Diverse Adversaries for Mitigating Bias in Training (2021)

Abstract: Adversarial learning can learn fairer and less biased models of language processing than standard training. However, current adversarial techniques only partially mitigate the problem of model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and stability of training.

## introduction while nlp models have achieved great successes, results can depend on spurious correlations with protected attributes of the authors of a given text, such as gender, age, or race. including protected attributes in models can lead to problems such as leakage of personally-identifying information of the author @xcite , and unfair models, i.e., models which do not perform equally well for different sub-classes of user. this kind of unfairness has been shown to exist in many different tasks, including part-of-speech tagging @xcite and sentiment analysis @xcite . one approach to diminishing the influence of protected attributes is to use adversarial methods, where an encoder attempts to prevent a discriminator from identifying the protected attributes in a given task @xcite . specifically, an adversarial network is made up of an attacker and encoder, where the attacker detects protected information in the representation of the encoder, and the optimization of the encoder incorporates two parts: (1) minimizing the main loss, and (2) maximizing the attacker loss (i.e., preventing protected attributes from being detected by the attacker). preventing protected attributes from being detected tends to result in fairer models, as protected attributes will more likely be independent rather than confounding variables. although this method leads to demonstrably less biased models, there are still limitations, most notably that significant protected information still remains in the model's encodings and prediction outputs @xcite . many different approaches have been proposed to strengthen the attacker, including: increasing the discriminator hidden dimensionality; assigning different weights to the adversarial component during training; using an ensemble of adversaries with different initializations; and reinitializing the adversarial weights every t epochs @xcite . of these, the ensemble method has been shown to perform best, but independently-trained attackers can generally still detect private information after adversarial removal. in this paper, we adopt adversarial debiasing approaches and present a novel way of strengthening the adversarial component via orthogonality constraints @xcite . over a sentiment analysis dataset with racial labels of the document authors, we show our method to result in both more accurate and fairer models, with privacy leakage close to the lower-bound. foot_0 ## methodology formally, given an input x i annotated with main task label y i and protected attribute label g i , a main task model m is trained to predict ŷi = m (x i ), and an adversary, aka "discriminator", a is trained to predict ĝi = a(h m,i ) from m 's last hidden layer representation h m,i . in this paper, we treat the k sub-discriminators a i are independently initialized. given a single input x i , the main task encoder computes a hidden representation h m,i , which is used as the input to the main model output layer and sub-discriminators. from the k-th sub-discriminator, the estimated protected attribute label is ĝa k a neural network classifier as a combination of two connected parts: (1) an encoder e, and (2) a linear classifier c. for example, in the main task model m , the encoder e m is used to compute the hidden representation h m,i from an input x i , i.e., h m,@xmath0, and the decoder is used to make a prediction, ŷi = c m (h m,i ). similarly, for a discriminator, ĝi = a(h m,i ) = c a (e a (h m,i )). ## conclusion and future work we have proposed an approach to enhance subdiscriminators in adversarial ensembles by introducing a difference loss. over a tweet sentiment classification task, we showed that our method substantially improves over standard adversarial methods, including ensemble-based methods. in future work, we intend to perform experimentation over other tasks. theoretically, our approach is general-purpose, and can be used not only for adversarial debiasing but also any other application where adversarial training is used, such as domain adaptation @xcite .