Technical Partnerships
Technical Collaboration Opportunities
We seek partnerships with NLP engineers, data scientists, software developers, and security experts to build the algorithms and systems that operationalize the vulnerability prediction framework.
What We Offer: Novel computational challenges with real-world social impact, co-authorship on technical publications, potential equity/licensing if tools commercialized.
What We Need: Expertise in NLP (moral disengagement detection), machine learning (VI calculation), pattern recognition (EMM tactics), cryptographic systems (evidence generation).
Technical Challenges
NLP – Moral Disengagement Detection
Challenge: Train models to detect Bandura’s 8 moral disengagement mechanisms in institutional communications
Methods: BERT-based classification, transfer learning, active learning with human-in-the-loop
Target: F1 > 0.80 across all 8 mechanisms
Pattern Recognition – EMM Tactics
Challenge: Identify manipulation patterns (compression, pump/dump, grind, loop, flush) in document sequences
Methods: Time-series analysis, anomaly detection, sequence modeling (LSTM/Transformers)
Target: 80% detection accuracy with <10% false positive rate
VI Algorithm Development
Challenge: Compute Vulnerability Index from multidimensional assessments (philosophical coherence, value stability, SWLS, CAPS)
Methods: Factor analysis, composite scoring, machine learning for weight optimization
Target: VI predicts grinding outcomes with AUC > 0.80
DDI Quantification Engine
Challenge: Real-time calculation of Digital Dignity Index from uploaded documents
Methods: Document parsing, NLP tactic detection, procedural burden metrics, composite scoring
Target: <5 minute processing time for typical claim file (50 documents)
Cryptographic Evidence Systems
Challenge: Tamper-proof documentation with legal admissibility
Methods: SHA-256 hashing, blockchain anchoring, chain of custody logging, digital signatures
Target: Meet Federal Rules of Evidence standards for electronic records
Causal Inference Modeling
Challenge: Demonstrate VI×DDI multiplicative interaction predicting outcomes
Methods: Propensity score matching, instrumental variables, structural equation modeling
Target: Establish causal (not just correlational) evidence for VI×DDI→outcome pathway
Collaboration Models
1. Algorithm Development Partnership
Structure: Technical specialist(s) develop specific algorithms (e.g., NLP models for moral disengagement)
Requirements: Relevant technical expertise, access to computational resources, 10-20 hours/week commitment
Outputs: Working algorithm, technical documentation, co-authored paper, potential IP licensing
Timeline: 6-12 months per algorithm
2. System Architecture Partnership
Structure: Build complete platform integrating VI calculation, DDI engine, evidence generation
Requirements: Full-stack development expertise, UI/UX design, database architecture, security engineering
Outputs: Deployable system, user documentation, potential startup formation
Timeline: 12-24 months
3. Academic Research Collaboration
Structure: Computer science faculty/students develop algorithms as research projects
Requirements: University affiliation, research lab resources, IRB if human subjects data
Outputs: Publications, student theses, validated models
Timeline: Varies (6 months – 2 years)
4. Technical Consultation
Structure: Expert advises on specific technical challenges (e.g., NLP architecture decisions)
Requirements: Domain expertise, flexible availability for consultations
Outputs: Technical recommendations, acknowledgment in publications/patents
Timeline: Ongoing as needed
Technical Stack & Resources
Data & Training Materials
- Labeled Dataset: ~500 institutional documents annotated for moral disengagement mechanisms (requires NDA)
- Case Files: De-identified grinding campaign documentation for pattern training
- Assessment Data: Simulated VI/DDI/NAI data for algorithm development (real data pending IRB approval)
Preferred Technologies
- NLP: Python, Transformers library (HuggingFace), BERT/RoBERTa models, spaCy
- ML: scikit-learn, PyTorch, TensorFlow, pandas, numpy
- Backend: Python (Django/Flask) or Node.js, PostgreSQL, Redis
- Frontend: React, TypeScript, Tailwind CSS
- Security: OpenSSL, blockchain APIs (Ethereum/Hyperledger), AWS KMS
- Deployment: Docker, Kubernetes, AWS/GCP/Azure
Computational Requirements
- NLP Training: GPU access (NVIDIA V100/A100 or equivalent), 32GB+ RAM
- Model Deployment: API infrastructure, load balancing for scale
- Data Storage: Encrypted databases, HIPAA-compliant hosting if clinical data
Technical Partnership Benefits
For Individual Developers
- Work on meaningful social impact project (not just another ad optimization algorithm)
- Novel technical challenges (moral disengagement NLP, vulnerability prediction)
- Portfolio-building work with real-world deployment potential
- Co-authorship on technical publications
- Potential equity stake if platform commercialized
For Tech Companies/Startups
- Access to novel IP (patent-protected algorithms)
- First-mover advantage in institutional manipulation detection market
- Corporate social responsibility alignment (consumer protection, disability advocacy)
- Licensing opportunities for validated tools
For Academic Researchers
- Publication opportunities in AI ethics, computational social science, NLP
- Student thesis/dissertation projects with real-world impact
- Grant funding potential (NSF CISE, NIH informatics, private foundations)
- Interdisciplinary collaboration (CS + psychology + philosophy + law)
Technical Partnership Inquiry
Next Steps After Submission:
- Acknowledgment within 48 hours
- NDA execution (for access to training data and algorithm specs)
- Technical deep-dive consultation
- Project scoping and resource planning
- Development kickoff or ongoing collaboration setup
Questions? Email technical@disrupttheloop.com