Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs

Interpretable AI can match black-box performance: The paper demonstrates that "glass-box" models designed to be transparent and explainable can achieve comparable results to traditional black-box AI systems in reinforcement learning tasks, challenging the assumption that transparency requires sacrificing performance.

Post-hoc explanations have reliability limitations: The authors highlight that most current explainability techniques are "post-hoc methods, which however may be only partially reliable, as they do not reflect exactly the state of the original models," suggesting that explanations added after the fact may not accurately represent how the AI actually makes decisions.

Modular pipelines can decompose AI decision-making: By breaking down the decision process into separate interpretable modules—one for extracting features from raw data and another for reasoning—the approach makes it easier to understand which specific steps lead to particular AI decisions, rather than treating the entire system as a black box.

Raw visual data remains a challenge for interpretability: Even with interpretable approaches, the paper notes that models "struggle when working with raw data, especially when the input dimensionality increases," indicating that making AI systems transparent becomes harder when they must process complex visual information directly.

Performance trade-offs exist in complex scenarios: While the approach works well in simpler settings, "performance degrades in frame-skipping settings," suggesting that achieving both interpretability and reliability may require different solutions depending on task complexity.