# Requirements Engineering for Machine Learning: Perspectives from Data Scientists

## TL;DR

This paper explores the unique requirements engineering challenges for machine learning systems, emphasizing the need for new quality measures, understanding ML-specific performance metrics, and integrating these into RE processes.

## Contribution

It provides initial insights and a methodology framework for requirements engineering tailored to ML-based systems based on interviews with data scientists.

## Key findings

- ML development paradigm shift affects RE practices
- ML-specific quality requirements like explainability are crucial
- Understanding ML performance measures is essential for functional requirements

## Abstract

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.