Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. Many of these decision making systems use machine learning methods that fit complex models to massive datasets. The successful deployment of machine learning (ML) methods to many (critical) application domains crucially depends on its explainability. Indeed, humans have a strong desire to get explanations that resolve the uncertainty about experienced phenomena like the predictions and decisions obtained from ML methods. Explainable ML is challenging since explanations must be tailored (personalized) to individual users with varying backgrounds. Some users might have received university-level education in ML, while other users might have no formal training in linear algebra. Linear regression with few features might be perfectly interpretable for the first group but might be considered a black-box by the latter. We propose a simple probabilistic model for the predictions and user knowledge. This model allows to study explainable ML using information theory. Explaining is here considered as the task of reducing the "surprise" incurred by a prediction. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction, given the user background.
Maastricht University（马斯特里赫特大学）；University of Amsterdam（阿姆斯特丹大学）
Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After…
This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs) have undoubtedly brought great success to a wide range of applications in computer vision, computational linguistics, and AI. However, foundational principles underlying the DNNs' success and their resilience to adversarial attacks are still largely missing. Interpreting and theorizing the internal mechanisms of DNNs becomes a compelling yet controversial topic. This workshop pays a special interest in theoretic foundations, limitations, and new…
Hong Kong Baptist University（香港浸会大学）；Rutgers University（罗格斯大学）
Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we desi…
School of Computer Science (National Pilot Software Engineering School)（计算机科学学院（国家示范性软件工程学院））；Beijing University of Posts and Telecommunications（北京邮电大学）
As student failure rates continue to increase in higher education, predicting student performance in the following semester has become a significant demand. Personalized student performance prediction helps educators gain a comprehensive view of student status and effectively intervene in advance. However, existing works scarcely consider the explainability of student performance prediction, which educators are most concerned about. In this paper, we propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA) by utilizing relationships in student profil…
Nanjing University（南京大学）；School of Intelligent Software and Engineering, Nanjing University (Suzhou Campus)（南京大学（苏州校区）智能软件与工程学院）；Hunan Normal University（湖南师范大学）；Hunan University of Technology and Business（湖南工商大学）；Brunel University London（布鲁内尔伦敦大学）
The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architectur…
Hong Kong Baptist University（香港浸会大学）；Rutgers University（罗格斯大学）
Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
University of California San Diego（加州大学圣地亚哥分校）；ZOZO Research（ZOZO研究）；Waseda University（早稻田大学）；University of Southern California（南加州大学）；Chiba Institute of Technology（千叶科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation
Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算机科学中心（国家济南超级计算中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；University of Technology Sydney（悉尼科技大学）
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainab…
Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform random forest models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgic…
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
University of Catania, Department of Economics and Business（卡塔尼亚大学经济与商业系）；Poznan University of Technology, Institute of Computing Science（波兹南技术大学计算科学研究所）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Kno…
Amazon Web Services Inc（亚马逊网络服务公司）；Cerner Corporation（塞纳公司）
Chronic disease progression is emerging as an important area of investment for healthcare providers. As the quantity and richness of available clinical data continue to increase along with advances in machine learning, there is great potential to advance our approaches to caring for patients. An ideal approach to this problem should generate good performance on at least three axes namely, a) perform across many clinical conditions without requiring deep clinical expertise or extensive data scientist effort, b) generalization across populations, and c) be explainable (model interpretability). W…
University of California, San Diego（加州大学圣迭戈分校）；University of California, Irvine（加州大学欧文分校）
Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales' for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that co…
National Technical University of Athens（雅典国立技术大学）；University of Athens（雅典大学）
Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human…
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
Institute of Information Technology Jahangirnagar University（贾汗吉尔纳加尔大学信息科技学院）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Cefalo Dhaka Bangladesh（达卡 Bangladesh 的 Cefalo）
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Engineering and Technology institute Groningen (ENTEG), University of Groningen（格罗宁根大学工程与技术学院（ENTEG））；Department of Human Movement Sciences, University Medical Center Groningen（格罗宁根大学医学中心人类运动科学系）
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Unit of Computer Systems and Bioinformatics, Department of Engineering（计算机系统与生物信息学单位，工程系）；Research Unit of Radiation Oncology, Department of Medicine and Surgery（放射肿瘤研究单位，医学与外科系）；Operative Research Unit of Radiation Oncology（放射肿瘤操作研究单位）；Research Unit of Anatomical Pathology, Department of Medicine and Surgery（解剖病理研究单位，医学与外科系）；Anatomical Pathology Operative Research Unit（解剖病理操作研究单位）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering（诊断与介入部门，放射物理，生物医学工程）
SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making
Computing & Information Sciences（计算与信息科学）；Florida International University（佛罗里达国际大学）；Computer Science & Engineering（计算机科学与工程）；University of South Florida（佛罗里达州立大学）
Brunel University London（伦敦布鲁内尔大学）；Technical University of Darmstadt（达姆施塔特工业大学）；American International University-Bangladesh（孟加拉国国际大学）；Dhaka University of Engineering and Technology（达卡工程技术大学）；George Mason University（乔治梅森大学）
IBM Research Zurich（IBM苏黎世研究院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；RWTH Aachen University（亚琛工业大学）；Heidelberg University（海德堡大学）
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based (MCA) encoder significantly outperforms a baseline mod…
Booth School of Business, The University of Chicago（芝加哥大学布斯商学院）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）
We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The distances between the clusters can then be regularized to reflect the similarity between different clusters of users. We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear model to theoretically demonstrate that our approach outperforms agents learning independently an…
Platform of Bioinformatics (PBI), University Lausanne Hospital (CHUV) and Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL)（生物信息平台（PBI）、日内瓦大学医院（CHUV）和生物医学学院（FBM）、日内瓦大学（UNIL））；PBI, CHUV and FBM, UNIL（PBI、CHUV和FBM、UNIL）；Department of Biomedical Engineering, University of Basel（生物医学工程系、巴塞尔大学）；Roche Diagnostics International Ltd.（罗氏诊断国际有限公司）；F. Hoffmann - La Roche Ltd., Roche Information Solutions（F. Hoffmann-La Roche有限公司、罗氏信息解决方案）
University of Southampton（南安普顿大学）；European Central Bank（欧洲中央银行）
Explainable AI constitutes a fundamental step towards establishing fairness and addressing bias in algorithmic decision-making. Despite the large body of work on the topic, the benefit of solutions is mostly evaluated from a conceptual or theoretical point of view and the usefulness for real-world use cases remains uncertain. In this work, we aim to state clear user-centric desiderata for explainable AI reflecting common explainability needs experienced in statistical production systems of the European Central Bank. We link the desiderata to archetypical user roles and give examples of techniq…
