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[1] Prateek Mittal,et al. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers , 2017, ArXiv.
[2] Bartosz Krawczyk,et al. Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine , 2019, ArXiv.
[3] Javier Del Ser,et al. What Lies Beneath: A Note on the Explainability of Black-box Machine Learning Models for Road Traffic Forecasting , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).
[4] Prateek Mittal,et al. DARTS: Deceiving Autonomous Cars with Toxic Signs , 2018, ArXiv.
[5] Ankur P. Parikh,et al. Thieves on Sesame Street! Model Extraction of BERT-based APIs , 2019, ICLR.
[6] Ankur Teredesai,et al. Interpretable Machine Learning in Healthcare , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).
[7] Thomas M. Powers,et al. The Ethics of the Ethics of AI , 2020 .
[8] Olof Mogren,et al. Adversarial representation learning for private speech generation , 2020, ArXiv.
[9] Timothy J. O'Shea,et al. Radio Machine Learning Dataset Generation with GNU Radio , 2016 .
[10] Vincent C. Müller,et al. Ethics of artificial intelligence and robotics , 2020 .
[11] Cristina Conati,et al. AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling , 2018, ArXiv.
[12] Bo Chen,et al. Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach , 2021, IEEE Transactions on Smart Grid.
[13] Yi Zeng,et al. Linking Artificial Intelligence Principles , 2018, SafeAI@AAAI.
[14] L. Floridi,et al. Book review : Group Privacy : New Challenges of Data Technologies , 2017 .
[15] Raihan Ur Rasool,et al. