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...

Now a days mobile phones are most often used for data communication rather than voice calls. Due to this change in user behavior, there is a need to improve the QoS received by the user. One of the ways of improving the QoS is an efficient scheduling algorithm which incorporates the needs of the users and variation in channel condition. The parameters used to measure the efficiency of the sched...

Equivalence between designs is a fundamental notion in verification. The linear and branching approaches to verification induce different notions of equivalence. When the designs are modeled by fair state-transition systems, equivalence in the linear paradigm corresponds to fair trace equivalence, and in the branching paradigm corresponds to fair bisimulation. In this work we study the expressi...

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 ...

We consider a cognitive radio (CR) network in which nodes employ multi-input multi-output (MIMO) for spatial multiplexing (realized through precoding matrices). Our objective is to jointly allocate available channels to various CR links such that no channel is allocated to more than one link, and simultaneously optimize the precoding matrices (one per allocated channel) at each transmitter so a...

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...