Ethical AI Framework
Our binding commitment to building responsible, transparent, and human-centered AI — specifically designed for the social, cultural, and economic realities of South Asia.
Preamble
Project Pak-LLM is not merely a technology initiative — it is a sovereignty statement. We believe that the communities of Pakistan and South Asia deserve AI that understands their languages, respects their values, serves their interests, and is accountable to them. This framework formalizes our ethical obligations and makes them publicly enforceable.
We draw from global AI ethics frameworks (IEEE, EU AI Act, OECD Principles on AI) while adapting them meaningfully to our regional context — including Islamic ethics, Pakistani legal norms, and the lived experiences of Urdu-speaking communities.
Core Ethical Principles
1. Transparency
- We clearly disclose that Pak-LLM is an AI system — it never pretends to be human.
- Model architecture, training data sources, known limitations, and benchmark results will be published in our technical documentation.
- We disclose all third-party providers processing user data (see Privacy Policy).
- AI-generated outputs are always clearly labelled as such within the interface.
- Synthetic media generated via Pak-Image and Pak-Video is embedded with non-obtrusive watermarks signifying AI origins.
2. Fairness & Non-Discrimination
- Training data curation actively guards against perpetuating gender, ethnic, sectarian, or socioeconomic biases.
- The model is evaluated across all major regional languages and dialects — not only Standard Urdu — to ensure equitable performance.
- No user is denied access to the Services on the basis of religion, ethnicity, gender, or political affiliation.
- We conduct regular bias audits and publish findings publicly.
3. Non-Maleficence (Do No Harm)
- Hard-coded guardrails prevent generation of content that promotes violence, terrorism, CSAM, self-harm, or illegal activity — regardless of how the request is framed.
- Prompt injection and jailbreak attempts are detected, blocked, and logged for security analysis.
- The model explicitly refuses to generate malware, phishing content, or information that facilitates unauthorized system access.
- All Pak-Image and Pak-Video generation prompts pass through an automated safety screening pipeline (powered by Llama Guard) before any inference is executed — unsafe prompts are rejected with a clear explanation.
- We do not offer API access to actors we reasonably suspect of harmful intent.
4. Human Oversight & Control
- Pak-LLM is designed as a decision-support tool, never a decision-maker. Final authority always rests with the human user.
- High-stakes domains (medical, legal, financial) are explicitly flagged with disclaimers inside AI outputs.
- An emergency override mechanism allows administrators to halt AI inference across all sessions within minutes.
- A human review queue is maintained for flagged content reports submitted by users.
5. Privacy & Data Sovereignty
- User conversation data is never used to train or fine-tune the model without explicit opt-in consent.
- Data residency options prioritizing Pakistan-based infrastructure are on our active roadmap.
- We enforce strict data minimization — we collect only what is operationally necessary.
- Users retain full ownership of all content they input into the system.
6. Accountability
- A named responsible person (Saad Bin Farrukh, Principal Consultant, SBF Consultancy) is publicly accountable for the system's behavior.
- A responsible disclosure program (security.txt) allows external researchers to report ethical or security concerns.
- We maintain an incident log for significant model failures and publish post-mortem summaries.
- We commit to independent third-party ethical audits as the platform scales.
7. Sustainability & Societal Benefit
- Pak-LLM prioritizes energy-efficient inference architectures (quantized models, LoRA adapters) to minimize environmental footprint.
- The project explicitly targets digital inclusion — reaching communities currently underserved by English-dominant AI systems.
- A portion of commercial revenue will be allocated to open-source Urdu NLP research as the platform becomes profitable.
- We actively partner with educational institutions to make AI literacy accessible across socioeconomic groups.
Synthetic Media Ethics
The introduction of Pak-Image and Pak-Video into the Pak-LLM platform creates specific ethical obligations around AI-generated visual media. The following principles govern our synthetic media systems:
Mandatory Safety Screening
Every image and video generation prompt is passed through an automated content safety pipeline before inference begins. Prompts containing prohibited content (violence, CSAM, deepfake requests of public figures, sectarian incitement, etc.) are rejected with a clear error message. This check runs server-side and cannot be bypassed.
