Masking Actor Information Leads to Fairer Political Claims Detection (2020)

Abstract: A central concern in Computational Social Sciences (CSS) is fairness: where the role of NLP is to scale up text analysis to large corpora, the quality of automatic analyses should be as independent as possible of textual properties. We analyze the performance of a state-of-the-art neural model on the task of political claims detection (i.e., the identification of forward-looking statements made by political actors) and identify a strong frequency bias: claims made by frequent actors are recognized better. We propose two simple debiasing methods which mask proper names and pronouns during training of the model, thus removing personal information bias. We find that (a) these methods significantly decrease frequency bias while keeping the overall performance stable; and (b) the resulting models improve when evaluated in an out-of-domain setting.

## introduction in recent years, nlp methods have found increasing adoption in the social sciences as part of the movement towards computational social sciences or css @xcite . an important part of the appeal of css is the promise to scale up the amount of data under consideration: from what can be annotated manually to what can be analyzed automatically, typically an increase by several orders of magnitude, enabling a paradigm shift towards new research questions @xcite . however, this shift comes with new challenges: if the analyses are carried out by a machine, how can we trust that any outcomes really stem from the underlying data, rather than from processing artifacts? consequently, css must be crucially interested in the algorithmic fairness or (absence of) bias of the underlying machine learning methods (e.g., @xcite . however, work on this topic in nlp over the last years has found angela merkel called for swift tax cuts. ## frequency bias and debiasing we carry out an analysis of the predictions of our claim detector on the mardy dataset with 10-fold cv to maximize the amount of data under consideration. we group the actors into three frequency bands using the gold standard actor annotation, as shown in note that the claim detection model should only pay attention to mentions of actor to the extent this helps in its task. its sensitivity to actor frequency indicates that the presence of a previously seen actor name is a strong indicator for the presence of a claim. we nevertheless believe that this is an undesirable situation, since it means that the model extracts a systematically biased set of claims from the corpus: claims made by frequently mentioned actors (such as office holders or spokespersons) are reinforced, while claims made by infrequently mentioned actors are disregarded. this type of bias can lead to 'echo chambers' @xcite and confers overly high visibility onto frequent actors @xcite . to avoid exactly this type of bias, discourse analysis in social science generally factors out the 'newsworthiness' of claims by disregarding its number of mentions. we computationally debias our claims classifier. ## experiment 1: in-domain modeling we first investigate the effect of frequency debasing in a standard in-domain setting, re-using the setup from section 3 (10-fold cross-validation, claimlevel evaluation) to train one standard and four debiased models. next, we repeat the analysis by frequency band on the set of local claims from section 3 for all 5 the difference between the f-score reported here for standard and the one from section 2 is the difference between token-level f1-score and claim-level f2-score evaluation. we believe that claim-level evaluation provides a more meaningful evaluation of claim identification but have reported token-level evaluation above for comparison to previous work. regarding the precise metric, weighting recall higher then precision provides a better match for a semi-automatic setup with manual post-correction @xcite , which is arguably necessary at the present level of performance. regarding a more qualitative understanding of the actor masking methods, consider the following claim which was recognized by both debiased models but not standard: der dresdner superintendent christian behr ruft zu nächstenliebe und dialogbereitschaft auf. (dresden superintendent christian behr calls for charity and readiness for dialog.) (1) we also see improvements for actors realized as general noun phrases (which are almost guaranteed to occur infrequently): anwohner und npd-politiker protestierten gegen die geplante unterkunft. (local residents and npd politicians protested against the planned accommodation facilities.) ## experiment 2: out-of-domain modeling we now carry out a second experiment following the intuition that models relying on less specific features generalize better to out-of-domain datawhich was also the original motivation for denoising autoencoders @xcite . as out-ofdomain dataset, we used the akw @xcite corpus. this is another german corpus for the task of political claims identification, which covers the debate on the future of nuclear energy use in germany in the four months after the nuclear disaster of fukushima, japan in march 2011. the dataset contains 828 articles and 934 claims, all associated with one of 348 unique actors. we re-use the frequency bands computed for mardy, under the assumption that it is the frequency distribution in the training data that matters for performance. akw differs from the mardy corpus in the subject of the debate, the time span, and the newspapers (die welt and süddeutsche zeitung). we used akw solely as test set for models trained on mardy. ## conclusion this paper has discussed the task of political claims analysis as an example of computational social science where nlp methods are finding adoption to scale analysis to large data sets. we have argued that this scenario must be aware of systematic biases in the output of the nlp methods. the nlp community has mostly focused on biases grounded in extralinguistic reality, e.g., gender @xcite @xcite , race @xcite , or age @xcite . we identified frequency as a language-internal bias present in a current neural model in political claims analysis. it warrants the same kind of attention as other bias types: lower recall for infrequent actors is inherently unfair, hitting those who can afford least to have their contribution overlooked. we compared two approaches to mitigating frequency bias in political claims detection and tested them on in-domain and out-of-domain settings. we found that a simple data modification strategy does as good as or better than modifying the model objective. actor masking improves recall for infrequent actors without affecting overall performance, and, as a side benefit, also improves out-of-domain generalization. while we only evaluated the strategy on one model, we believe its benefits carry over to other model architectures and similar tasks. clearly, actor frequency is only one of a large number of potential frequency-related biases. since frequency is known to be strongly correlated with performance in machine learning-based nlp, such biases should be investigated more systematically in areas building on nlp such as computational social sciences. to remove these biases, however, presumably more sophisticated methods will be necessarily in the general case.