  The (COVID-19) pandemic-induced restrictions on travel and social gatherings
have prompted most conference organizers to move their events online. However,
in contrast to physical conferences, virtual conferences face a challenge in
efficiently scheduling talks, accounting for the availability of participants
from different time-zones as well as their interests in attending different
talks. In such settings, a natural objective for the conference organizers
would be to maximize some global welfare measure, such as the total expected
audience participation across all talks. However, we show that optimizing for
global welfare could result in a schedule that is unfair to the stakeholders,
i.e., the individual utilities for participants and speakers can be highly
unequal. To address the fairness concerns, we formally define fairness notions
for participants and speakers, and subsequently derive suitable fairness
objectives for them. We show that the welfare and fairness objectives can be in
conflict with each other, and there is a need to maintain a balance between
these objective while caring for them simultaneously. Thus, we propose a joint
optimization framework that allows conference organizers to design talk
schedules that balance (i.e., allow trade-offs) between global welfare,
participant fairness and the speaker fairness objectives. We show that the
optimization problem can be solved using integer linear programming, and
empirically evaluate the necessity and benefits of such joint optimization
approach in virtual conference scheduling.
