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Ai Mental Health & Cyber Safety Presentation

Module A - The Normalization Engine

Linguistic Challenge: Roman Urdu lacks standardized orthography (e.g., "kesa" vs "kaisa"), creating orthographic "noise" that significantly degrades the accuracy of downstream AI models.

Technical Role: Acts as a Sequence-to-Sequence (Seq2Seq) transliteration and lexical normalization layer to standardize inputs before analysis.

Model: A specialized transformer architecture, specifically m2m100 fine-tuned on parallel corpora or UrduParaphraseBERT.

Primary Dataset: Roman-Urdu-Parl (RUP). A large-scale parallel corpus of 6.37 million sentence pairs designed to support machine transliteration and word embedding training.

Link: https://arxiv.org/abs/2503.21530

Outcome: Reduces orthographic noise by achieving up to 97.44% Char-BLEU accuracy for Roman-Urdu to Urdu conversion, ensuring Module B receives high-quality "clean" data for risk analysis.

Module B - Risk Stratification (BERT)

Heading: The "Safety Guard": High-Speed Deterministic Detection.

Core Logic: Uses a discriminative transformer architecture (specifically BERT-Mini or XLM-RoBERTa) to classify user intent and risk level rather than predict the next word.

Performance: Achieves 90.12% accuracy in security anomaly detection and 82.45% accuracy in malicious type differentiation. For depression-specific detection, pre-trained BERT models have achieved accuracy rates as high as 99%.

Industrial Utility: Functions as a high-speed "Logic Gate" with ultra-low latency (200–500 ms), which is critical for real-time safety interventions and deciding whether to bypass the generative layer.

Primary Datasets:

  1. RUDA-2025 (Roman Urdu & Urdu Depression Analysis): 25,004 manually categorized posts labeled for four classes: mild, moderate, severe, and non-depression. Link: https://doi.org/10.3390/ai6080191
  2. Urdu Cyberbullying Corpus (Adeeba, 2024): 12,759 annotated tweets categorized into threat, insult, profane, and name-calling; specifically includes patterns for blackmail and coercion. Link: https://www.researchgate.net/publication/380190943_Addressing_cyberbullying_in_Urdu_tweets_a_comprehensive_dataset_and_detection_system

Tasks: Simultaneously identifies Depression Severity and Cyberbullying/Cyber-threat intents to calculate a real-time Risk Index (R).

Module C - Empathetic Voice (Local LLM)

Heading: The "Counselor": Generative Support and Cultural Nuance.

Core Logic: A decoder-only Large Language Model (specifically Llama-3.1-8B fine-tuned into Qalb) optimized for conversational empathy and culturally aligned guidance.

Instruction Tuning: Leverages a "Modified Self-Instruct" pipeline to enhance reasoning and empathy without the prohibitive cost of manual labeling.

Primary Datasets:

  1. Alif Urdu-Instruct: 51,686 systematically curated instruction-response pairs, with 40% containing bilingual or code-mixed content to mirror natural Pakistani communication styles. Link: https://github.com/traversaal-ai/alif-urdu-llm
  2. MentalChat16K: A benchmark of 16,113 question-answer pairs derived from anonymized clinical trial transcripts and synthetic dialogues covering topics like grief, depression, and anxiety. Link: https://github.com/PennShenLab/MentalChat16K

Role: Engages only in "Low" or "Moderate" risk scenarios. It provides non-judgmental, evidence-based support while strictly following safety guardrails to prevent hallucinations in high-stakes clinical contexts.

System Flow Summary

User Input (Roman Urdu / Urdu / Mixed Language)

→ Module A: Normalization Engine (Noise Reduction & Standardization)

→ Module B: Risk Stratification (Real-Time Risk Index Calculation)

→ Decision Layer:

  • High Risk → Safety Escalation (No Generative Response)
  • Low/Moderate Risk → Module C Activation

→ Module C: Empathetic AI Response

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