ArticleACM computing surveys2024
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning.
Article in ACM computing surveys, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Evaluating algorithmic fairness in the performance of the AHA PREVENT equations versus pooled cohort equations among single-race and multiracial populations.American journal of preventive cardiology · 2026Article
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- TigCLaF: a cross-lingual large language model framework for sentiment-aware text classification in low-resource tigrigna.Scientific reports · 2026Article
- Empowering Informal Caregivers of Persons With Early-Stage Dementia by Large Language Models: Mixed Methods Evaluation.JMIR formative research · 2026Article
- Artificial Intelligence Research as a Continuous Clinical Service.Mayo Clinic proceedings. Digital health · 2026Review
- Joint explainable and fair AI in healthcare.BMC medical informatics and decision making · 2025Article
- Multimodal prototypical network for interpretable sentiment classification.Scientific reports · 2025Article
- FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport.Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · 2025Article
- A Data Centric HitL Framework for Conducting aSsystematic Error Analysis of NLP Datasets using Explainable AI.Scientific reports · 2025Article
- ACES-GNN: can graph neural network learn to explain activity cliffs?Digital discovery · 2025Article
- LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2025Article
- Robust explanation supervision for false positive reduction in pulmonary nodule detection.Medical physics · 2024Article
- Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI.International journal of geographical information science : IJGIS · 2024Article
- Global explanation supervision for Graph Neural Networks.Frontiers in big data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model's reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs' reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website: https://kugaoyang.github.io/EGL/.
Indexed as
Identifiers
What 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.