Data journeys: explaining AI workflows through abstraction

Daga, Enrico and Groth, Paul (2023). Data journeys: explaining AI workflows through abstraction. Semantic Web (Early Access).

DOI: https://doi.org/10.3233/SW-233407

Abstract

Artificial intelligence systems are not built on single simple datasets or trained models. Instead, they are built using complex data science workflows involving multiple datasets, models, preparation scripts and algorithms. Given this complexity, in order to understand these complex AI systems, we need to provide explanations of their functioning at higher levels of abstraction. To tackle this problem, we focus on the extraction and representation of data journeys from these workflows. A data journey is a multi-layered semantic representation of data processing activity linked to data science code and assets. We propose an ontology to capture the essential elements of a data journey and an approach to extract such data journeys. Using a corpus of Python notebooks from Kaggle, we show that we are able to capture high-level semantic data flow that is more compact than using the code structure itself. Furthermore, we show that introducing an intermediate knowledge graph representation outperforms models that rely only on the code itself. Finally, we reflect on the challenges and opportunities presented by computational data journeys for explainable AI.

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