Bridging Fairness and Environmental Sustainability in Natural Language Processing (2022)

Abstract: Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of research on the interplay between the two fields. This lacuna is highly problematic, since there is increasing evidence that an exclusive focus on fairness can actually hinder environmental sustainability, and vice versa. In this work, we shed light on this crucial intersection in NLP by (1) investigating the efficiency of current fairness approaches through surveying example methods for reducing unfair stereotypical bias from the literature, and (2) evaluating a common technique to reduce energy consumption (and thus environmental impact) of English NLP models, knowledge distillation (KD), for its impact on fairness. In this case study, we evaluate the effect of important KD factors, including layer and dimensionality reduction, with respect to: (a) performance on the distillation task (natural language inference and semantic similarity prediction), and (b) multiple measures and dimensions of stereotypical bias (e.g., gender bias measured via the Word Embedding Association Test). Our results lead us to clarify current assumptions regarding the effect of KD on unfair bias: contrary to other findings, we show that KD can actually decrease model fairness.

## introduction fairness and environmental sustainability are critical to the future of human society, and, thus, also reflected by the united nations' 17 sustainable development goals (e.g., goal 5: gender equality, and goal 13: climate action). 1 accordingly, both topics are currently also active research areas in natural language processing (nlp). on the one hand, several works have established that language representations are prone to encode 1 https://sdgs.un.org/goals and amplify stereotypical social biases (e.g., @xcite , and, consequently, are a source of representational harm @xcite @xcite . to address this issue and provide fairer language technologies, various approaches have developed methods for measuring bias (e.g., @xcite @xcite as well as debiasing methods (e.g., @xcite . on the other hand, recent advances in nlp have been fueled largely by increasingly computationally expensive pre-trained language models (plms). whereas the original bert base model has 110m parameters @xcite , the switch transformer model, designed as a more efficient alternative to more recent plms, has over a trillion parameters @xcite . while these models consistently obtain superior performance across a variety of nlp benchmarks @xcite , researchers have pointed out the increasing potential co 2 emissions of these models. @xcite estimated that pre-training a bert base transformer @xcite using energy with average u.s. carbon intensity has co 2 emissions comparable to a passenger on a trans-american flight. more recent calculations confirm that the energy consumption of plms continues to grow along with their size @xcite and that consumption at inference time is non-negligible @xcite . these findings have fueled the development of more environmentally sustainable nlp. for instance, tuning only a few new lightweight adapter layers instead of the whole architecture (e.g., @xcite , and compressing models (e.g., @xcite can reduce the energy consumption during training or inference. however, while both fairness and sustainability are active research fields in our community, 2 it is extremely surprising that there is so little work on the intersection of both aspects. we argue that this lack of focus is problematic, as some fairness approaches can jeopardize sustainability and sustainability approaches might hinder fairness. for instance, @xcite propose a data-driven debiasing approach, which requires pretraining a fairer model from scratch. thus, for each and every stereotype, a novel plm must be trained, reducing environmental sustainability. @xcite pointed to potential issues of such an approach and proposed a modular, and therefore more sustainable method. in the other direction, recent work in computer vision has shown that compressed models are less robust, and can even amplify algorithmic bias @xcite . @xcite investigated the relationship between pruning and low-resource machine translation, finding that pruning can actually aid generalization in this scenario by reducing undesirable memorization. however, aside from few works, there has been no systematic research on the interplay between the two fields in nlp. contributions. in this work, we acknowledge the potential for race conditions between fairness and environmental sustainability in nlp and call for more research on the interplay between the two fields. to shed light on the problem and to provide a starting point for fair and environmentally sustainable nlp, (1) we provide a literature overview and systematize a selection of exemplary fairness approaches according to their sustainability aspects. we show that the surveyed approaches require energy at various training stages and argue that fairness research should consider these aspects. (2) based on work suggesting the potential of model compression to increase fairness @xcite , we take a closer look at knowledge distillation (kd; @xcite as an example method targeting the environmental sustainability of language technology. in this approach, a (smaller) student model is guided by the knowledge of a (bigger) teacher model. we extensively analyze the effect of kd on intrinsic and extrinsic bias measures (e.g., word embedding association test (e.g., @xcite , bias-nli @xcite ) across two tasks (natural language inference and semantic similarity prediction). we e.g., sustainlp ( https://aclanthology.org/ 2021.sustainlp-1.0/ , and lt-edi ( https: //aclanthology.org/2022.ltedi-1.0/) ) investigate important kd-factors, such as the number of hidden layers of the student and their dimensionality. contrary to concurrent findings @xcite , we show that kd can actually decrease fairness. thus, fairness in such sustainability approaches needs to be carefully monitored. we hope to inspire and inform future research into fair and environmentally sustainable language technology and make all code produced publicly available at: https://github.com/uhhds/ knowledge_distillation_fairness . ## how fairness can harm sustainability to illustrate the tight relationship between environmental sustainability and fairness in current nlp, we conduct an exemplary analysis of current mitigation approaches for unfair bias. here, our goal is not to conduct an exhaustive survey, but to showcase when, why, and to what extent fairness approaches can be environmentally harmful. ## related work fairness in nlp. there exists a plethora of works on increasing the fairness of nlp models, most prominently focused on the issue of unfair stereotypes in the models (e.g., @xcite @xcite . we only provide an overview and refer the reader to more comprehensive surveys on the topic (e.g., @xcite @xcite . @xcite were the first to point to the issue of stereotypes encoded in static word embeddings, which led to a series of works focused on measuring and mitigating these biases (e.g., @xcite , as well as assessing the reliability of the tests @xcite @xcite @xcite . for instance, @xcite proposed the well-known weat. recent works focus on measuring and mitigating bias in contextualized language representations @xcite @xcite @xcite and in downstream scenarios, e.g., for dialog (e.g., @xcite @xcite , co-reference resolution @xcite , and nli @xcite . similarly, researchers have explored multilingual scenarios (e.g., @xcite @xcite , more fine-grained biases @xcite , and more biases, beyond the prominent sexism and racism dimensions (e.g., @xcite , like speciesist bias @xcite . sustainability in nlp. @xcite have called for more awareness of nlp's environmental impact. reducing the energy consumption can be achieved through efficient pre-training @xcite , smaller models and employing less pre-training data considering the specific needs of the task at hand (e.g., @xcite . if a plm is already in-place, one can rely on sample-efficient methods (e.g., @xcite , or refrain from fully finetuning the model (e.g, @xcite . similarly, one can compress the models via distillation (e.g., @xcite @xcite , pruning (e.g., @xcite @xcite , and quantization (e.g., @xcite , to increase energy-efficiency of later training stages or at inference time. a survey is provided by @xcite . in the area of distillation, researchers have explored distillation in different setups, e.g., for a specific task (e.g., @xcite , on a meta-level (e.g., @xcite , or for a specific resource scenario (e.g., @xcite . other efforts focused on accurate energy and emission measurement and provide tools for monitoring energy consumption (e.g., @xcite . while most research in the area of nlp focuses on reducing operational costs, i.e., carbon emissions due to the energy re-quired to develop and run models, downstream impacts of model deployment stand to have a much larger impact on the environment @xcite . @xcite for a detailed presentation of how machine learning can help to counter climate change more broadly, including a disucussion of nlp applications. bridging fairness and sustainability. to the best of our knowledge, there are currently only few works that are located at the intersection of the two fields in nlp: @xcite proposed to use adapters for decreasing energy consumption during training-based debiasing and increasing the reusability of this knowledge, which has been proven effective by @xcite . recently, the unpublished work of @xcite asks whether compression can improve fairness. in contrast, @xcite find that off-the-shelf distilled models, such as distilbert, exhibit higher biases, but do not provide a systematic evaluation of the effect of kd dimensions. concurrent to our work, @xcite demonstrate similar trends, but focus on gender bias (quantified through a single measure) and the number of hidden layers in the student, only. starting from the assumption that compression can lead to biased models, @xcite propose a fairness-increasing kd loss and demonstrate their baselines to be more biased. in a similar vein, @xcite discuss the robustness of bert compression. in computer vision, researchers have shown that compression exacerbates algorithmic bias (e.g., @xcite . e.g., @xcite demonstrate pruned models to be brittle to out-of-distribution points. @xcite present the most relevant work in this space, exploring the low-resource double-bind: individuals with the least access to computational resources are also likely to have scarce data resources. they find that model pruning can lead to better performance on low-resource languages by reducing undesirable memorization of rare examples. this study represents a valuable step towards better understanding the intersection of fairness and sustainability. in this work, we argue that more research is needed to understand the complex relationships between the two fields. ## conclusion fairness and environmental sustainability are equally important goals in nlp. however, the vast majority of research in our community focuses ex-clusively on one of these aspects. we argue that bridging fairness and environmental sustainability is thus still an unresolved issue. to start bringing these fields together in a more holistic research on ethical issues in nlp, we conducted a two-step analysis: first, we provided an overview on the efficiency of exemplary fairness approaches. second, we ran an empirical analysis of the fairness of kd, as a popular example of methods to enhance sustainability. we find that use of kd can actually decrease fairness, motivating our plea for research into joint approaches. we hope that our work inspires such research on the interplay between the two fields for fair and sustainable nlp.