Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters (2024)

Abstract: Bias mitigation of Language Models has been the topic of many studies with a recent focus on learning separate modules like adapters for on-demand debiasing. Besides optimizing for a modularized debiased model, it is often critical in practice to control the degree of bias reduction at inference time, e.g., in order to tune for a desired performance-fairness trade-off in search results or to control the strength of debiasing in classification tasks. In this paper, we introduce Controllable Gate Adapter (ConGater), a novel modular gating mechanism with adjustable sensitivity parameters, %In addition to better perseverance of task performance and enhanced information removal, which allows for a gradual transition from the biased state of the model to the fully debiased version at inference time. We demonstrate ConGater performance by (1) conducting adversarial debiasing experiments with three different models on three classification tasks with four protected attributes, and (2) reducing the bias of search results through fairness list-wise regularization to enable adjusting a trade-off between performance and fairness metrics. Our experiments on the classification tasks show that compared to baselines of the same caliber, ConGater can maintain higher task performance while containing less information regarding the attributes. Our results on the retrieval task show that the fully debiased ConGater can achieve the same fairness performance while maintaining more than twice as high task performance than recent strong baselines. Overall, besides strong performance ConGater enables the continuous transitioning between biased and debiased states of models, enhancing personalization of use and interpretability through controllability.

## related work efficient modular training introduces an alternative to fine-tuning, where a (small) network is trained for a specific objective while the core model's parameters remain unchanged @xcite . adapters realize modular training with a non-linear feed-forward network added to each layer @xcite @xcite of transformers. several works study the various aspects of adapters, such as parameter efficiency @xcite @xcite , and transfer learning capacity @xcite . recently, @xcite show that scale and shifting of embedding is sufficient for effectively learning the task. bias mitigation. mitigating societal bias in lms is explored particularly in the context of attribute erasure. the aim of this task is to reduce the encoded information of a specific attribute from the latent embeddings and is particularly utilized in the context of mitigating empirical societal biases in lms @xcite . bias mitigation is approached by methods such as linearly projecting embeddings into the space with minimum correlations to protected attributes @xcite , to achieve empirical fairness, through using a distribution alignment loss @xcite , or by applying adversarial training to learn representations agnostic to protected attributes @xcite @xcite @xcite . controllability. decoder lms have been extensively studied for controllability. researchers mostly cover tasks such as attribute manipulation (like positive/negative sentiment), and imposing predefined syntactic/semantic structure to text generation @xcite @xcite @xcite . as examples, @xcite introduces steering vectors as a way of changing the semantics of text generation, while @xcite learn an alignment function to force the text generation in the direction of a specific target concept. more recently, @xcite used likelihood between expert and anti-expert models to detoxify text generation. information retrieval. in ir tasks, many bias mitigation methods have been proposed. these methods mostly use list-wise optimization @xcite . @xcite proposed integrating adversarial training in deep-ranking models to improve bias mitigation. @xcite introduced bias-aware optimization method using coder @xcite with tas-b @xcite . we will use their method as the training strategy for our ir fairness task 2.2. few recent studies explore modularized adversarial bias mitigation of encoder language models. @xcite use a stack of adapters, while @xcite first learns separate adapters for tasks and debiasing attributes and then combine them on-demand using the fusion network. utilizing masking methods, @xcite learns binary masks applied to the initial network to erase the concept of interest, and @xcite train sparse weight-difference subnetworks, one for each attribute, which can be added to the core model on-demand. our work extends this line of research by introducing a modularized, graded (non-binary), and controllable approach evaluated on continuous concept erasure. finally, the gating mechanism has been used in various architectures to learn via scaling. as examples, @xcite propose the self-gate activation function with trainable parameters, while @xcite introduce the tempered sigmoid activation with a bias and scaling factor. @xcite introduced squeeze and excitation networks which use bottle-neck networks added after each convolutional layer followed by a sigmoid activation function. compared to squeeze and excitation which captures global attention to the channels, our model uses a second training signal to isolate and filter out protected attributes. another difference between our method and squeeze and excitation is the usage of a new activation function instead of a sigmoid activation function which gives us the benefit of controllability at inference. ## model and training in this section, we first introduce the proposed controllable gate adapter (congater) and trajectory-sigmoid (t-sigmoid) activation function. we then explain the parallel and post-hoc training regimes, and how the gating sensitivity parameter of t-sigmoid can be adjusted at inference time to control the effectiveness of the gates. ## experiment setup datasets we conduct our classification experiments on three datasets: the bios (de-arteaga et al., 2019) dataset which contains short biographies used to predict a person's job. the name and any indication of the person's gender in the biography are omitted. the dataset labels are 28 occupations for the task, and two protected attribute classes (female/male). the second dataset is fdcl18 (founta et al., 2018b) for hate speech detection, containing a set of tweets each classified as hateful, abusive, spam, or normal. following previous studies @xcite , we assign race dialect labels of african american and white american to fdcl18 using the probabilistic model developed by @xcite . the third dataset is pan16 @xcite containing a set of tweets accompanied by the labels of gender and age of the authors. the task's objective is to predict whether another user is mentioned in a tweet. pan16 provides the binary task classes of mention, and no mention, two gender labels, and five age groups. for the ir task, we use the fairness-sensitive queries dataset msmarco f air @xcite which contains 215 queries. the queries are from the msmarco passage retrieval collection @xcite , with 8,841,822 passages. we use 158 words related to each of the two protected attribute classes (female/male) following @xcite , to calculate the neutrality of each document following @xcite . the details of the neutrality criteria can be found in appendix d. ## results and discussion we start our discussion by going through results of the classification tasks, and continue discussing our observations on the search result experiment. ## conclusion we introduce congater, a gated module enhanced with a novel controllable activation function, which enables continuous adjustment for the information flow of an attribute at inference time. we conduct 13 experiments spanning 2 tasks (classification and information retrieval) 4 datasets, and 4 models to remove attribute information (classification) and enhance fairness of the model (ir). our results show that congater can successfully isolate and filter out attribute information with the least harm to task performance. in addition, we showed that congater is able to continuously traverse between the biased and debiased states, enhancing personalization and interpretability through controllability.