Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding (2021)

Abstract: 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.

## introduction language data often carries implicit biases or contains sensitive information that may have negative consequences for human and machine understanding. for example, a person's choice of vocabulary can reveal their social identity (age, gender, or political affiliation) @xcite ; a few examples are shown in hiding sensitive information in textual dataincluding text that carries implicit bias-is an essential task. in this paper we consider the setting of graduate school admissions as a case-study, where fair evaluation of applicants should depend on academic performance or research potential, irrespective of nationality, gender, etc. text from reference ## depen as shown in figure 1 , our neutralizing approach depen 1 has two stages: detect and perturb. ## related work debiasing language generation there are three main streams to debias nlg tasks: counterfactual data augmentation @xcite ; training-time methods @xcite @xcite ; and inference-time methods. saunders and byrne (2020) mitigate gender bias in machine translation via transfer learning using handcrafted gender-balanced datasets. @xcite generate with well-formulated bias triggers based on @xcite to equalize biases between demographics. @xcite propose a gradient-based method for controllable generation and show its efficacy in toxicity reduction. however, all these methods require explicit labels or parallel data regarding the desired attribute. re-writing here specific parts of the original text are revised to be more aligned with a target attribute @xcite . representative approaches use an encoder-decoder setup with a discriminator (e,g. style) @xcite @xcite @xcite , backtranslation @xcite @xcite , pretraining @xcite , or use retrieval framework @xcite . a few approaches adapt these techniques for debiasing. @xcite mitigate gender bias by converting between masculineand feminine-inflected sentences with data augmentation; @xcite jointly train a reconstruction and an out-of-domain paraphrasing task to correct bias, which requires a parallel corpus with attribute-sensitive (e.g. gender) verbs assigned and masked. in contrast, we aim to rewrite neutrally without human guidance. ## conclusion in this work, we propose a gradient-based rewriting framework, depen, to neutralize a text that carries sensitive information (e.g., gender) by detecting the sensitive-predictable parts and perturbing the regeneration via a neutralization constraint. the constraint will shift the re-generated sentences to be uniform distributed for the sensitive attribute (e.g., neither male nor female) with minimal editing to maintain the semantic content.