Grace Guo, Lifu Deng, et al.
FAccT 2024
We refer to explainability as a system’s ability to provide sound and human-understandable insights concerning its outcomes. Explanations should accurately reflect causal relations in process executions. This abstract suggests augmenting process discovery (PD) with causal process discovery (CD) to generate causal-process-execution narratives. These narratives serve as input for large language models (LLMs) to derive sound and human-interpretable explanations. A multi-layered knowledge-graph is employed to facilitate diverse process views.
Grace Guo, Lifu Deng, et al.
FAccT 2024
Taku Ito, Luca Cocchi, et al.
ICML 2025
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019