All Should Be Equal in the Eyes of Language Models: Counterfactually
  Aware Fair Text Generation
Pragyan Banerjee Abhinav Java Surgan Jandial Simra Shahid Shaz Furniturewala Balaji Krishnamurthy Sumit Bhatia
Fairness in Language Models (LMs) remains a longstanding challenge, given the
inherent biases in training data that can be perpetuated by models and affect
the downstream tasks. Recent methods employ expensive retraining or attempt
debiasing during inference by constraining model outputs to contrast from a
reference set of biased templates or exemplars. Regardless, they dont address
the primary goal of fairness to maintain equitability across different
demographic groups. In this work, we posit that inferencing LMs to generate
unbiased output for one demographic under a context ensues from being aware of
outputs for other demographics under the same context. To this end, we propose
Counterfactually Aware Fair InferencE (CAFIE), a framework that dynamically
compares the model understanding of diverse demographics to generate more
equitable sentences. We conduct an extensive empirical evaluation using base
LMs of varying sizes and across three diverse datasets and found that CAFIE
outperforms strong baselines. CAFIE produces fairer text and strikes the best
balance between fairness and language modeling capability