# How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks

arXiv: https://arxiv.org/abs/2009.05835 · HTML: https://mlsanity.com/paper/2009.05835/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 19 of 19)

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- [COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification](https://mlsanity.com/paper/2109.06711/) (2021)
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- [End-to-End Deep Learning for Real-Time Neuroimaging-Based Assessment of Bimanual Motor Skills](https://mlsanity.com/paper/2504.03681/) (2025)
- [Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation](https://mlsanity.com/paper/2204.14255/) (2022)
- [Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography](https://mlsanity.com/paper/2207.09312/) (2022)
- [Cognitive-Motor Integration in Assessing Bimanual Motor Skills](https://mlsanity.com/paper/2404.10889/) (2024)
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- [Airway Skill Assessment with Spatiotemporal Attention Mechanisms Using Human Gaze](https://mlsanity.com/paper/2506.19306/) (2025)

## Abstract (arXiv)

A critical step to building trustworthy deep neural networks is trust quantification, where we ask the question: How much can we trust a deep neural network? In this study, we take a step towards simple, interpretable metrics for trust quantification by introducing a suite of metrics for assessing the overall trustworthiness of deep neural networks based on their behaviour when answering a set of questions. We conduct a thought experiment and explore two key questions about trust in relation to confidence: 1) How much trust do we have in actors who give wrong answers with great confidence? and 2) How much trust do we have in actors who give right answers hesitantly? Based on insights gained, we introduce the concept of question-answer trust to quantify trustworthiness of an individual answer based on confident behaviour under correct and incorrect answer scenarios, and the concept of trust density to characterize the distribution of overall trust for an individual answer scenario. We further introduce the concept of trust spectrum for representing overall trust with respect to the spectrum of possible answer scenarios across correctly and incorrectly answered questions. Finally, we introduce NetTrustScore, a scalar metric summarizing overall trustworthiness. The suite of metrics aligns with past social psychology studies that study the relationship between trust and confidence. Leveraging these metrics, we quantify the trustworthiness of several well-known deep neural network architectures for image recognition to get a deeper understanding of where trust breaks down. The proposed metrics are by no means perfect, but the hope is to push the conversation towards better metrics to help guide practitioners and regulators in producing, deploying, and certifying deep learning solutions that can be trusted to operate in real-world, mission-critical scenarios.