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explainability-based Trust Algorithm for electricity price forecasting models

Energy and AI 2023年第4期14卷 141-158页

作者： Leena Heistrene Ram Machlev Michael Perl Juri Belikov Dmitry Baimel Kfir Levy Shie Mannor Yoash Levron Electrical Engineering Department School of TechnologyPandit Deendayal Energy UniversityGandhinagarGujarat382007India The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa3200003Israel Department of Software Science Tallinn University of TechnologyAkadeemia tee 15ATallinn12618Estonia Electrical&Electronics Engineering Department Sami Shamoon College of EngineeringBeer-Sheva84100Israel

Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during *** is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,*** the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a *** uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF ***,it becomes essential to identify whether or not the model prediction at a given instance is *** this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence *** suggested algorithm generates trust scores that reflect the model’s prediction quality for each new *** scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different *** score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed *** show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training *** the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.

关键词： Electricity price forecasting EPF Explainable AI model XAI SHAP explainability

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Causal temporal graph attention network for fault diagnosis of chemical processes

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Chinese Journal of Chemical Engineering 2024年第6期70卷 20-32页

作者： Jiaojiao Luo Zhehao Jin Heping Jin Qian Li Xu Ji Yiyang Dai School of Chemical Engineering Sichuan UniversityChengdu 610065China China Three Gorges Corporation Beijing 100038China

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical *** the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical ***,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their *** this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of *** attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault *** in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

关键词： Chemical processes Safety Fault diagnosis Causal discovery Attention mechanism explainability

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Text Difficulty Study:Do Machines Behave the Same as Humans Regarding Text Difficulty?

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Machine Intelligence Research 2024年第2期21卷 283-293页

作者： Bowen Chen Xiao Ding Yi Zhao Bo Fu Tingmao Lin Bing Qin Ting Liu Research Center for Social Computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China Fundamental Technology Center China Construction Bank Financial Technology Co.Ltd.Beijing 100033China

With the emergence of pre-trained models,current neural networks are able to give task performance that is comparable to ***,we know little about the fundamental working mechanism of pre-trained models in which we do not know how they approach such performance and how the task is solved by the *** example,given a task,human learns from easy to hard,whereas the model learns ***,difficulty-insensitive learning leads to great success in natural language processing(NLP),but little attention has been paid to the effect of text difficulty in *** propose a human learning matching index(HLM Index)to investigate the effect of text *** results show:1)LSTM gives more human-like learning behavior than ***,UID-SuperLinear gives the best evaluation of text difficulty among four text difficulty *** nine tasks,some tasks’performance is related to text difficulty,whereas others are not.2)Model trained on easy data performs best in both easy and medium test data,whereas trained on hard data only performs well on hard test data.3)Train the model from easy to hard,leading to quicker convergence.

关键词： Cognition inspired natural language processing psycholinguistics explainability text difficulty curriculum learning

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Media Power Measuring via Emotional Contagion

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Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Explainable data transformation recommendation for automatic visualization

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Frontiers of Information Technology & Electronic Engineering 2023年第7期24卷 1007-1027页

作者： Ziliang WU Wei CHEN Yuxin MA Tong XU Fan YAN Lei LV Zhonghao QIAN Jiazhi XIA State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization *** automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original ***,these limited data transformations fail to capture complex patterns such as clusters and *** recent advances in feature engineering provide the potential for more kinds of automatic data transformations,the auto-generated transformations lack explainability concerning how patterns are connected with the original *** tackle these challenges,we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic *** summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study,respectively.A recommendation algorithm is designed to compute optimal transformations,which can reveal specified types of patterns and maintain *** demonstrate the effectiveness of our approach through two cases and a user study.

