📄 InterXAI White Paper
Reimagining Explainable AI through Intertextual Critique
Version 1.0 – July 2025
Executive Summary
InterXAI is a human-centered platform that bridges Explainable AI (XAI) and intertextual analysis. It empowers users to compare machine-generated model explanations with human interpretations, fostering critical engagement, accountability, and enriched understanding of AI behavior.
Combining computational linguistics, literary methods, and digital humanities, InterXAI offers a hybrid framework for interpretability and critique in machine learning systems.
1. Vision & Purpose
Modern AI systems are increasingly opaque, and existing XAI tools often center the machine’s perspective. InterXAI flips the lens. We ask not just how a model works—but how its outputs are interpreted, challenged, and reframed by human users.
By drawing from hermeneutics, reader-response theory, and intertextuality, InterXAI provides a method to analyze, visualize, and question AI decisions in light of cultural, ethical, and narrative frames.
2. Core Features
🧠 XAI Engine
- Integrates SHAP, LIME, and other ML explanation libraries
- Provides model-level explanations of classification, regression, and generative outputs
- API-ready and extendable to vision and multi-modal models
✋ Human Annotation Engine
- Allows annotators to critique, contextualize, or reframe model outputs
- Supports tagging of metaphors, references, omissions, and alternative interpretations
- Tracks consensus or divergence across multiple readers
🔗 Intertextual Linker
- Links model outputs to external corpora (e.g., Wikipedia, classic texts, media archives)
- Highlights allusions, reused phrasing, or conceptual drift
- Integrates with NLP pipelines for text similarity and citation detection
🧲 Comparison Orchestrator
- Merges machine and human insights side-by-side
- Detects gaps between technical explanations and human critiques
- Generates visual dashboards and exportable insights
3. Use Cases
- Education: Teaching AI literacy and critical thinking via annotated model outputs
- Digital Humanities: Analyzing AI-generated interpretations of historical or literary texts
- Media & Ethics: Auditing content recommendation or moderation explanations
- NLP Research: Comparing generated text explanations with scholarly commentary
- Policy & Compliance: Visualizing accountability in high-stakes automated decisions
4. System Architecture
- Input: Literary, historical, social media, or user-provided texts
- Processing: XAI engine + human-centered annotation layer
- Integration: LLMs, SHAP/LIME, Intertextual APIs
- Export: PDF/HTML/JSON formats for scholarly, public, or audit purposes
Deployment: GitHub Pages + Netlify frontend. Companion Streamlit dashboard supports real-time analysis and comparison.
5. Technical Stack
- Python, Streamlit, SHAP, LIME, Hugging Face Transformers
- Jekyll, GitHub Pages, Netlify (UI & publishing)
- Integration-ready with spaCy, Gensim, Wikidata APIs
6. Contribution & Collaboration
InterXAI is an open, evolving project. You can:
- Submit a case study
- Collaborate with us
- Explore case studies and the blog
7. Future Roadmap
- 🔍 Inter-Annotator Analysis Module
- 🧜 Visualization of Intertextual Threads
- 📃 Dataset Builder for XAI + Human Commentary
- 📊 Dashboard for Influence Mapping
8. Licensing & Ethics
InterXAI values transparency, interpretability, and scholarly openness.
- Code: MIT License
- Research Outputs: Creative Commons (CC-BY-SA)
- Commitments: Ethical AI development & inclusive critique
9. Contact
Project Lead: Felix B. Oke
📧 bfiliks4xt@gmail.com
🔗 GitHub | LinkedIn
🌐 Website: interxai.netlify.app