# Functional trustworthiness of AI systems by statistically valid testing

arXiv: https://arxiv.org/abs/2310.02727 · HTML: https://mlsanity.com/paper/2310.02727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 19 of 19)

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- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2302.12095/) (2023)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)](https://mlsanity.com/paper/2110.05922/) (2021)
- [Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications](https://mlsanity.com/paper/2103.16910/) (2021)
- [Provable Lipschitz Certification for Generative Models](https://mlsanity.com/paper/2107.02732/) (2021)
- [On generalization in moment-based domain adaptation](https://mlsanity.com/paper/2002.08260/) (2020)

## Abstract (arXiv)

The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial Intelligence (AI) Act for the conformity assessment of AI systems. We observe that not only the current draft of the EU AI Act, but also the accompanying standardization efforts in CEN/CENELEC, have resorted to the position that real functional guarantees of AI systems supposedly would be unrealistic and too complex anyways. Yet enacting a conformity assessment procedure that creates the false illusion of trust in insufficiently assessed AI systems is at best naive and at worst grossly negligent. The EU AI Act thus misses the point of ensuring quality by functional trustworthiness and correctly attributing responsibilities. The trustworthiness of an AI decision system lies first and foremost in the correct statistical testing on randomly selected samples and in the precision of the definition of the application domain, which enables drawing samples in the first place. We will subsequently call this testable quality functional trustworthiness. It includes a design, development, and deployment that enables correct statistical testing of all relevant functions. We are firmly convinced and advocate that a reliable assessment of the statistical functional properties of an AI system has to be the indispensable, mandatory nucleus of the conformity assessment. In this paper, we describe the three necessary elements to establish a reliable functional trustworthiness, i.e., (1) the definition of the technical distribution of the application, (2) the risk-based minimum performance requirements, and (3) the statistically valid testing based on independent random samples.