Search for: All records

We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION ’s potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption. 

Data models are foundational to the creation of data and any data-driven system. Every algorithm, ML model, statistical model, and database depends on a data model to function. As such, data models are rich sites for examining the material, social, and political conditions shaping technical systems. Inspired by literary criticism, we propose close readings of data models—treating them as artifacts to be analyzed like texts. This practice highlights the materiality, genealogy, techne, closure, and design of data systems. While literary theory teaches that no single reading is “correct,” systematic guidance is vital—especially for those in computing and data science, where sociopolitical dimensions are often overlooked. To address this gap, we introduce the CREDAL methodology for close readings of data models. We describe its iterative development and share results from a qualitative evaluation, demonstrating its usability and value for critical data studies.