Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform

TL;DR

This paper applies a quality model based on ISO25000 to a real-world AI platform for wildflower monitoring, demonstrating how to define quality requirements for trustworthy AI systems in practical scenarios.

Contribution

It demonstrates the application of a structured quality model to specify quality requirements for an AI system in a real-life case study, guiding trustworthy AI development.

Findings

The quality model helps define quality requirements for data, model, and software.

Scenarios illustrate use, extension, and improvement of the AI platform.

Future work includes adding metrics, tools, and best practices.

Abstract

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.