  Written language carries explicit and implicit biases that can distract from
meaningful signals. For example, letters of reference may describe male and
female candidates differently, or their writing style may indirectly reveal
demographic characteristics. At best, such biases distract from the meaningful
content of the text; at worst they can lead to unfair outcomes. We investigate
the challenge of re-generating input sentences to 'neutralize' sensitive
attributes while maintaining the semantic meaning of the original text (e.g. is
the candidate qualified?). We propose a gradient-based rewriting framework,
Detect and Perturb to Neutralize (DEPEN), that first detects sensitive
components and masks them for regeneration, then perturbs the generation model
at decoding time under a neutralizing constraint that pushes the (predicted)
distribution of sensitive attributes towards a uniform distribution. Our
experiments in two different scenarios show that DEPEN can regenerate fluent
alternatives that are neutral in the sensitive attribute while maintaining the
semantics of other attributes.
