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Connecting the Dots in Trustworthy artificial intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

arXiv

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arXiv 2023年

作者： Díaz-Rodríguez, Natalia Ser, Javier Del Coeckelbergh, Mark de Prado, Marcos López Herrera-Viedma, Enrique Herrera, Francisco Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Derio48160 Spain Bilbao48013 Spain Department of Philosophy University of Vienna Vienna1010 Austria School of Engineering Cornell University IthacaNY14850 United States ADIA Lab Al Maryah Island Abu Dhabi United Arab Emirates Department of Mathematics Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

MSC Codes 68T01Trustworthy artificial intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system’s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight;robustness and safety;privacy and data governance;transparency;diversity, non-discrimination and fairness;societal and environmental wellbeing;and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. © 2023, CC BY-NC-ND.

关键词： artificial intelligence

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Adaptive Cleaning in Manufacturing: A Decision Tree Model for Efficient Factory Sanitation

Adaptive Cleaning in Manufacturing: A Decision Tree Model fo...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： M. Arulprakash Ramakrishnan Raman Atul A Gokhale K. Saravanan Ishwarya M.V S. Sujatha Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Artificial Intelligence and Data Science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Industrial cleanliness is crucial in production settings, and this system offers a novel way to use Internet of Things (IoT) technology to strengthen this practice. The suggested solution uses a Decision Tree (DT) model and real-time data received from IoT sensors placed strategically around the facility to enable adaptive enhancements to cleaning processes. This system of sensors keeps tabs on a number of variables, including manufacturing schedules, environmental factors, and circulation. In real-time, the DT model may use this data to optimize cleaning locations, durations, and procedures, cutting down on wasteful downtime and resource usage without compromising cleanliness. When contrasted with conventional static cleaning methods, the adaptive cleaning system shows considerable gains in both sanitation efficiency and resource occupancy. Improving cleaning using IoT technology makes it more responsive and smart, which in turn makes production more efficient and less detrimental to the environment. It explains how the IoT and DT models worked together to make manufacturing processes more adaptable, under the lens of industrial hygiene.

关键词： Adaptation models Schedules Adaptive systems Cleaning Real-time systems Production facilities Environmental factors

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BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Lan, Libin Cai, Pengzhou Jiang, Lu Liu, Xiaojuan Li, Yongmei Zhang, Yudong College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Computer Science Chongqing University Chongqing400044 China College of Artificial Intelligence Chongqing University of Technology Chongqing401135 China Department of Radiology The First Affiliated Hospital of Chongqing Medical University Chongqing400016 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

Accurate medical image segmentation plays an essential role in clinical quantification, disease diagnosis, treatment planning and many other applications. Both convolution-based and transformer-based u-shaped architectures have made significant success in various medical image segmentation tasks. The former can efficiently learn local information of images while requiring much more image-specific inductive biases inherent to convolution operation. The latter can effectively capture long-range dependency at different feature scales using self-attention, whereas it typically encounters the challenges of quadratic compute and memory requirements with sequence length increasing. To address this problem, through integrating the merits of these two paradigms in a well-designed u-shaped architecture, we propose a hybrid yet effective CNN-Transformer network, named BRAUNet++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity. Furthermore, this network restructures skip connection by incorporating channel-spatial attention which adopts convolution operations, aiming to minimize local spatial information loss and amplify global dimension-interaction of multi-scale features. Extensive experiments on three diverse imaging modalities datasets demonstrate that our proposed approach outperforms other state-of-the-art methods including its baseline: BRAU-Net under almost all evaluation metrics, which reveals the generality and robustness of our approach for multi-modal medical image segmentation tasks. The code and models are publicly available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Domain-Specific Versus General-Purpose Word Representations in Sentiment Analysis for Deep Learning Models 7th

Domain-Specific Versus General-Purpose Word Representations ...

引用

7th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2018

作者： Hung, Bui Thanh Data Analytics & Artificial Intelligence Laboratory Engineering---Technology Faculty Thu Dau Mot University 6 Tran Van On Phu Hoa Thu Dau MotBinh Duong Viet Nam

ISBN: (纸本)9789813291850

Sentiment analysis, also known as opinion mining or emotion artificial intelligence, has been gradually becoming a hot-trend topic in the recent years due to its wide applications and also the challenges in handling the dataset efficiently. With the popular deep learning-based approach, using vectors of word as an input for the models is a good way for the machine to learn the relation between words in a document. In this paper, vector representations of word are created based on two different ways: learning from its own internal dataset, or deriving from a famous pre-trained library known as fastText. The two representations will then be used in documents (we classify sentiment on document level) to generate input to 4 deep learning models: Long-Short Term Memory Network (LSTM), Bi-directional Long-Short Term Memory Network (BiLSTM), Convolutional Neural Network (CNN) and Convolutional Neural Network combined with Long-Short Term Memory Network (CNN-LSTM). We do experiment in Vietnamese review dataset and explorer words in domain-specific and general–purpose before turning into vectors in two different ways: tokenized and not tokenized. The result shows the best result of each of the approaches in four deep learning models: LSTM, Bi-LSTM, CNN and CNN-LSTM. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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A review on computer vision systems in monitoring of poultry: A welfare perspective

