bluuebunny/crossref_metadata_2025_split huggingface dataset:
xxAI - Beyond Explainable Artificial Intelligence
Authors: Holzinger Andreas , Goebel Randy , Fong Ruth , Moon Taesup , Müller Klaus-Robert , Samek Wojciech 
Published: April 2022

AbstractThe success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”
XAI and Strategy Extraction via Reward Redistribution
Authors: Dinu Marius-Constantin , Hofmarcher Markus , Patil Vihang P. , Dorfer Matthias , Blies Patrick M. , Brandstetter Johannes , Arjona-Medina Jose A. , Hochreiter Sepp 
Published: April 2022

AbstractIn reinforcement learning, an agent interacts with an environment from which it receives rewards, that are then used to learn a task. However, it is often unclear what strategies or concepts the agent has learned to solve the task. Thus, interpretability of the agent’s behavior is an important aspect in practical applications, next to the agent’s performance at the task itself. However, with the increasing complexity of both tasks and agents, interpreting the agent’s behavior becomes much more difficult. Therefore, developing new interpretable RL agents is of high importance. To this end, we propose to use Align-RUDDER as an interpretability method for reinforcement learning. Align-RUDDER is a method based on the recently introduced RUDDER framework, which relies on contribution analysis of an LSTM model, to redistribute rewards to key events. From these key events a strategy can be derived, guiding the agent’s decisions in order to solve a certain task. More importantly, the key events are in general interpretable by humans, and are often sub-tasks; where solving these sub-tasks is crucial for solving the main task. Align-RUDDER enhances the RUDDER framework with methods from multiple sequence alignment (MSA) to identify key events from demonstration trajectories. MSA needs only a few trajectories in order to perform well, and is much better understood than deep learning models such as LSTMs. Consequently, strategies and concepts can be learned from a few expert demonstrations, where the expert can be a human or an agent trained by reinforcement learning. By substituting RUDDER’s LSTM with a profile model that is obtained from MSA of demonstration trajectories, we are able to interpret an agent at three stages: First, by extracting common strategies from demonstration trajectories with MSA. Second, by encoding the most prevalent strategy via the MSA profile model and therefore explaining the expert’s behavior. And third, by allowing the interpretation of an arbitrary agent’s behavior based on its demonstration trajectories.
Beyond the Visual Analysis of Deep Model Saliency
Authors: Bargal Sarah Adel , Zunino Andrea , Petsiuk Vitali , Zhang Jianming , Murino Vittorio , Sclaroff Stan , Saenko Kate 
Published: April 2022

AbstractIncreased explainability in machine learning is traditionally associated with lower performance, e.g. a decision tree is more explainable, but less accurate than a deep neural network. We argue that, in fact, increasing the explainability of a deep classifier can improve its generalization. In this chapter, we survey a line of our published work that demonstrates how spatial and spatiotemporal visual explainability can be obtained, and how such explainability can be used to train models that generalize better on unseen in-domain and out-of-domain samples, refine fine-grained classification predictions, better utilize network capacity, and are more robust to network compression.
ECQ$$^{\text {x}}$$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
Authors: Becking Daniel , Dreyer Maximilian , Samek Wojciech , Müller Karsten , Lapuschkin Sebastian 
Published: April 2022

AbstractThe remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increases in memory and computational demands make deep learning prohibitive for resource-constrained hardware platforms such as mobile devices. Recent efforts aim to reduce these overheads, while preserving model performance as much as possible, and include parameter reduction techniques, parameter quantization, and lossless compression techniques.In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the quantization clusters, the assignment function additionally considers weight relevances obtained from Layer-wise Relevance Propagation (LRP) and the information content of the clusters (entropy optimization). The ultimate goal is to preserve the most relevant weights in quantization clusters of highest information content.Experimental results show that this novel Entropy-Constrained and XAI-adjusted Quantization (ECQ$$^{\text {x}}$$

x

) method generates ultra low-precision (2–5 bit) and simultaneously sparse neural networks while maintaining or even improving model performance. Due to reduced parameter precision and high number of zero-elements, the rendered networks are highly compressible in terms of file size, up to 103$$\times $$
×
compared to the full-precision unquantized DNN model. Our approach was evaluated on different types of models and datasets (including Google Speech Commands, CIFAR-10 and Pascal VOC) and compared with previous work.
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
Authors: Mamalakis Antonios , Ebert-Uphoff Imme , Barnes Elizabeth A. 
Published: April 2022

AbstractIn recent years, artificial intelligence and specifically artificial neural networks (NNs) have shown great success in solving complex, nonlinear problems in earth sciences. Despite their success, the strategies upon which NNs make decisions are hard to decipher, which prevents scientists from interpreting and building trust in the NN predictions; a highly desired and necessary condition for the further use and exploitation of NNs’ potential. Thus, a variety of methods have been recently introduced with the aim of attributing the NN predictions to specific features in the input space and explaining their strategy. The so-called eXplainable Artificial Intelligence (XAI) is already seeing great application in a plethora of fields, offering promising results and insights about the decision strategies of NNs. Here, we provide an overview of the most recent work from our group, applying XAI to meteorology and climate science. Specifically, we present results from satellite applications that include weather phenomena identification and image to image translation, applications to climate prediction at subseasonal to decadal timescales, and detection of forced climatic changes and anthropogenic footprint. We also summarize a recently introduced synthetic benchmark dataset that can be used to improve our understanding of different XAI methods and introduce objectivity into the assessment of their fidelity. With this overview, we aim to illustrate how gaining accurate insights about the NN decision strategy can help climate scientists and meteorologists improve practices in fine-tuning model architectures, calibrating trust in climate and weather prediction and attribution, and learning new science.
Towards Explainability for AI Fairness
Authors: Zhou Jianlong , Chen Fang , Holzinger Andreas 
Published: April 2022

AbstractAI explainability is becoming indispensable to allow users to gain insights into the AI system’s decision-making process. Meanwhile, fairness is another rising concern that algorithmic predictions may be misaligned to the designer’s intent or social expectations such as discrimination to specific groups. In this work, we provide a state-of-the-art overview on the relations between explanation and AI fairness and especially the roles of explanation on human’s fairness judgement. The investigations demonstrate that fair decision making requires extensive contextual understanding, and AI explanations help identify potential variables that are driving the unfair outcomes. It is found that different types of AI explanations affect human’s fairness judgements differently. Some properties of features and social science theories need to be considered in making senses of fairness with explanations. Different challenges are identified to make responsible AI for trustworthy decision making from the perspective of explainability and fairness.
Logic and Pragmatics in AI Explanation
Authors: Tsai Chun-Hua , Carroll John M. 
Published: April 2022

AbstractThis paper reviews logical approaches and challenges raised for explaining AI. We discuss the issues of presenting explanations as accurate computational models that users cannot understand or use. Then, we introduce pragmatic approaches that consider explanation a sort of speech act that commits to felicity conditions, including intelligibility, trustworthiness, and usefulness to the users. We argue Explainable AI (XAI) is more than a matter of accurate and complete computational explanation, that it requires pragmatics to address the issues it seeks to address. At the end of this paper, we draw a historical analogy to usability. This term was understood logically and pragmatically, but that has evolved empirically through time to become more prosperous and more functional.
Explainable AI Methods - A Brief Overview
Authors: Holzinger Andreas , Saranti Anna , Molnar Christoph , Biecek Przemyslaw , Samek Wojciech 
Published: April 2022

AbstractExplainable Artificial Intelligence (xAI) is an established field with a vibrant community that has developed a variety of very successful approaches to explain and interpret predictions of complex machine learning models such as deep neural networks. In this article, we briefly introduce a few selected methods and discuss them in a short, clear and concise way. The goal of this article is to give beginners, especially application engineers and data scientists, a quick overview of the state of the art in this current topic. The following 17 methods are covered in this chapter: LIME, Anchors, GraphLIME, LRP, DTD, PDA, TCAV, XGNN, SHAP, ASV, Break-Down, Shapley Flow, Textual Explanations of Visual Models, Integrated Gradients, Causal Models, Meaningful Perturbations, and X-NeSyL.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Authors: Molnar Christoph , König Gunnar , Herbinger Julia , Freiesleben Timo , Dandl Susanne , Scholbeck Christian A. , Casalicchio Giuseppe , Grosse-Wentrup Moritz , Bischl Bernd 
Published: April 2022

AbstractAn increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified causal interpretations, and illustrate them with examples. We focus on pitfalls for global methods that describe the average model behavior, but many pitfalls also apply to local methods that explain individual predictions. Our paper addresses ML practitioners by raising awareness of pitfalls and identifying solutions for correct model interpretation, but also addresses ML researchers by discussing open issues for further research.
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
Authors: Salewski Leonard , Koepke A. Sophia , Lensch Hendrik P. A. , Akata Zeynep 
Published: April 2022

AbstractProviding explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generating natural language explanations for VQA, we introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with natural language explanations. For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs. By construction, the CLEVR-X explanations are correct and describe the reasoning and visual information that is necessary to answer a given question. We conducted a user study to confirm that the ground-truth explanations in our proposed dataset are indeed complete and relevant. We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation generation quality for different question and answer types. Additionally, we study the influence of using different numbers of ground-truth explanations on the convergence of natural language generation (NLG) metrics. The CLEVR-X dataset is publicly available athttps://github.com/ExplainableML/CLEVR-X.
Explaining the Predictions of Unsupervised Learning Models
Authors: Montavon Grégoire , Kauffmann Jacob , Samek Wojciech , Müller Klaus-Robert 
Published: April 2022

