Exploring Techniques to Detect and Mitigate Non-Inclusive Language Bias in Marketing Communications Using a Dictionary-Based Approach (2023)

Abstract: We propose a new dataset for detecting non-inclusive language in sentences in English. These sentences were gathered from public sites, explaining what is inclusive and what is non-inclusive. We also extracted potentially non-inclusive keywords/phrases from the guidelines from business websites. A phrase dictionary was created by using an automatic extension with a word embedding trained on a massive corpus of general English text. In the end, a phrase dictionary was constructed by hand-editing the previous one to exclude inappropriate expansions and add the keywords from the guidelines. In a business context, the words individuals use can significantly impact the culture of inclusion and the quality of interactions with clients and prospects. Knowing the right words to avoid helps customers of different backgrounds and historically excluded groups feel included. They can make it easier to have productive, engaging, and positive communications. You can find the dictionaries, the code, and the method for making requests for the corpus at (we will release the link for data and code once the paper is accepted).

## introduction language evolves, and appropriate terminology changes as culture and society shift. using inclusive language fosters a culture of inclusion and belonging, helps to create an environment where people of all experiences and backgrounds feel welcome, and reduces negative stereotypes foot_0 . it supports a customer-centric approach by assisting firms in recognizing and connecting with internal and external customers with the utmost respect and kindness. language has the potential to divide people and in academia, industry, and other communities, this has become intensely evident @xcite . some firmly identify with a conventional idea of gender bias, while others take a broader approach, focusing on principles of inclusivity of all bodies and genders cao and daumé @xcite ; @xcite . there's a lot to be gained from taking an aerial view, one that examines the worth of all points of view, as well as the potential harm and missed opportunities that result from a lack of regard for or value for difference. inclusive language takes into account not only gender, but also age, race, ethnicity, culture, sexual orientation, disability, and health status @xcite . we may unintentionally exclude or offend others if we lack information about and sensitivity to certain words or phrases. being aware and mindful of our written and oral communications can help create and nurture a supportive and inclusive environment. a few main areas of preferred language and terminology include race and ethnicity, people with disabilities, gender identity, and idioms. organizations can use preferred language and avoid non-inclusive language as a helpful tool to respond to societal shifts and deliver better products and solutions. the work of manually reviewing the use of noninclusive language in the material that universities, industries, and the public administration generate is too time-consuming for the equality offices that are housed inside these institutions. natural language processing (nlp) technologies offer a promising way to solve the problem of non-inclusive language, saving businesses time and making inclusive language the norm in business settings. but these systems often reflect the same behaviors that businesses are trying to change through diversity and inclusion efforts @xcite ; @xcite ; @xcite ; chakravarthi (2023). on the other hand, the knowledge base shows how organized information can be used along with unorganized data. using techniques from nlp, we created a phrase dictionary and test sentences to automate the detection of non-inclusive sentences. the approach is intended to be applied to documents written in english. our work makes the following contributions: the best-performing model utilized the dictionary and glove @xcite and scored a weighted f1-score of 0.62 for the binary class on a test set consisting of english sentences. the performance of the model was improved as a result of the automatic extension of the phrase dictionary. the fact that the coverage of extended dictionaries did, in fact, increase shows that the words that were automatically added to the corpus improved performance. examples from the dataset for both binary and fine-grained labels are depicted in figure 1 . ## related work formal theories of inclusive language have been asserted as an essential objective for the future development of society, yet there needs to be concrete guidance for their implementation. in the domains of nlp and machine learning, empirical studies have provided evidence for techniques that are effective in recognizing and minimizing the presence of bias, vagueness, and exclusion in datasets and models. moreover, there needs to be more literature on the practical application of these methods within downstream applications @xcite . while there are several works in nlp on gender inclusion @xcite and gender bias @xcite ; @xcite ; @xcite , more research is needed. @xcite introduce winogender schemas and assess rule-based, statistical, and neural coreference resolution algorithms. they discover that the professional forecasts of these algorithms greatly favor one gender over the other. @xcite presented a strategy to eliminate gender prejudice by analyzing the degree to which words are gendered based on the extent to which they point in a particular gender direction. weat, which stands for "the association between two sets of target words and two sets of attribute words," was a metric that was established by @xcite in order to quantify the bias that exists between attributes and targets. blodgett et al. (2020) surveyed 146 papers analyzing different kinds of bias in nlp systems. in their study, it was discovered that (a) most work objectives are frequently imprecise, inconsistent, and devoid of normative reasoning, and (b) most proposed quantitative methodologies for assessing or reducing "bias" are poorly matched to their goals and do not engage with the relevant literature outside of nlp. to assist researchers and