SAIF — Syria AI Foundation

Evaluation & Benchmarks – v0.1

Purpose

This outline defines a phased evaluation framework for measuring model quality, reliability, and institutional readiness.

Evaluation Principles

Principles include methodological clarity, repeatability, transparent assumptions, and consistency across reporting cycles.

Benchmark Scope (Representative)

Benchmark scope is representative, non-exhaustive, and phased, expanding as validation resources and governance controls mature.

Safety & Robustness Considerations (High-Level)

Evaluation tracks include safety-oriented tests, robustness checks, and monitored failure modes under documented constraints.

Methodology (High-Level)

Methodology combines benchmark definition, test set governance, scoring protocols, and periodic recalibration procedures.

Reporting & Reproducibility

Results are expected to be documented with versioning, metric definitions, and reproducibility notes suitable for external review.

Research & Publication Commitment

SAIF will publish policy-level evaluation updates and benchmark summaries within responsible disclosure boundaries.

Participation & Contact

Researchers and evaluators can contribute to benchmark design, review, and reporting through structured participation channels.