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artificial intelligence in Agriculture 2020年第1期4卷 184-208页

作者： Cedric Okinda Innocent Nyalala Tchalla Korohou Celestine Okinda Jintao Wang Tracy Achieng Patrick Wamalwa Tai Mang Mingxia Shen College of Artificial Intelligence Laboratory ofModern Facility Agriculture Technology and Equipment Engineering of Jiangsu ProvinceNanjing Agricultural UniversityJiangsu 210031PR China College of Veterinary Science and Agriculture University of NairobiLower KabeteNairobiKenya Faculty of Bioscience and Engineering Ghent UniversityGhentBelgium Faculty of Engineering Department of Agricultural EngineeringEgerton UniversityNjoroKenya

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related *** the current development in information technologies,computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties,and its ability to present a wide range of ***,it can be applied to monitor several bio-processes and *** review summarizes the current advances in poultrymonitoring techniques based on computer vision systems,i.e.,conventional machine learning-based and deep learning-based systems.A detailed presentation on the machine learning-based system was presented,i.e.,pre-processing,segmentation,feature extraction,feature selection,and dimension reduction,and ***,deep learning approaches in poultry monitoring were also ***,the challenges and possible solutions presented by researches in poultry monitoring,such as variable illumination conditions,occlusion problems,and lack of augmented and labeled poultry datasets,were discussed.

关键词： Computer vision Deep learning Machine learning Monitoring Poultry Welfare

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Secure IoHT data Sharing in the Cloud: Machine Learning with Logistic Regression for Privacy Protection

Secure IoHT Data Sharing in the Cloud: Machine Learning with...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ramakrishnan Raman K. Saravanan Atul A Gokhale K. Anitha Ishwarya M.V S. Sujatha Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Healthcare delivery and patient monitoring have been transformed by the fast rise of the Internet of Healthcare Things (IoHT). Cloud storage of sensitive health data creates privacy issues. Logistic Regression (LR) is used to ensure IoHT data sharing in this investigation. The cloud architecture allows efficient data exchange while protecting health information. LR is used to predict data sharing risk and make privacy choices. A varied collection of health measures and demographic data trains the model to generalize well to numerous contexts. The suggested system is tested extensively with Logistic Regression, which reliably identifies and mitigates privacy threats. data accessibility and privacy are balanced by the model's high sensitivity and specificity. It also examines the model's interpretability to provide privacy prediction factors. This openness improves user trust and regulatory compliance. This technique is scalable for large-scale IoHT installations. The cloud-based IoHT data sharing challenge has a unique approach. Machine learning, especially Logistic Regression, protects privacy without reducing health data usefulness. This research increases healthcare data security in the connected device age.

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RAKG:Document-level Retrieval Augmented Knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Hairong Si, Jiaheng Yan, Guohang Qi, Boyuan Cai, Pinlong Mao, Song Wang, Ding Shi, Botian Shanghai Artificial Intelligence Laboratory China School of Statistics and Data Science Nankai University Tianjin China Faculty of Computing Harbin Institute of Technology China Computer School Beijing Information Science and Technology University China

With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabilities of large language models (LLMs) has become increasingly prominent. However, traditional Knowledge Graph Construction (KGC) methods face challenges like complex entity disambiguation, rigid schema definition, and insufficient cross-document knowledge integration. This paper focuses on the task of automatic document-level knowledge graph construction. It proposes the Document-level Retrieval Augmented Knowledge Graph Construction (RAKG) framework. RAKG extracts pre-entities from text chunks and utilizes these pre-entities as queries for RAG, effectively addressing the issue of long-context forgetting in LLMs and reducing the complexity of Coreference Resolution. In contrast to conventional KGC methods, RAKG more effectively captures global information and the interconnections among disparate nodes, thereby enhancing the overall performance of the model. Additionally, we transfer the RAG evaluation framework to the KGC field and filter and evaluate the generated knowledge graphs, thereby avoiding incorrectly generated entities and relationships caused by hallucinations in LLMs. We further developed the MINE dataset by constructing standard knowledge graphs for each article and experimentally validated the performance of RAKG. The results show that RAKG achieves an accuracy of 95.91% on the MINE dataset, a 6.2 % point improvement over the current best baseline, GraphRAG (89.71%). The code is available at https://***/LMMApplication/RAKG. Copyright © 2025, The Authors. All rights reserved.

关键词： Graphic methods

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Incremental Feature Selection for Dynamic Incomplete data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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