This paper presents a theory of fairness within the framework of Dijkstra's weakest preconditions and Back's action systems. It is general enough to deal with all kinds of fairness, not just strong and weak fairness. A deenition of fairness and reenement to deal with non-terminating programs is also obtained. For terminating and non-terminating programs with and without fairness, theorems about...

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware rec...

The purpose of this research is to examine the influential attributes employees’ attitudes and intentions stay in domain human resources management a low-cost carrier business. Using justice theory as theoretical underpinning, financial compensation, nonfinancial coworker relationships, procedural fairness were derived. explained attitude intention stay. This study used survey collected data on...

We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite the fact that fairness is consistent with ...

Fairness is an important issue for parallel job scheduling policies, but has been ignored in most of previous studies. In this paper, we consider two different styles of job fairness: FCFS and EQ. Commonly used summary statistics are applied to different job measures to evaluate the fairness under a wide range of non-preemptive parallel job scheduling policies, including priority backfill polic...

In this paper we give a general definition of weighted fairness and discuss how a pricing policy can be mapped to general weighted (GW) fairness. The GW fairness can be achieved by calculating the ExcessFairshare (weighted fairshare of the left over bandwidth) for each VC. We show how a switch algorithm can be modified to support the GW fairness by using the ExcessFairshare term. We use ERICA+ ...

We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite the fact that fairness is consistent with ...

Masses of experiments have shown individual preference for fairness which seems irrational. The reason behind it remains a focus for research. The effect of spite (individuals are only concerned with their own relative standing) on the evolution of fairness has attracted increasing attention from experiments, but only has been implicitly studied in one evolutionary model. The model did not invo...

Abstract In this paper we study performance tradeoffs of fairness algorithms for ring networks with spatial bandwidth reuse, by using two measures: (i) the fairness cycle size as a complexity measure, and (ii) the Max-Min optimal fairness criterion as a throughput measure. The fairness cycle size is determined by the number of communication links involved in every instance of the fairness algor...

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness ...