Syrian and Levantine Linguistic Inclusion Charter – v0.1

Introduction

This charter defines SAIF’s initial institutional direction for linguistic inclusion in AI infrastructure serving Syria and the Levant. It is policy-oriented and designed to guide phased execution, transparent governance, and long-term continuity, while contributing openly to more representative Arabic and global AI practice in responsible language technology.

Foundational Principle

Linguistic inclusion is treated as core infrastructure quality, not a secondary feature. Public-interest AI systems should represent linguistic realities responsibly, with safeguards for accuracy, fairness, and institutional accountability.

Scope of Linguistic Inclusion

The scope below is representative and non-exhaustive. It is intended to expand in phases as governance, resources, and validated data pipelines mature.

• Phase 1 baseline: Modern Standard Arabic and institutional language use cases.
• Phase 2 expansion: Levantine Arabic dialects with region-aware variation handling.
• Phase 2 expansion: Kurdish linguistic traditions present in Syria (e.g., Kurmanji).
• Phase 2 expansion: Syriac / Assyrian heritage languages.
• Phase 3 inclusion: Armenian communities of the region.
• Phase 3 inclusion: Circassian linguistic heritage.
• Phase 3 inclusion: Turkmen communities.
• Ongoing: Other historically rooted minority languages, explicitly treated as representative and non-exhaustive.

Why This Matters

Language coverage directly affects equitable access, institutional usability, and reliability of public-interest AI services. Inclusion improves model relevance while reducing exclusion risk for historically underrepresented communities.

Community Participation Model

Participation will be structured through guided contribution tracks, expert review, and documented quality controls. Community input is incorporated through transparent intake, validation checkpoints, and policy-aligned governance decisions.

Technical Approach (High-Level)

SAIF applies a phased data-and-evaluation workflow: corpus mapping, source qualification, annotation standards, benchmark design, and iterative model testing. Each phase is constrained by safety, provenance discipline, and measurable quality gates.

Research & Publication Commitment

SAIF commits to publishing high-level methods, progress updates, and lessons learned within responsible disclosure limits. Documentation standards are intended to support reproducibility, external scrutiny, and institutional continuity.

Participation & Contact

Contributors from research, linguistics, engineering, and civic institutions are invited to participate under governance-aligned contribution protocols.

Long-Term Vision

The long-term goal is a durable and inclusive linguistic infrastructure layer for AI systems in Syria and the Levant: institutionally governed, technically rigorous, and progressively representative across communities, while remaining useful to more representative Arabic and global AI practice in responsible language technology.