Design Requirements for Human-Centered Graph Neural Network Explanations
<|reference_start|>Design Requirements for Human-Centered Graph Neural Network Explanations: Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease trust in them as well as deter any collaboration opportunities between the AI expert and non-technical, domain expert. Here, we first discuss the two papers that aim to provide GNN explanations to domain experts in an accessible manner and then establish a set of design requirements for human-centered GNN explanations. Finally, we offer two example prototypes to demonstrate some of those proposed requirements.<|reference_end|>
@article{habibi2024design,
    title={Design Requirements for Human-Centered Graph Neural Network Explanations},
    author={Pantea Habibi, Peyman Baghershahi, Sourav Medya, Debaleena
  Chattopadhyay},
    journal={arXiv preprint arXiv:2405.06917},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06917},
    primaryClass={cs.LG cs.HC}
}