Seeing Through V isual BERT : A Causal Adventure on Memetic Landscapes (2024)

Abstract: Detecting offensive memes is crucial, yet standard deep neural network systems often remain opaque. Various input attribution-based methods attempt to interpret their behavior, but they face challenges with implicitly offensive memes and non-causal attributions. To address these issues, we propose a framework based on a Structural Causal Model (SCM). In this framework, VisualBERT is trained to predict the class of an input meme based on both meme input and causal concepts, allowing for transparent interpretation. Our qualitative evaluation demonstrates the framework’s effectiveness in understanding model behavior, particularly in determining whether the model was right due to the right reason, and in identifying reasons behind misclassification. Additionally, quantitative analysis assesses the significance of proposed modelling choices, such as de-confounding, adversarial learning, and dynamic routing, and compares them with input attribution methods. Surprisingly, we find that input attribution methods do not guarantee causality within our framework, raising questions about their reliability in safety-critical applications. The project page is at: https://newcodevelop.github.io/causality_adventure/

## introduction memes have evolved from spreading humor to being used for disseminating offensive content, necessitating the development of neural multimodal systems to detect such content @xcite . however, these systems often lack transparency, undermining public trust in real-world applications and highlighting the need for interpretability and trustworthiness. while large language models (llms) and vision language models (vlms) could predict offensive memes and provide self-explanations, these explanations are not always faithful to model behavior @xcite . our self-consistency checks ( §appendix b) confirm this issue for offensive meme detection, prompting us to focus on enhancing the reliability of existing multimodal classifiers. current interpretability techniques, such as input attributions (e.g., integrated gradient @xcite ), struggle with implicit content and causality. for implicitly offensive memes, attribution methods fail to capture underlying concepts like 'racism' (refer to figure 1 ). also, they only detect influential features without considering their causal impact @xcite . causality-based techniques like causalm @xcite and amnesic probing @xcite offer solutions but face scalability issues and focus on global rather than local explanations like: 'is adjective important for sentiment analysis' or 'is part-of-speech information crucial for word prediction?' we address these limitations with a novel causal framework integrating visualbert @xcite with causal concepts. our approach extracts implicit context from the meme as a set of causal concepts and uses dynamic routing and adversarial learning to predict meme offensiveness based on both meme content and contribution from causal concepts. quantitative analysis (section 6) shows that traditional interpretability techniques, which rely on correlation, do not always align with causality, emphasizing that correlation does not imply causation. through ablation studies, we demonstrate that our framework bases its predictions on relevant causal concepts, enhancing trustworthiness. qualitative analysis (section 7) indicates whether the model's decisions are justifiable and explains its error cases. our proposed framework is novel, modelagnostic, and acts as a proof-of-concept which demonstrates the potential of using causal analysis to elucidate the decision-making process of multimodal classifiers. ## experimental setups the experimental setups and dataset details are elaborated in the appendix section f due to space constraints. here we introduce various metrics and baselines. ## analysing the model through causal lens is the model always right due to the right reason? no, it is not always the case. we gain insight into this by examining the model causally. in figure 6 , we present two examples of memes from the test set along with the model's predictions, both correctly classified as offensive. in the first example, the meme's offensiveness is linked to the holocaust and antisemitic ideas, accurately reflected in the top-5 causal concepts. notably, concepts like 'violence', 'holocaust', and 'nazism' from the gold standard set directly relate to the meme's context. with two out of the top-5 causal keywords aligning with the meme's context, we conclude that the model's prediction was correct for the right reason. in the second example, despite the meme being antisemitic, the identified causal concepts are irrelevant to antisemitism, although highly offensive. however, certain keywords (such as 'holocaust' and 'genocide') identified by gradientshap are attributed. three of these ('racism', 'holocaust', and 'genocide') directly relate to the meme's context. initially, it may seem the model was right for the right reason. however, the absence of overlap between the set of causal concepts and the gold standard concept set indicates that the model classified the meme correctly but with erroneous causal attribution. this discrepancy suggests that although accurate input attributions exist, the model may base its decision on different causal concepts. such analyses can help a user to trust the model predictions. ## error analysis in this section, we analyze the model's performance using causal concepts. for example, in the first row of figure 5 : memes of relevant keywords x cau x attr violence racism holocaust gore nazism indecency anti-muslim genocide terrorism terrorism holocaust, nazism, genocide, extremism x cau x attr i.r. indecency immoral racism anti-muslim holocaust politics genocide misogynistic misogynistic meme as offensive, indicating a dataset-wide bias towards words like 'mohammed'. similarly for the second meme, the mere presence of the concept of 'blackness' was sufficient for classifying the meme as offensive despite having nothing related to racism in it. this also shows dataset bias where lots of racists (∼ 47.1%) and religious memes (∼ 39.3%) are present. similarly, in the third meme, the word 'jew' prompts the model to associate the meme with concepts like 'violence', and 'genocide' possibly due to insufficient visual context and association of the word jew in antisemitic offensive memes. 4 in the fourth meme, although offensive, the model finds it humorous due to a lack of background knowledge, especially regarding the wordplay on spongebob, and the smiling spongebob face may further contribute to misclassification. the fifth meme's misclassification can be attributed to a humorous background image unrelated to the meme text, creating a modality conflict. note that the fourth and fifth memes which are classified as non-offensive have the 'funny' keyword as a part of the top 5 causal keywords. the sixth meme shows a sexually offensive re-mark but due to conflicting visual modality (showing violence), this meme got misclassified as non offensive. the keywords generated can also be attributed to dataset bias as shown in the first two memes. by examining causal keywords alongside model inputs, this type of error analysis offers insights into why the model made mistakes. ## conclusion in this paper, we introduce a multimodal causal framework aimed at transparently analyzing visual-bert predictions. guided by an scm, the framework compels visualbert to base its decisions on interpretable human-understandable concepts. evaluation on the facebook hateful meme dataset reveals key insights: i) input attribution methods may lack causal underpinning, ii) modelling choices significantly influence relevant causal attributions, enhancing model trustworthiness. the qualitative analysis delves into whether the model is 'right for the right' reasons and uncovers causal factors behind misclassifications. the simplicity and versatility of our framework (i.e. the underlying structural causal model and its translation to modelling choices) allow its application across various tasks and multimodal models. although we show the importance of our architecture on meme offensive detection tasks as a testbed, its application may be important in medicine where the need for trustworthy systems is paramount.