# Rethinking Certification for Trustworthy Machine Learning-Based Applications

arXiv: https://arxiv.org/abs/2305.16822 · HTML: https://mlsanity.com/paper/2305.16822/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 4 of 4)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Certification of embedded systems based on Machine Learning: A survey](https://mlsanity.com/paper/2106.07221/) (2021)

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

- [Continuous Management of Machine Learning-Based Application Behavior](https://mlsanity.com/paper/2311.12686/) (2023)
- [The Gold in Bias: Maturing the AI Design Process through Verification](https://mlsanity.com/paper/2609.29730/) (2026)

## Abstract (arXiv)

Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness. Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to non-deterministic applications built on ML models. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues, and proposes a first certification scheme for ML-based applications.