practitioners in avoiding these problems, blodgett et al. (2020) presented three guidelines for analyzing "bias" in nlp systems, along with a number of specific study topics for each. these recommendations are predicated on a greater knowledge of the connections between language and social hierarchies, a crucial step in defining a road ahead in our view. how the insensitivity of annotators to dialect differences might contribute to other biases in computerized hate speech detection models, thereby exacerbating harm to minority populations, was studied by @xcite . in particular, african american english (aae) and annotators' assessments of toxicity in current datasets are highly correlated. this bias in annotated training data and the tendency of machine learning models to exacerbate it cause existing hate speech classifiers to frequently mislabel aae material as abusive/offensive/hate speech (high false positive rate) davidson et al. (2019); xia et al. (2020). a number of methods have been proposed for evaluating and addressing biases that exist in datasets and the models that use them @xcite . all the above research deals with only one dimension of the problem but we deal with all the biases ranging from age bias, disability bias, gender bias, and other biases. in our research, we created a phrase dictionary and fine-grained test set to cover these non-inclusive categories. ## dataset the most frequent approach to the problem posed by nlp text classification tasks such as sentiment analysis, hate speech detection, and offensive language identification uses specialized dictionaries, sometimes known as lexicons, in which each word is assigned a proportional weight (positive or negative) based on the attitude it communicates. negation, irony, ambiguity, idioms, and neologisms are just a few examples of the common linguistic subtleties that can make it challenging to exactly create a model for text classification problems. for these procedures to be effective, therefore, the specialists must have access to the raw texts and are often watched during the process. in our work, we create training, testing dataset, and dictionaries to improve the models' performances. for our current research, we collected sentences and phrases from government and other organization guidelines documents and websites. for the test sentences, we gathered sentences from these websites and two annotators manually checked the validity of the sentences. ## methodology our main aim of the dictionary-based approach is to analyze the binary and fine-grained classes from the sentences that are collected and annotated manually and establish the benchmark for this problem. the overall process is shown in figure 2 . the binary types are inclusive & non-inclusive, and the fine-grained classes are age bias, disability bias, other bias & gender bias. these sentences were taken as a test set for our phrase dictionary approach. testing these sentences using the phrase dictionary with different approaches. we created nine different lexicons with dictionary data and word embeddings. we used seven sets of word embeddings to collect more words related to the keywords and bias with the help of gensim downloader foot_8 . using this gensim downloader, we expanded the keywords to create more keywords for the lexicon approaches. we combined each word embedding to the original keywords and predicted with the test set for binary and fine-grained classes. firstly, we took the phrase dictionary and predicted them by comparing them with the sentence in the test data. secondly, we collected the words in word embeddings such as fasttext, word2vec, and glove, and the prediction was made with each embedding add-on with the keywords. lastly, we combined all the words collected from the word embeddings with the keywords and made the prediction. ## results we have tested several different combinations of methods discussed in the previous sections across the test set sentences with lexicon-based sentences. as an evaluation measure, macro and weighted scores for precision, recall, and f1 scores are reported. we have used a phrase dictionary approach and test set sentences in english texts. these tasks are still crucial when dealing with the lexical method. we evaluated the lexical approach classified with test sentences in the previous section and briefly discussed their performance. we used nine different lexical-based dictionaries to classify the english sentences that are named as the test set. for all these experiments, predictions are given in the below dictionary + all word embedding (combined) provided a more accurate prediction of 0.640 in the binary classes, which is predicting as an inclusive and non-inclusive, and fidelity + glove 200 also provided a more accurate prediction of 0.440 in the fine-grained task which is finding as an age bias, other bias, disability bias, and gender bias. other dictionaries also predicted better accuracy, similar to the best-performed method. the accuracy is used to evaluate these results because it calculates the critical metric when assessing the effects of all processes or models. it is a metric that measures how close the predicted values are to the actual values. this is an easy-to-understand metric that compares different methods in the nlp domain. additionally, we can use accuracy to compare different algorithms or methods and data sets with each other. it is also widely used in the evaluation of supervised learning models. ## conclusion we introduce a new phrase dictionary and dataset for non-inclusive sentences at binary and finegrained levels of classification. this pioneering release combines word embedding-derived keywords with government and organizational guideline sentences, annotated for binary and fine-grained categorization. our experiments utilize dictionarybased methods to set performance benchmarks. notably, in binary classification, the dictionary and glove200 combo achieves a high macro f1 score of 0.390. similarly, the fine-grained task sees promise with the dictionary and fasttext fusion, yielding a top macro f1 score of 0.360. moving forward, we plan to enhance our lexicon-based approach by integrating machine learning and fewshot learning techniques for more extensive appli