# An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers

arXiv: https://arxiv.org/abs/2410.01850 · HTML: https://mlsanity.com/paper/2410.01850/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [Hybrid Convolutional Neural Networks with Reliability Guarantee](https://mlsanity.com/paper/2405.05146/) (2024)

## Built on by (most cited first, 1 of 1)

- [Workflow for Safe-AI](https://mlsanity.com/paper/2503.14563/) (2025)

## Abstract (arXiv)

The generation and execution of qualifiable safe and dependable AI models, necessitates definition of a transparent, complete yet adaptable and preferably lightweight workflow. Given the rapidly progressing domain of AI research and the relative immaturity of the safe-AI domain the process stability upon which functionally safety developments rest must be married with some degree of adaptability. This early-stage work proposes such a workflow basing it on a an extended ONNX model description. A use case provides one foundations of this body of work which we expect to be extended by other, third party use-cases.