Bias Mitigation in Misogynous Meme Recognition: A Preliminary Study
Authors: Gianmaria Balducci, Giulia Rizzi, Elisabetta Fersini (Università di Milano Bicocca, Universitat Politècnica de València, PMI Reboot S.r.l., Universitat Politècnica de València, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper, we address the problem of automatic misogynous meme recognition by dealing with potentially biased elements that could lead to unfair models. In particular, a bias estimation technique is proposed to identify those textual and visual elements that unintendedly affect the model prediction, together with a naive bias mitigation strategy. The proposed approach is able to achieve good recognition performance characterized by promising generalization capabilities.

Introduction
In the context of social media, memes have become popular as a means of expressing irony or opinions on various topics. However, these memes can also perpetuate discriminatory behaviours towards certain groups and minorities. Misogyny, in particular, has gained attention as a form of hateful language conveyed through memes in various ways, such as female stereotyping, shaming, objectification, and violence. While misogyny recognition mechanisms have been widely investigated focusing on textual sources (i.e., tweets) [1, 2, 3, 4], misogynous identification in multimodal settings, and in particular on memes, is still in its infancy. In [5], a few naive unimodal and multimodal approaches have been investigated to understand the contribution of textual and visual cues. Further investigations from the same authors [6] have introduced a multimodal approach that considers both visual (in the form of captioning) and textual information to distinguish between misogynous and non-misogynous memes. Recently, the performance of multiple pre-trained and trained from scratch models have been compared to verify if domain-specific pre-training could help to improve the recognition performance [7]. Independently on the textual, visual or multimodal sources, several authors highlighted how the classification models may be subject to bias that could affect the real performance of the models [8, 9] in a real setting. Most of the investigations propose a few bias estimation metrics and related mitigation policies that are based on a fixed set of seed words to quantify and minimize the bias at the dataset or model level. When dealing with misogynous memes recognition, metrics to estimate the bias and techniques to mitigate it are still missing. To this purpose, we provide the following main contributions: a candidate biased elements identification in a multi-modal setting, focusing on both textual and visual constituents of a meme; a mitigation strategy at training time, named Masking Mitigation, that masks the candidate biased elements to reduce the distortion introduced by their presence. The rest of the paper is organized as follows. In Section 2 a summary of the state of the art is reported. In Section 3 the candidate biased element identification strategy is detailed. In Section 4 the proposed mitigation strategy is presented. In Section 5 the experimental results are reported. In Section 6 conclusion are reported.

Conclusion
This paper addressed the problem of mitigating misogynous meme detection. In particular, a candidate biased element estimation and a corresponding mitigation strategy is proposed to perform fair prediction in a real setting. The proposed approach, validated on a benchmark dataset, achieved remarkable results both in terms of prediction and generalization capabilities, reducing the bias in a significant way.