ArticleAdvances in neural information processing systems2022
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance.NPJ digital medicine · 2025Article
- AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.European heart journal. Digital health · 2025Article
- Explaining counterfactual images.Nature biomedical engineering · 2025Article
- On the interpretability of part-prototype based classifiers: a human centric analysis.Scientific reports · 2023Article
- CRAFT: Concept Recursive Activation FacTorization for Explainability.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- RISE: an open-source architecture for interdisciplinary and reproducible human-robot interaction research.Frontiers in robotics and AI · 2023Article
- Harmonizing the object recognition strategies of deep neural networks with humans.Advances in neural information processing systems · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical - without much consideration for the human end-user. In particular, it is not yet clear (1) how useful current explainability methods are in real-world scenarios; and (2) whether current performance metrics accurately reflect the usefulness of explanation methods for the end user. To fill this gap, we conducted psychophysics experiments at scale (
Identifiers
37786623PMC10544769What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.