  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.
