  Recently, almost all conferences have moved to virtual mode due to the
pandemic-induced restrictions on travel and social gathering. Contrary to
in-person conferences, virtual conferences face the challenge of efficiently
scheduling talks, accounting for the availability of participants from
different timezones and their interests in attending different talks. A natural
objective for conference organizers is to maximize efficiency, e.g., total
expected audience participation across all talks. However, we show that
optimizing for efficiency alone can result in an unfair virtual conference
schedule, where individual utilities for participants and speakers can be
highly unequal. To address this, we formally define fairness notions for
participants and speakers, and derive suitable objectives to account for them.
As the efficiency and fairness objectives can be in conflict with each other,
we propose a joint optimization framework that allows conference organizers to
design schedules that balance (i.e., allow trade-offs) among efficiency,
participant fairness and speaker fairness objectives. While the optimization
problem can be solved using integer programming to schedule smaller
conferences, we provide two scalable techniques to cater to bigger conferences.
Extensive evaluations over multiple real-world datasets show the efficacy and
flexibility of our proposed approaches.