Non-Consensual Deepfake Prohibition
Generating realistic synthetic likenesses of real, identifiable private individuals without their explicit consent is permanently prohibited. This includes face-swapping, voice-cloning requests in video prompts, and any request explicitly naming a real private person in a fabricated scenario.
AI Origin Transparency
All images and videos generated by Pak-Image and Pak-Video are clearly labelled as "AI-Generated" within the platform interface. Users are required by our Terms of Service not to represent generated media as authentic photographs or real footage when distributing it externally.
Islamic & Regional Cultural Compliance
Our prompt safety filters include specific guards for content that violates Islamic ethical norms upheld by Pakistani law, including content that is sexually explicit, blasphemous, sectarian, or contrary to public decency. These filters reflect the cultural values of our primary user community.
Daily Generation Quotas
To prevent misuse at scale and ensure equitable access to compute resources, generation is subject to daily quotas: 5 images and 2 videos per user per 24-hour period (Pakistan Standard Time). Attempts to circumvent quotas through account creation or API manipulation are a violation of our Terms of Service.
Prompt & Generation Logging
Generation prompts are logged server-side for security auditing, content policy enforcement, and abuse detection. Prompts are not used to train or fine-tune models. Logs are retained for 30 days then purged, consistent with our data retention policy.
Absolute Red Lines
The following uses are permanently prohibited regardless of commercial pressure, user requests, or technical feasibility:
Mass surveillance systems targeting civilian populations
Social credit scoring or politically-motivated profiling
Autonomous lethal weapons systems
Generating content sexualizing minors (CSAM)
Deliberate disinformation or propaganda campaigns
Tools designed to suppress political dissent or free speech
Generating non-consensual deepfakes, deceptive political media, or sectarian visual content (under Pak-Image/Pak-Video)
Generating copyrighted materials or violent synthetic media designed to mislead or provoke
OWASP LLM Top 10 Alignment
Our technical implementations directly address the OWASP Top 10 for LLM Applications:
| OWASP Risk | Our Mitigation | Status |
|---|---|---|
| LLM01: Prompt Injection | 11-pattern regex detector, privilege boundary wrapper, hardened system prompt | ✅ Live |
| LLM02: Insecure Output Handling | Outputs rendered as sanitized Markdown; no eval() or dynamic code execution | ✅ Live |
| LLM03: Training Data Poisoning | Curated regional datasets; safetensors model format; provenance tracking | 🔄 In Progress |
| LLM04: Model DoS | Per-user rate limiting (20 req/min), payload size cap (1MB), 20-turn history limit | ✅ Live |
| LLM05: Supply Chain Vulnerabilities | NPM lockfile, SBOM tracking, dependency review on every PR | 🔄 In Progress |
| LLM06: Sensitive Info Disclosure | System prompt is never echoed; user data scoped by user_id in DB | ✅ Live |
| LLM07: Insecure Plugin Design | No plugins or tool-calling in current deployment; sandboxed for future | 📋 Planned |
| LLM08: Excessive Agency | Model can only return text; no external tool calls or code execution | ✅ Live |
| LLM09: Overreliance | Prominent disclaimers on all AI outputs; medical/legal/financial warnings | ✅ Live |
| LLM10: Model Theft | Model weights not publicly exposed; API key rotation; rate limiting | ✅ Live |
Governance & Enforcement
This framework is enforced through three layers: (1) technical guardrails baked into the system at the API level, (2) policy-level Terms of Service that create legally binding obligations for users, and (3) organizational oversight by SBF Consultancy leadership.
We intend to form an independent AI Ethics Advisory Board comprising regional scholars, technologists, and civil society representatives as the platform reaches production scale.
Concerns or violations of this framework may be reported to info@sbf-consultancy.net or via the responsible disclosure channel at /.well-known/security.txt.