Causal Models in Requirement Specifications for Machine Learning: A vision

• URL: http://arxiv.org/abs/2502.11629v1
• Date: Mon, 17 Feb 2025 10:20:17 GMT
• Title: Causal Models in Requirement Specifications for Machine Learning: A vision
• Authors: Hans-Martin Heyn, Yufei Mao, Roland Weiss, Eric Knauss,
• Abstract summary: This vision paper explores causal modelling as an requirements engineering (RE) activity. We propose a workflow to elicit low-level model and data requirements from high-level prior knowledge. The approach is demonstrated on an industrial fault detection system.
• Score: 4.348086726793516
• License:
• 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.

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