A pipeline that forces AI to justify decisions before acting (I'm a florist) (github.com)

A newly announced pre-action auditing pipeline aims to enhance decision observability in AI systems by rigorously evaluating how decisions are formed and validated before execution. This deterministic four-phase pipeline runs input scenarios from a CSV file through a series of rule-based evaluations, generating detailed outputs that expose the integrity of decision-making processes at every stage. By assessing decision posture selection, validation, constraint enforcement, and behavioral analysis over time, the system allows developers to identify where unsafe decisions originate and whether they are effectively addressed downstream. This approach is significant for the AI/ML community as it fosters accountability and transparency in automated decision-making processes, an area of growing concern in AI ethics. Unlike traditional output-only evaluations that reveal results after action, this system provides a comprehensive inspection of decision formation, helping developers trace failures back to their roots. It operates on Python 3.11+ and requires no external dependencies, making it accessible for testing with structured scenarios. By collectively analyzing decisions across multiple runs, the pipeline encourages a deeper understanding of how decisions evolve, ensuring ethical standards are upheld in AI applications.