Architecture

LazAI’s Data Evaluation and Alignment architecture is built on three interconnected layers, designed to operate seamlessly within its broader blockchain and iDAO governance framework:

1. Data Submission & Metadata Anchoring Layer

This layer handles initial data ingestion, ensuring provenance and basic integrity before evaluation.

• iDAO-Driven Submission: Data contributors (individuals or organizations) submit datasets through their affiliated iDAOs, which act as curators. Each submission includes structured metadata:

  • Source attribution (via cryptographic signatures).

  • Domain tags (e.g., “biomedical,” “financial transactions”).

  • Format specifications (e.g., text, images, tabular data) and preprocessing logs.

• On-Chain Anchoring: A hash of the dataset and its metadata is recorded on the LazChain, creating an immutable audit trail. This hash is linked to the contributor’s DAT token, establishing initial ownership and traceability.

2. Evaluation Engine Layer

This layer executes multi-dimensional data assessment, combining automated checks and community validation.

• Automated Quality Metrics:

  • Integrity Checks: Cryptographic verification of data hashes to detect tampering.

  • Statistical Validity: Tools to measure noise, duplication, and coverage (e.g., for text data, perplexity scores; for images, resolution and labeling consistency).

  • Privacy Compliance: Scans for sensitive information (e.g., PII) using zero-knowledge proofs to ensure compliance with iDAO-defined policies.

• Contextual Alignment Checks:

  • Model Compatibility: For datasets submitted to train specific models (e.g., a sentiment analysis model), the engine verifies alignment with the model’s input schema and objective (e.g., labeling consistency with sentiment labels).

  • Domain Expertise Integration: iDAOs can appoint domain-specific validators to manually review high-stakes data (e.g., clinical trial records), with their assessments weighted via on-chain voting.

• Incentivized Challenges: External “challengers” can submit fraud proofs to dispute evaluation results (e.g., identifying hidden biases in a dataset). Successful challenges earn DAT rewards, while invalid claims result in penalties, ensuring rigor.

3. Alignment & Reward Layer

This layer ties evaluation outcomes to economic incentives and iterative improvement.

• Dynamic Value Scoring: Each dataset receives a Data Quality Score (DQS) based on evaluation results, encoded in its associated DAT token. The DQS influences:

  • Access privileges (e.g., high-DQS data is prioritized for premium AI models).

  • Reward calculations (contributors earn a share of model revenue proportional to their data’s DQS and usage frequency).

• Feedback Loops: Post-deployment, the system tracks how data impacts model performance (e.g., inference accuracy, reduction in hallucinations). This data is fed back into the evaluation engine to refine future assessments—for example, a dataset that improves model accuracy over time sees its DQS increase.

• iDAO Governance: Each iDAO sets its own evaluation policies (e.g., weighting of automated vs. manual checks) via on-chain proposals, ensuring alignment with community values (e.g., privacy-focused iDAOs may prioritize anonymization metrics).

By integrating these layers, LazAI’s Data Evaluation and Alignment architecture transforms fragmented, untrusted data into a verifiable, high-quality asset—one that drives AI innovation while ensuring contributors are fairly rewarded for their contributions.