关键词： Data transformation Data transformation recommendation Automatic visualization explainability

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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Mazen Ebraheem Assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent ***’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing *** learning models are at the forefront of this ***,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their *** a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep *** situation is substantially different in safety-critical *** lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this ***,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an *** lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment *** is the term introduced in the preceding *** AImodel’s black box nature will become explainable with these *** in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial *** models’explainable natures will help them commercially in diagnosis decisions in the medical *** paper explores the different frameworks for the explainability of AI models in the medical *** available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

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Computers, Materials & Continua 2023年第8期76卷 1701-1719页

作者： Yongsoo Lee Yeeun Lee Eungyu Lee Taejin Lee Department of Information Security Hoseo UniversityAsan31499Korea

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect ***,the rapidly changing data environment makes model life-cycle management after deployment ***-time detection of drift signals from various threats is fundamental for effectively managing deployed ***,detecting drift in unsupervised environments can be *** study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised *** proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current *** validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by *** on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world *** results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised *** proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment *** conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for *** proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection *** is versatile and suitable for various realtime data analysis contexts beyond DGA detection *** study significan

关键词： Cybersecurity machine learning(ML) model life-cycle management drift detection unsupervised environments shapley additive explanations(SHAP) explainability

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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1023-1051页

作者： Francisco JoséGarcía-Penlvo Andrea Vázquez-Ingelmo Alicia García-Holgado GRIAL Research Group Computer Science DepartmentUniversity of SalamancaSalamanca37008Spain

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be *** AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very *** these reasons,it is important to rely on expert knowledge when applying these ***,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI *** goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input *** systematic review follows the methodology proposed by Kitchenham and other authors in the field of software *** a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were *** reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

关键词： SLR systematic literature review artificial intelligence machine learning algorithm recommendation heuristics explainability

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On Interdisciplinary Studies of a New Generation of Artificial Intelligence and Logic

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Social Sciences in China 2022年第3期43卷 21-42页

作者： Liao Beishui School of Philosophy Zhejiang University

A new generation of artificial intelligence(NGAI),currently based on big data and machine learning,follows a path of *** this path achieves huge success in data-intensive applications under closed environments,there are some bottleneck problems,including a lack of explainability,the difficulty of ethical alignment,the weakness ofcognitive reasoning,*** address these problems inevitably involves thedepiction of information from an open,dynamic and real environment and the modeling of human reasoning and explanation *** argumentation is a general formalism for modeling various types of knowledge representation and reasoning in a context of disagreement,and is flexible enough to incorporate other types of knowledge for decisionmaking,such as preferences,weights,and ***,there are various approaches for efficient computation of argumentation semantics by exploiting the locality and modularity of argumentation,and for providing explanations based on arguments and *** organic combination of formal argumentation with existing big data and machine learning techniques can be expected to break through some existing technical bottlenecks and facilitate the sustainable development of NGAI.

关键词： new generation of artificial intelligence(NGAI) cognitive reasoning ethical alignment explainability formal argumentation

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人工智能中的“解释”：来自社会科学的洞见（上）

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数字人文研究 2024年第1期

作者：蒂姆·米勒

随着研究人员和从业者寻求为其算法提供更多透明度，可解释人工智能（Explainable Artificial Intelligence）有一场新近的复苏。这项研究的大部分聚焦于向人类观察者明确地解释决策或行动。观察人类如何相互解释可以作为人工智能解释... 详细信息

随着研究人员和从业者寻求为其算法提供更多透明度，可解释人工智能（Explainable Artificial Intelligence）有一场新近的复苏。这项研究的大部分聚焦于向人类观察者明确地解释决策或行动。观察人类如何相互解释可以作为人工智能解释的一个有用起点，这应该没有争议。诚然，公正地说，大多数关于可解释人工智能的工作仅利用研究人员对什么构成“好”解释的直觉。哲学、心理学和认知科学领域存在大量关于人们如何定义、生成、选择、评估和呈现解释的有价值的研究，这些研究认为人们在解释过程中运用了某些认知偏见和社会期望。文章认为，可解释人工智能领域可以建立在这些现有研究的基础上，并回顾了研究这些主题的哲学、认知心理学/科学和社会心理学的相关论文，由此获取了一些重要的发现，并讨论了如何将这些发现融入可解释人工智能的相关工作中。

关键词：解释可解释性可阐释性可解释人工智能透明度 explanation explainability interpretability explainable AI transparency

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