# Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems

arXiv: https://arxiv.org/abs/2006.12453 · HTML: https://mlsanity.com/paper/2006.12453/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [A Set Membership Approach to Discovering Feature Relevance and Explaining Neural Classifier Decisions](https://mlsanity.com/paper/2204.02241/) (2022)
- [A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System](https://mlsanity.com/paper/2206.01722/) (2022)

## Abstract (arXiv)

Machine learning is becoming increasingly important to control the behavior of safety and financially critical components in sophisticated environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limited if any formal guarantees, prevents those explanations from being digestible by the relevant stakeholders (e.g., end users, certification authorities, engineers) with their diverse backgrounds and situation-specific needs. We introduce Fanoos, a framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity. We demonstrate the ability of Fanoos to produce and adjust the abstractness of explanations in response to user requests on a learned controller for an inverted double pendulum and on a learned CPU usage model.