# Validity in Design Science

arXiv: https://arxiv.org/abs/2503.09466 · HTML: https://mlsanity.com/paper/2503.09466/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [CIRCLE: A Framework for Evaluating AI from a Real-World Lens](https://mlsanity.com/paper/2602.24055/) (2026)
- [ALIGNS: Unlocking nomological networks in psychological measurement through a large language model](https://mlsanity.com/paper/2509.09723/) (2025)
- [Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma](https://mlsanity.com/paper/2603.13294/) (2026)
- [Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support](https://mlsanity.com/paper/2605.09534/) (2026)

## Abstract (arXiv)

Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives the Design Science Validity Framework and a process model for applying it. The framework comprises three high-level claim and validity types-criterion, causal, and context-as well as validity subtypes. The framework guides researchers in integrating validity considerations into projects employing design science and contributes to the growing body of research on design science methodology. It also provides a systematic way to articulate and validate the knowledge claims of design science projects. We apply the framework to examples from existing research and then use it to demonstrate the validity of knowledge claims about the framework itself.