Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.11771v1

Read the source

Source

Plain-Language Brief

This research paper reports early findings from an ongoing study investigating how requirements engineering (RE) practices support explainability in AI-based systems, particularly in the industrial context of Daimler Truck. The authors found recurring challenges across elicitation, specification, and validation steps, indicating that current RE practices may not adequately address explainability requirements.

What the Document Says

  • Explainability is important for AI-based systems, particularly in safety-critical and regulated domains.
  • Prior research has proposed frameworks, patterns, and user-centered approaches for explainability, but there's limited empirical understanding of how existing RE practices support explainability requirements across the RE lifecycle, especially in an industrial context.
  • The paper reports early findings from a study investigating explainability requirements at Daimler Truck using established RE techniques.
  • The study involved eight practitioners at Daimler Truck and used think-aloud protocols and moderated group discussions across requirements elicitation, specification, and validation steps.
  • The preliminary analysis reveals recurring challenges across all steps: conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty.
  • The findings suggest that current RE practices provide limited support for systematically addressing 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.

Key Concepts Mentioned

  • Explainability
  • Requirements Engineering (RE)
  • AI-based systems
  • industrial context
  • Daimler Truck

Claims Made by the Document

  • Current RE practices provide limited support for systematically addressing explainability requirements.

Evidence Used

  • Preliminary analysis of findings from a study investigating explainability requirements at Daimler Truck using established RE techniques.
  • Recurring challenges across elicitation, specification, and validation steps as revealed by the preliminary analysis.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.