  The ingrained principles of fairness in a dialogue system's decision-making
process and generated responses are crucial for user engagement, satisfaction,
and task achievement. Absence of equitable and inclusive principles can hinder
the formation of common ground, which in turn negatively impacts the overall
performance of the system. For example, misusing pronouns in a user interaction
may cause ambiguity about the intended subject. Yet, there is no comprehensive
study of equitable text generation in dialogue. Aptly, in this work, we use
theories of computational learning to study this problem. We provide formal
definitions of equity in text generation, and further, prove formal connections
between learning human-likeness and learning equity: algorithms for improving
equity ultimately reduce to algorithms for improving human-likeness (on
augmented data). With this insight, we also formulate reasonable conditions
under which text generation algorithms can learn to generate equitable text
without any modifications to the biased training data on which they learn. To
exemplify our theory in practice, we look at a group of algorithms for the
GuessWhat?! visual dialogue game and, using this example, test our theory
empirically. Our theory accurately predicts relative-performance of multiple
algorithms in generating equitable text as measured by both human and automated
evaluation.
