9. Architecture Decisions

Important, expensive, large scale or risky architecture decisions including rationales. With "decisions" we mean selecting one alternative based on given criteria.

9.1. ADR-001: Use of AI for Ad Fulfillment Validation

9.1.1. Status

Proposed

9.1.2. Context

The development of a new ad fulfillment system requires a robust and scalable validation process. Traditional rule-based algorithms may struggle to scale effectively as the system grows, and there is a need for an innovative approach to get ahead of these scalability concerns and maintain a high level of accuracy.

9.1.3. Decision

Adopt using Artificial Intelligence (AI) for ad fulfillment validation. Utilizing machine learning models can enhance the system’s capability to detect subtle patterns, anomalies, and potential fraudulent activities, leading to improved accuracy in identifying valid ad fulfillments.

Diagram

9.1.4. Alternatives

  • 1. No Validation: Do not implement any ad fulfillment validation.

  • 2. Rule-Based Validation: Use rule-based validation algorithms, such as pattern or word matching.

  • 3. Human Review: Implement a manual review process.

9.1.5. Consequences

1. AI Adoption:

Pros

  • Improved accuracy in identifying complex patterns.

  • Adaptive to evolving fraudulent tactics.

  • Potential for automated decision-making.

  • Scalable.

Cons

  • Requires training and fine-tuning of machine learning models.

  • Initial implementation effort and resource investment.

  • Potential for false positives.

2. No Validation:

Pros

  • Minimal implementation effort.

Cons

  • High risk of invalid ad fulfillments.

  • Negative user experience.

  • High risk of fraudulent activities.

3. Rule-Based Validation:

Pros

  • Familiar and easier to implement initially.

  • Minimal training data requirements.

  • Low cost.

  • Potential for false positives.

Cons

  • Limited adaptability to evolving patterns.

  • May result in false negatives for complex scenarios.

4. Human Review:

Pros

  • Human intuition and expertise can provide nuanced evaluations.

Cons

  • Slower response time.

  • Subject to human error.

  • Difficult to scale.

  • High cost.

9.1.6. Next Steps

  1. Conduct a feasibility study to assess the AI model’s training data requirements and potential accuracy improvements.

  2. Evaluate available AI frameworks and tools suitable for ad fulfillment validation.

  3. Develop a pilot implementation to validate the effectiveness of AI in real-world scenarios.