Causal Models in Requirement Specifications for Machine Learning: A vision

Abstract

Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE activity that allows the systematic integration of prior domain knowledge into the design of ML software systems. We propose a workflow to elicit low-level model and data requirements from high-level prior knowledge using causal models. The approach is demonstrated on an industrial fault detection system. This paper outlines future research needed to establish causal modelling as an RE practice.

References in corpus (8)

• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• Underspecification Presents Challenges for Credibility in Modern Machine Learning
• Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study
• Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Controlled Experiment
• Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study
• Engineering problems in machine learning systems
• Identifying Concerns When Specifying Machine Learning-Enabled Systems: A Perspective-Based Approach
• Is it Possible to Disregard Obsolete Requirements? A Family of Experiments in Software Effort Estimation