Ranking is widely used for ob- ject selection, when resources are limited and it is necessary to select a subset of most rel- evant objects for further ...

Ranking is widely used for object selection, when resources are limited and it is necessary to select a subset of most relevant objects for further processing.

2022/01/24 · NeurIPS2021読み会の発表スライドです論文: Fairness in Ranking under Uncertainty ランキング対象の価値が完全に把握出来ない状況でのランキング ...

2021/07/14 · We show how to compute rankings that optimally trade off approximate fairness against utility to the principal.

The goal of this method is to reduce the uncertainty and approach unique solutions for different source strength and location. The error of the method is found ...

Fairness has emerged as an important consideration in algorithmic decision making. Unfairness occurs when an agent with higher merit obtains a worse outcome ...

The result shows Bioenergy with Carbon Capture and Storage (BECCS) as the most optimal alternative for achieving negative emission goals.

We believe that uncertainty of merit is one of the most important sources of unfairness, and modeling it explicitly and axiomatically is key to addressing it.

In the paper, we present two sets of experiments -- a simulation based on the Movielens dataset, and a real-world paper recommender system run at KDD 2020.

2024/06/10 · We show how to compute rankings that optimally trade off approximate fairness against utility to the principal. In addition to the theoretical ...