AbstractUnsupervised learning is a subfield of machine learning that focuses on learning the structure of data without making use of labels. This implies a different set of learning algorithms than those used for supervised learning, and consequently, also prevents a direct transposition of Explainable AI (XAI) methods from the supervised to the less studied unsupervised setting. In this chapter, we review our recently proposed ‘neuralization-propagation’ (NEON) approach for bringing XAI to workhorses of unsupervised learning such as kernel density estimation and k-means clustering. NEON first converts (without retraining) the unsupervised model into a functionally equivalent neural network so that, in a second step, supervised XAI techniques such as layer-wise relevance propagation (LRP) can be used. The approach is showcased on two application examples: (1) analysis of spending behavior in wholesale customer data and (2) analysis of visual features in industrial and scene images.
Towards Causal Algorithmic Recourse
Authors: Karimi Amir-Hossein , von Kügelgen Julius , Schölkopf Bernhard , Valera Isabel 
Published: April 2022

AbstractAlgorithmic recourse is concerned with aiding individuals who are unfavorably treated by automated decision-making systems to overcome their hardship, by offering recommendations that would result in a more favorable prediction when acted upon. Such recourse actions are typically obtained through solving an optimization problem that minimizes changes to the individual’s feature vector, subject to various plausibility, diversity, and sparsity constraints. Whereas previous works offer solutions to the optimization problem in a variety of settings, they critically overlook real-world considerations pertaining to the environment in which recourse actions are performed.The present work emphasizes that changes to a subset of the individual’s attributes may have consequential down-stream effects on other attributes, thus making recourse a fundamcausal problem. Here, we model such considerations using the framework of structural causal models, and highlight pitfalls of not considering causal relations through examples and theory. Such insights allow us to reformulate the optimization problem to directly optimize for minimally-costly recourse over a space of feasible actions (in the form of causal interventions) rather than optimizing for minimally-distant “counterfactual explanations”. We offer both the optimization formulations and solutions to deterministic and probabilistic recourse, on an individualized and sub-population level, overcoming the steep assumptive requirements of offering recourse in general settings. Finally, using synthetic and semi-synthetic experiments based on the German Credit dataset, we demonstrate how such methods can be applied in practice under minimal causal assumptions.
Cryptosystem and Authentication System
Authors: Zheng Zhiyong 
Published: April 2022

AbstractIn 1949, Shannon published a famous paper entitled “communication theory of secure systems” in the technical bulletin of Bell laboratory. Based on the mathematical theory of information established by him in 1948 (see Chap. 3), this paper makes a comprehensive discussion on the problem of secure communication and establishes the mathematical theory of secure communication system. It has a great impact on the later development of cryptography. It is generally believed that Shannon transformed cryptography from art (creative ways and methods) to science, so he is also known as the father of modern cryptography. The main purpose of this chapter is to introduce Shannon’s important ideas and results in cryptography theory, which is the cornerstone of the whole modern cryptography.
Lattice-Based Cryptography
Authors: Zheng Zhiyong 
Published: April 2022

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The extracellular matrix molecule tenascin-C modulates cell cycle progression and motility of adult neural stem/progenitor cells from the subependymal zone
Authors: Schaberg Elena , Götz Magdalena , Faissner Andreas 
Published: April 2022

AbstractAdult neurogenesis has been described in two canonical regions of the adult central nervous system (CNS) of rodents, the subgranular zone (SGZ) of the hippocampus and the subependymal zone (SEZ) of the lateral ventricles. The stem cell niche of the SEZ provides a privileged environment composed of a specialized extracellular matrix (ECM) that comprises the glycoproteins tenascin-C (Tnc) and laminin-1 (LN1). In the present study, we investigated the function of these ECM glycoproteins in the adult stem cell niche. Adult neural stem/progenitor cells (aNSPCs) of the SEZ were prepared from wild type (Tnc+/+) and Tnc knockout (Tnc−/−) mice and analyzed using molecular and cell biological approaches. A delayed maturation of aNSPCs in Tnc−/− tissue was reflected by a reduced capacity to form neurospheres in response to epidermal growth factor (EGF). To examine a potential influence of the ECM on cell proliferation, aNSPCs of both genotypes were studied by cell tracking using digital video microscopy. aNSPCs were cultivated on three different substrates, namely, poly-d-lysine (PDL) and PDL replenished with either LN1 or Tnc for up to 6 days in vitro. On each of the three substrates aNSPCs displayed lineage trees that could be investigated with regard to cell cycle length. The latter appeared reduced in Tnc−/− aNSPCs on PDL and LN1 substrates, less so on Tnc that seemed to compensate the absence of the ECM compound to some extent. Close inspection of the lineage trees revealed a subpopulation of late dividing aNSPCslate that engaged into cycling after a notable delay. aNSPCslate exhibited a clearly different morphology, with a larger cell body and conspicuous processes. aNSPCslate reiterated the reduction in cell cycle length on all substrates tested, which was not rescued on Tnc substrates. When the migratory activity of aNSPC-derived progeny was determined, Tnc−/− neuroblasts displayed significantly longer migration tracks. This was traced to an increased rate of migration episodes compared to the wild-type cells that rested for longer time periods. We conclude that Tnc intervenes in the proliferation of aNSPCs and modulates the motility of neuroblasts in the niche of the SEZ.