MQG4AI Towards Responsible High-risk AI -- Illustrated for Transparency Focusing on Explainability Techniques

AI systems need structured quality checkpoints throughout their lifecycle: The paper proposes "Quality Gates" at different stages of AI development to ensure responsible practices are maintained continuously, rather than only at the end—this approach recognizes that AI systems evolve and require ongoing oversight.

Explainability requires standardized quality evaluation: The research emphasizes that simply making AI systems explainable isn't enough; there must be consistent methods to evaluate whether explanations are actually good quality, which is essential for transparency in high-risk applications.

One-size-fits-all AI guidelines don't work in practice: The paper identifies a gap between generic responsible AI principles and real-world use cases, proposing instead a customizable methodology that can be tailored to specific industry needs while still maintaining ethical and regulatory standards.

Transparency demands alignment across technical, ethical, and regulatory requirements: Effective responsible AI requires bridging three different domains—technical implementation, ethical considerations, and legal compliance—rather than treating them separately, which the proposed approach attempts to do through its lifecycle planning framework.

Explanation quality matters for high-risk AI decisions: By focusing on the explanation stage during model development, the paper highlights that for critical applications (healthcare, criminal justice, finance), the ability to understand why an AI made a decision is a quality requirement that must be actively managed and verified.