  We explore an active learning approach for dynamic fair resource allocation
problems. Unlike previous work that assumes full feedback from all agents on
their allocations, we consider feedback from a select subset of agents at each
epoch of the online resource allocation process. Despite this restriction, our
proposed algorithms provide regret bounds that are sub-linear in number of
time-periods for various measures that include fairness metrics commonly used
in resource allocation problems and stability considerations in matching
mechanisms. The key insight of our algorithms lies in adaptively identifying
the most informative feedback using dueling upper and lower confidence bounds.
With this strategy, we show that efficient decision-making does not require
extensive feedback and produces efficient outcomes for a variety of problem
classes.
