It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons (2024)

Abstract: We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although prior approaches based on bias classification have struggled to obtain a high accuracy for the task, we are able to develop a useful model for scoring bias by learning to accurately perform pairwise comparisons. We show that we can interpret the parameters of the trained model to discover the words most indicative of bias. We also apply our model in three different settings by studying the temporal evolution of bias in Wikipedia articles, comparing news sources based on bias, and scoring bias in law amendments. In each case, we demonstrate that the outputs of the model can be explained and validated, even for the two domains that are outside the training-data domain. We also use the model to compare the general level of bias between domains, where we see that legal texts are the least biased and news media are the most biased, with Wikipedia articles in between.

## introduction subjective bias as defined by @xcite is that which "occurs when language that should be neutral and fair is skewed by feeling, opinion, or taste (whether consciously or unconsciously)". with the explosion of human generated data on the web, content affected by such subjective bias inform the perspectives and influence the decisions, both political and otherwise, of an increasing number of people. when people are unaware of bias in present in the content they consume, it contributes to the formation of echo chambers and makes it difficult to build consensus for actions for the common good. therefore, it is important to identify and measure this bias and to do so in an explainable manner so as to be trustworthy and easy to verify. currently, this is done manually in several domains: wikipedia editors mark articles and edits as violating neutrality, companies such as allsides (allsides, 2022) provide ratings of bias in the media, and political scientists analyze speeches to study subjective language as expressions of ideological positions. however, such manual analysis cannot scale to the exponentially growing size of web data, hence necessitating the use of automated approaches. machine-learning models that can benefit from the large training data are of particular interest in this regard. the english-language wikipedia is in many ways an ideal source of training data for these models. it has a neutral point of view (npov) policy @xcite , the adherence to which can be used as a measure of unbiasedness (neutrality). the policy requires following principles such as not stating opinions as facts @xcite , not using language that sympathizes with or disparages the subject, etc. wikipedia also has an active community of editors that enforces this policy by making edits to reword or remove problematic content from articles and leaving comments to indicate npov issues. moreover, the data is extensive due to wikipedia's vast collection of articles spanning a wide range of subjects; and the complete revision history of these articles, along with the editors' comments, is accessible to the public. our goal in this work is to develop a model trained on pov-related edits to wikipedia articles that can quantify bias in web documents and study its applicability to wikipedia itself, as well as to domains outside the training data such as news and legal texts. in addition to being reasonably accurate, we also want the model to be interpretable, i.e., we want to use the parameters of the trained model to infer the words indicative of bias and to explain the output of the models. ## datasets we use four datasets in this paper, two of which we collected ourselves. we will now briefly describe the datasets. ## model we now describe the model we propose for bias comparison and scoring. interpretability and computational efficiency are our primary concerns, hence we generally avoid using dnns in the model architecture. nevertheless, to estimate the performance improvements we can expect from using dnns we also build a version of our model that uses them at the feature extraction step (see appendix a). ## evaluation and applications in this section, we evaluate the performance of our models, examine their interpretability, and explore their applications in a variety of domains. some additional analysis is also given in the appendix. ## conclusion in this paper, we developed a simple, interpretable model to score bias in documents by learning from pairwise comparisons. we curated two novel datasets based on wikipedia revision histories to train and evaluate our model. formulating the problem as assigning a real-valued score for bias, rather classifying a text as biased or unbiased, reduces subjectivity and issues of thresholding. we obtain strong performance on a holdout set of pairs of wikipedia revisions. importantly, the model is interpretable: we can score individual words, a feature that an editor might rely upon to quickly identify the most problematic parts of a document that contribute to the bias. the list of globally most biased words contains a convincing list of strong adjectives and terms that tend to express emotions. we explored the predictions of the model over datasets including news articles and law amendments. the bias distributions over the three domains @xcite are quite different, with news the most biased, and laws the least, which can be explained by the policies governing the creation of content in each of them. we also observe that we can score the bias in different news outlets; these scores align well with crowdsourced labelings of bias of these outlets. the model we developed can be integrated into applications to identify, measure, and monitor bias. for instance, one could build a browser extension to enable users to identify bias in online documents and thereby guard themselves against undue influence. authors of documents that are expected to use objective language (such as legal documents or scientific articles) can measure the bias score to guide their writing. wikipedia and news editors could monitor bias as revisions are made to articles so as to take corrective action when needed. ultimately, we expect this work to contribute to better identifying and correcting both deliberate and subconscious bias in online discourse.