Towards a Unified Framework for Evaluating Explanations
<|reference_start|>Towards a Unified Framework for Evaluating Explanations: The challenge of creating interpretable models has been taken up by two main research communities: ML researchers primarily focused on lower-level explainability methods that suit the needs of engineers, and HCI researchers who have more heavily emphasized user-centered approaches often based on participatory design methods. This paper reviews how these communities have evaluated interpretability, identifying overlaps and semantic misalignments. We propose moving towards a unified framework of evaluation criteria and lay the groundwork for such a framework by articulating the relationships between existing criteria. We argue that explanations serve as mediators between models and stakeholders, whether for intrinsically interpretable models or opaque black-box models analyzed via post-hoc techniques. We further argue that useful explanations require both faithfulness and intelligibility. Explanation plausibility is a prerequisite for intelligibility, while stability is a prerequisite for explanation faithfulness. We illustrate these criteria, as well as specific evaluation methods, using examples from an ongoing study of an interpretable neural network for predicting a particular learner behavior.<|reference_end|>
@article{pinto2024towards,
    title={Towards a Unified Framework for Evaluating Explanations},
    author={Juan D. Pinto, Luc Paquette},
    journal={arXiv preprint arXiv:2405.14016},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14016},
    primaryClass={cs.LG cs.AI}
}