AI Dataset Auditing & Theological Red-Teaming

AI Dataset Auditing & Theological Red-Teaming: Ensuring Epistemic Safety in Generative AI

Mainstream Large Language Models (LLMs) are currently industrializing counterfeit authority across digital platforms and social media. Because these models generate text that appears highly polished and grammatically correct, users trust the outputs blindly, unaware that the underlying data contains critical linguistic, historical, and theological errors that require specialized training to verify.

Most concerning is the rise of "frankenstein citations." Mainstream AI models consistently mix the names of legitimate classical scholars and actual historical text titles with completely fabricated rulings (fatwas), corrupted translations, or fictional content.

Ordinary automated filters and generalist data annotators miss these deep-level systemic errors. I offer elite dataset auditing, advanced text-critical validation, and adversarial red-teaming to eliminate these hallucinations at the foundational level.

Core Consulting Solutions

1. Adversarial Theological Red-Teaming

I systematically probe and stress-test language models to expose hidden vulnerabilities, biases, and factual degradation before deployment. By intentionally prompting models with complex theological inquiries, I isolate and flag deceptive citations, ensuring the model maintains strict structural alignment with authoritative source hierarchies (Hadith, Tafsir, Fiqh).

2. Dataset Auditing & Factual Grounding

I conduct rigorous, human-in-the-loop (HITL) evaluations of training data and pre-training corpora. This process ensures absolute factual grounding, verifying that classical transmissions, historical timelines, and legal precedents are accurately represented and cleanly decoupled from synthetic "AI slop."

3. Classical Arabic (Fusha) Register Analysis

Mainstream models frequently degrade when transitioning between Modern Standard Arabic (MSA), regional dialects, and Classical/Quranic Arabic syntax. I analyze cross-lingual data mappings and translation registers to ensure that the nuanced semantic, morphological, and rhetorical structures of classical texts are preserved without contamination.