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Found 35 papers, 27 papers with code
Red-Teaming Segment Anything Model
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications.
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases.
Explain to Question not to Justify
Explainable Artificial Intelligence (XAI) is a young but very promising field of research.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Be Careful When Evaluating Explanations Regarding Ground Truth
Evaluating explanations of image classifiers regarding ground truth, e. g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves.
Glocal Explanations of Expected Goal Models in Soccer
This paper introduces the glocal explanations (between local and global levels) of the expected goal models to enable performance analysis at the team and player levels by proposing the use of aggregated versions of the SHAP values and partial dependence profiles.
Explaining and visualizing black-box models through counterfactual paths
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable.
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions.
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Time-to-event prediction, e. g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications.
Challenges facing the explainability of age prediction models: case study for two modalities
The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology.
Performance is not enough: the story told by a Rashomon quartet
The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure.
Performance, Opaqueness, Consequences, and Assumptions: Simple questions for responsible planning of machine learning solutions
With the help of the POCA method, preliminary requirements can be defined for the model-building process, so that costly model misspecification errors can be identified as soon as possible or even avoided.
Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections
To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour.
Explainable Artificial Intelligence (XAI)
Feature Importance
Graph-guided random forest for gene set selection
To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well.
Fooling Partial Dependence via Data Poisoning
We believe this to be the first work using a genetic algorithm for manipulating explanations, which is transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure
In this work, we propose new methods to support model analysis by exploiting the information about the correlation between variables.
Explainable artificial intelligence
Interpretable Machine Learning
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models.
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable.
Landscape of R packages for eXplainable Artificial Intelligence
The growing availability of data and computing power fuels the development of predictive models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
The Grammar of Interactive Explanatory Model Analysis
We conduct a user study to evaluate the usefulness of IEMA, which indicates that an interactive sequential analysis of a model increases the performance and confidence of human decision making.
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Can we train interpretable and accurate models, without timeless feature engineering?
Named Entity Recognition - Is There a Glass Ceiling?
Recent developments in Named Entity Recognition (NER) have resulted in better and better models.
Named Entity Recognition -- Is there a glass ceiling?
A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.
EPP: interpretable score of model predictive power
Second is, that for k-fold cross-validation, the model performance is in most cases calculated as an average performance from particular folds, which neglects the information how stable is the performance for different folds.
Model Development Process
Predictive modeling has an increasing number of applications in various fields.
The Landscape of R Packages for Automated Exploratory Data Analysis
The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis.
Do Not Trust Additive Explanations
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently.
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift.
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
In addition, we have created and published a new dataset that may be used for validation of robustness of a Q&A model.
How much should you ask? On the question structure in QA systems.
Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set.
How much should you ask? On the question structure in QA systems
Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset.
DALEX: explainers for complex predictive models
Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended.
Explanations of model predictions with live and breakDown packages
Complex models are commonly used in predictive modeling.
The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selection
In this article, we introduce The Merging Path Plot - a methodology, and factorMerger - an R package, for exploration and visualization of k-group dissimilarities.
