Interpretative Interfaces: Designing for AI-Mediated Reading Practices and the Knowledge Commons
Explainable AI (XAI) interfaces seek to make large language models more transparent, yet explanation alone does not produce understanding. Explaining a system's behavior is not the same as being able to engage with it, to probe and interpret its operations through direct manipulation. This distinction matters for scientific disciplines in particular: scientists who increasingly rely on LLMs for reading, citing, and producing literature reviews have little means of directly engaging with how these models process and transform the texts they generate. In this ongoing design research project, I argue for a shift from explainability to interpretative engagement. This shift moves away from acc...
| Authors | Gabrielle Benabdallah |
|---|---|
| Primary category | cs.HC |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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