Project Overview
A model-agnostic explainability layer for mission-critical AI decisions, bridging the trust gap between complex neural networks and human spacecraft operators.
The Problem
As deep learning models became more prevalent in mission control, spacecraft operators refused to trust the 'black box' decisions. When an AI suggested a critical maneuver, operators needed to know exactly *why* before executing it.
Our Solution
We built a comprehensive Explainable AI (XAI) suite that runs alongside any predictive model. It generates human-readable justifications, highlighting the exact telemetry sensors and historical patterns that led to the AI's conclusion.
System Architecture
Utilizing SHAP (SHapley Additive exPlanations) and LIME, the system processes the gradients and activations of the host models in real-time. The explanations are rendered as interactive heatmaps and natural language summaries in the operator dashboard.
Impact & Results
Increased operator adoption of AI recommendations from 22% to 91%. Reduced the time taken for human operators to verify and approve an AI-suggested collision avoidance maneuver by 60%.