While therewardcompensation mechanism isunknown,the learner can adapt his (her) decision to the past reward feedback so as to maximize the sum of rewards.

The objective is to maximize cumulative reward byexploring the actions to discover optimal ones (having the best expectedreward),balancedwithexploitingthem.

We therefore assume that the algorithm has access to an oracle that knows intuitively what it means to be fair, but cannot explicitly enunciate the fairness metric.

Neural Information Processing Systems http://nips.cc/

We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting,candidates' rewardsmaynotbedirectly comparable between groups,for example when the agent is an employer hiring candidates from different ethnic groups and some groups have a lower reward due to discriminatory bias and/or socialinjustice.

In this paper we adapt the nearest neighbour rule to the contextual bandit problem.