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Found 25 papers, 8 papers with code
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.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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.
Ethical ChatGPT: Concerns, Challenges, and Commandments
Large language models, e. g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population.
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i. e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual.
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
BIG-bench Machine Learning
Explainable artificial intelligence
+2
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.
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation
We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.
KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence
Machine intelligence is very successful at standard recognition tasks when having high-quality training data.
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
's C-indices were 0. 87 and 0. 85 for continuous and discrete grading, respectively, compared to 0. 79 (95%CI 0. 71-0. 86) for GG obtained from the reports.
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Artificial intelligence (AI) has been successfully applied in numerous scientific domains.
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations
In order to build effective and efficient interactive human-AI interfaces we have to deal with the question of how to evaluate the quality of explanations given by an explainable AI system.
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs.
Patch augmentation: Towards efficient decision boundaries for neural networks
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial attacks.
Kandinsky Patterns
Kandinsky Figures and Kandinsky Patterns are mathematically describable, simple self-contained hence controllable test data sets for the development, validation and training of explainability in artificial intelligence.
Human Activity Recognition using Recurrent Neural Networks
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living.
A Deep Learning Approach for Privacy Preservation in Assisted Living
In the era of Internet of Things (IoT) technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments.
The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations
AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important.
What do we need to build explainable AI systems for the medical domain?
In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain.
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points.
Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
In this paper, we describe the concept of entity-centric information access for the biomedical domain.
Augmentor: An Image Augmentation Library for Machine Learning
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting.
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention.
Review of Machine Learning Algorithms in Differential Expression Analysis
In biological research machine learning algorithms are part of nearly every analytical process.
