Fairness Incentives in Response to Unfair Dynamic Pricing

The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation is socially desirable. To address this, policy makers might leverage tools such as taxation and subsidy to adapt policy mechanisms dependent upon their social objective. In this paper, we explore the potential for AI methods to assist such intervention strategies. To this end, we design a basic simulated economy, wherein we introduce a dynamic social planner (SP) to generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours, and to use the collected tax budget to subsidize consumption among underrepresented groups. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and finally as a full reinforcement learning (RL) problem, evaluating welfare outcomes from each case. To alleviate the difficulty in retaining meaningful tax rates that apply to less frequently occurring brackets, we introduce FairReplayBuffer, which ensures that our RL agent samples experiences uniformly across a discretized fairness space. We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13.19% in the full RL setting.

Federated Edge Learning : Design Issues and Challenges

Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalability. However, implementing FL at the network edge is challenging due to system and data heterogeneity and resources constraints. In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL). The design of FEEL algorithms for resources-efficient learning raises several challenges. These challenges are essentially related to the multidisciplinary nature of the problem. As the data is the key component of the learning, this article advocates a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a general framework for the data-aware scheduling as a guideline for future research directions. We also discuss the main axes and requirements for data evaluation and some exploitable techniques and metrics.