Machine Learning Explainability and Uncertainty Quantification to Support Calibration of Trust in Automated Systems

The Explanations in Lunar Surface Exploration (ELSE) capability applies Mosaic ATMrsquo;s Explainable Basis Vectors (EBV) method for explainable machine learning (xML) and likelihood scores approach to uncertainty quantification (UQ) to lunar surface exploration. In Phase I, Mosaic ATM demonstrated the ability to generalize our EBV method from discrete numerical or binary inputs (e.g., wind speed or the presence/absence of rain) to computer vision classification problems. We demonstrated the feasibility of extracting various types of information from within a deep learning model to inform qualitative and quantitative judgments of whether the machine learning (ML) model is trustworthy. Such judgments can help human and automated system users of ML model outputs decide when to trust/distrust, the systemrsquo;s recommendations. Such an approach to appropriately calibrate trust in automated systems is crucial to expanding their use in high risk environments like deep space exploration.In Phase II, we propose to apply the EBV method to classification of lunar terrain features to support trusted autonomy in lunar exploration, to include:Use the EBV method to produce information from within the underlying ML model to support assessment of the veracity of lunar terrain judgment model results.Incorporate EBV explanations and uncertainty quantification (UQ) into a lunar rover analog to demonstrate the ability to inform an automated system of the trustworthiness of the model.Incorporate EBV explanations into a user interface (UI) to demonstrate the ability to support appropriate calibration of human trust in an automated system.Evaluate the ELSE concept and prototype in an analog environment.We have assembled a multi-disciplinary team, partnering with the Universities Space Research Association (USRA) as a research institution and the University of Central Florida (UCF), bringing together experts in lunar exploration, ML, and human-automation interaction.