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Found 12 papers, 10 papers with code
Local Interpretability of Random Forests for Multi-Target Regression
Although model-agnostic techniques exist for multi-target regression, specific techniques tailored to random forest models are not available.
Truthful Meta-Explanations for Local Interpretability of Machine Learning Models
As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent.
An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification
Transformers are widely used in natural language processing, where they consistently achieve state-of-the-art performance.
Local Multi-Label Explanations for Random Forest
Random Forest falls short on this property, especially when a large number of tree predictors are used.
Local Explanation of Dimensionality Reduction
Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data.
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect.
Decision Making
Explainable Artificial Intelligence (XAI)
+4
Conclusive Local Interpretation Rules for Random Forests
LionForests is a random forest-specific interpretation technique, which provides rules as explanations.
VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment.
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
Lack of evaluation and selection criteria also makes it difficult for the end user to choose the most suitable technique.
ETHOS: an Online Hate Speech Detection Dataset
Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms.
Ranked #1 on
Hate Speech Detection
on Ethos MultiLabel
LionForests: Local Interpretation of Random Forests
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance.
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning.
