# Towards Dependability Metrics for Neural Networks

arXiv: https://arxiv.org/abs/1806.02338 · HTML: https://mlsanity.com/paper/1806.02338/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 13 of 13)

- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings](https://mlsanity.com/paper/2201.00572/) (2022)
- [Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study](https://mlsanity.com/paper/2405.20876/) (2024)
- [PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles](https://mlsanity.com/paper/2302.11919/) (2023)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)
- [A Survey of Neural Network Robustness Assessment in Image Recognition](https://mlsanity.com/paper/2404.08285/) (2024)
- [Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains](https://mlsanity.com/paper/2108.07399/) (2021)
- [A causal model of safety assurance for machine learning](https://mlsanity.com/paper/2201.05451/) (2022)
- [Backdoor Mitigation in Deep Neural Networks via Strategic Retraining](https://mlsanity.com/paper/2212.07278/) (2022)
- [Outline of an Independent Systematic Blackbox Test for ML-based Systems](https://mlsanity.com/paper/2401.17062/) (2024)
- [Safe AI -- How is this Possible?](https://mlsanity.com/paper/2201.10436/) (2022)
- [Empowering the trustworthiness of ML-based critical systems through engineering activities](https://mlsanity.com/paper/2209.15438/) (2022)
- [Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems](https://mlsanity.com/paper/2609.24397/) (2026)

## Abstract (arXiv)

Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for NN and other learning-enabled components. In particular, there is an urgent need for an adequate set of metrics for measuring all-important NN dependability attributes. We address this challenge by proposing a number of NN-specific and efficiently computable metrics for measuring NN dependability attributes including robustness, interpretability, completeness, and correctness.