. J., Gummadi, K. P., & Weikum, G. (2018). Equity of Attention: Amortizing Individual Fairness in Rankings. Retrieved from http://arxiv.org/abs/1805.01788.


引用: http://hdl.handle.net/21.11116/0000-0002-1563-7
要旨
Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.