arXiv:2503.04989v2 Announce Type: replace Abstract: Classification of textual data in terms of sentiment, or more nuanced sociopsychological markers (e.g., agency), is now a popular approach commonly applied at the sentence level. In this paper, we exploit the integrated gradient (IG) method to capture the classification output at the word level, revealing which words actually contribute to the classification process. This approach improves explainability and provides in-depth insights into the text. We focus on sociopsychological markers beyond sentiment and investigate how to effectively tra
The increasing complexity and opacity of AI models necessitates new methods for understanding their decision-making processes, particularly in sensitive areas like sentiment and socio-psychological analysis.
Improved explainability in AI models, especially for nuanced semantic markers, enhances trust, enables better debugging, and reveals biases, which is critical for deployment in various high-stakes applications.
AI models can now provide more granular and interpretable insights into why they classify text in certain ways, moving beyond black-box predictions to word-level causal attribution.
- · AI developers
- · Social science researchers
- · Content moderation platforms
- · Ethical AI advocates
- · Developers of uninterpretable AI systems
- · Methods relying solely on aggregate sentiment analysis
Increased adoption of explainable AI (XAI) techniques across various natural language processing applications.
Development of tools that automatically highlight socio-psychological markers within text, influencing content creation and analysis workflows.
New regulatory frameworks for AI explainability in public-facing applications, driven by the ability to scrutinize model decisions at a granular level.
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