{"type":"ScholarlyArticle","title":"SEMANTIFY: Unveiling Memes with Robust Interpretability beyond Input Attribution","authors":[{"name":"Dibyanayan Bandyopadhyay","slug":"dibyanayan-bandyopadhyay","given":"Dibyanayan","family":"Bandyopadhyay","affiliation":"Department of Computer Science and Engineering , Indian Institute of Technology , Patna , India"},{"name":"Asmit Ganguly","slug":"asmit-ganguly","given":"Asmit","family":"Ganguly","affiliation":"Department of Computer Science and Engineering , Indian Institute of Technology , Patna , India"},{"name":"Baban Gain","slug":"baban-gain","given":"Baban","family":"Gain","affiliation":"Department of Computer Science and Engineering , Indian Institute of Technology , Patna , India"},{"name":"Asif Ekbal","slug":"asif-ekbal","given":"Asif","family":"Ekbal","affiliation":"School of AI and Data Science , Indian Institute of Technology , Jodhpur , India"}],"abstract":"Memes, initially created for humor and social commentary, have transformed into platforms for offensive online content. Detecting such content is crucial; however, existing deep learning-based meme offensiveness classifiers lack transparency, functioning as opaque black-box systems. While Integrated Gradient and similar input-attribution interpretability methods exist, they often yield inadequate and irrelevant keywords. To bridge this gap, we introduce SEMANTIFY, a novel system featuring a theoretically grounded multi-step filtering process.  SEMANTIFY extracts meaningful \"tokens\" from a predefined vocabulary, generating a pertinent and comprehensive set of interpretable keywords. These extracted keywords reveal the model's awareness of hidden meanings in memes, enhancing transparency. Evaluation of SEMANTIFY using interpretability metrics, including 'leakage-adjusted simulatability,' demonstrates its superiority over various baselines by up to 2.5 points. Human evaluation of 'relatedness' and 'exhaustiveness' of extracted keywords further validates its effectiveness. Additionally, a qualitative analysis of extracted keywords serves as a case study, unveiling model error cases and their reasons. SEMANTIFY contributes to the advancement of more interpretable multimodal systems for meme offensiveness detection, fostering trust for real-world applications.","doi":"10.24963/ijcai.2024/684","url":"https://ijcai.org/proceedings/2024/684","pdf":"https://ijcai.org/proceedings/2024/0684.pdf","markdown":"https://ijcai.org/proceedings/2024/684.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/684","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":684,"pages":{"first":6189,"last":6197},"keywords":["AI Ethics, Trust, Fairness: ETF: Societal impact of AI","Natural Language Processing: NLP: Interpretability and analysis of models for NLP","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Bandyopadhyay et al., 2023] Dibyanayan Bandyopadhyay, Gitanjali Kumari, Asif Ekbal, Santanu Pal, Arindam Chat- terjee, and Vinutha BN. 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