Overview
At Blazark Innovations Private Limited (“Blazark,” “we,” “our,” “us”), we are committed to developing, deploying, and maintaining AI and data-driven systems that are ethical, transparent, secure, and accountable. This policy governs all AI initiatives across Blazark and affiliated platforms (Merlin, Lumora, Vishwam, Biomet-Life, Prodromic, NyayKavach).
1. Purpose
- Uphold user trust and social responsibility.
- Comply with applicable laws and ethical standards.
- Minimize bias, misuse, or harm from automation or algorithmic decisions.
2. Scope
- All AI models, data pipelines, and automated decision systems we build or deploy.
- All employees, contractors, and third parties who contribute to or use our AI systems.
- All datasets collected, licensed, or processed for business or research purposes.
3. Core Principles
| Principle | Commitment |
|---|---|
Fairness & Non-Discrimination | No unfair disadvantage based on gender, caste, religion, ethnicity, disability, or socio-economic background; datasets assessed for representation and bias. |
Transparency & Explainability | Every automated decision must be explainable; we maintain documentation for data lineage, model logic, and limitations. |
Accountability & Human Oversight | Material decisions (healthcare, finance, legal) always include human review; responsibility lies with accountable teams. |
Privacy & Data Protection | Compliant with DPDP 2023, GDPR, and internal policies; collect only what’s necessary; anonymize/pseudonymize where possible. |
Security & Robustness | Adversarial testing, drift monitoring, access via RBAC, and comprehensive audit logging. |
Sustainability & Social Benefit | Prioritize social impact, sustainability, and inclusivity across research and deployment. |
4. Data Governance
- Data Minimization: Collect only what is necessary for legitimate purposes.
- Consent & Lawful Processing: Obtain consent where applicable; honor opt-out and deletion requests.
- Quality & Provenance: Validate data for accuracy, authenticity, and bias before training.
- Anonymization & Encryption: Use anonymization/pseudonymization; protect data at rest and in transit.
- Retention & Disposal: Retain only as long as needed; securely dispose thereafter.
5. AI Governance & Review Process
AI Ethics Committee (AIEC)
Oversees AI and data initiatives, reviews ethical impact, and approves high-risk deployments. Membership includes representatives from Data Science, Product, Security, and Legal.
Model Lifecycle Management (MLM)
Checkpoints: Data Review → Model Design → Validation → Bias Audit → Deployment → Post-deployment Monitoring.
Bias & Fairness Audit
Evaluate fairness metrics (e.g., demographic parity, equalized odds); retrain and mitigate when disparities are detected.
Continuous Monitoring
Monitor for drift, hallucinations, and unintended behavior; maintain alerting and human-in-the-loop guardrails.
6. AI System Categorization
| Risk Level | Examples | Governance Level |
|---|---|---|
High-Risk | Healthcare, legal insights, financial scoring | Requires AIEC approval and human-in-loop validation |
Medium-Risk | Marketing recommendations, personalization | Internal bias/performance review prior to release |
Low-Risk | Chatbots, automated analytics summaries | Standard internal QA |
7. Human-Centric Design
- AI augments — not replaces — human decision-making.
- Interfaces clearly indicate when users interact with AI.
- Users can request human intervention or an explanation at any time.
8. Third-Party AI & Data Vendors
- Vendors must adhere to equivalent privacy and security standards.
- All vendors undergo risk assessment prior to integration.
- Vendors provide transparency on data handling, model logic, and update cycles.
9. Incident Management & Reporting
Report any AI-related ethical concern, bias discovery, or data misuse to the AI Ethics Committee:
Reports are treated confidentially and investigated promptly.
10. Compliance & Continuous Improvement
- Annual policy review and updates for major legal or technological changes.
- Mandatory annual training for AI developers and operators.
- Pre-deployment ethical review and ongoing risk scoring.
- Documented results of audits and improvements for transparency.
11. Alignment with Global Standards
- Digital Personal Data Protection Act, 2023 (India)
- EU AI Act (2024)
- OECD Principles on AI (2019)
- NITI Aayog’s Responsible AI for All
- ISO/IEC 42001 (AI Management System)