Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal

2026-07-13

AI Summary

In safety-critical and regulated domains, existing requirements engineering practices struggle to systematically support explainability requirements, facing challenges such as conceptual ambiguity, insufficient expressiveness in specification, and fragmented validation approaches. This study employs a multi-stage qualitative methodology—including think-aloud protocols, facilitated group discussions, and cross-phase analysis of requirements engineering activities—to investigate how Daimler Truck engineers address explainability requirements in real-world projects. For the first time, it identifies explainability challenges that span the entire requirements lifecycle—from elicitation and specification to verification—thereby laying an empirical foundation for an explainable AI (XAI)-oriented requirements engineering framework and addressing the notable gap in practice-driven approaches within this field.

Abstract

Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-centered approaches to support explainability, there is limited empirical understanding of how existing Requirements Engineering (RE) practices support explainability requirements across the RE lifecycle, especially in an industrial context. This paper reports early findings from an ongoing industry-based study investigating how explainability requirements are elicited, specified, and validated using established RE techniques. We conducted a multi-phase qualitative study with eight practitioners at Daimler Truck, employing think-aloud protocols and moderated group discussions across requirements elicitation, specification, and validation steps. Our preliminary analysis reveals recurring challenges across all steps, including conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty. These findings indicate that current RE practices provide limited support to systematically address explainability requirements. The paper contributes empirical insights into step-specific and cross-cutting challenges and outlines a research vision toward developing an empirically grounded RE framework for explainable AI-based systems.

Problem

• Explainability
• Requirements Engineering
• Empirical Study
• Industrial Context
• AI-based Systems

Innovation

• Explainable AI
• Requirements Engineering
• Empirical Study
• Industrial Case Study
• Explainability Requirements