# Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform

arXiv: https://arxiv.org/abs/2303.13151 · HTML: https://mlsanity.com/paper/2303.13151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Black-box Explanation of Object Detectors via Saliency Maps](https://mlsanity.com/paper/2006.03204/) (2020)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems](https://mlsanity.com/paper/1908.02134/) (2019)

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

- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)

## Abstract (arXiv)

For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.