arXiv:2606.29878v1 Announce Type: new Abstract: Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value. We propose Decision Value Attribution (DVA), a Shapley-based framework for attributing the value of a fixed prediction--optimization pipeline. The framework d
The increasing integration of AI models into critical operational systems highlights the immediate need for robust explainability methods that address decision outcomes, not just predictions.
This framework offers a method to understand and attribute the value of AI-driven decisions, which is crucial for building trust, ensuring accountability, and optimizing complex predict-then-optimize systems.
Traditional AI explanation methods are now augmented by a framework specifically designed to explain the 'why' behind an AI system's ultimate decision, rather than just its forecast.
- · AI ethicists
- · Developers of predict-then-optimize systems
- · Industries with complex operational decisions
- · Opaque AI decision systems
- · Traditional explanation methods in operational AI
Improved debugging and auditing capabilities for AI-driven operational systems will emerge.
Increased regulatory scrutiny and requirements for explainable decisions in critical AI applications could result.
New standards for AI system validation focusing on decision-value attribution rather than just predictive accuracy may be established.
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Read at arXiv cs.LG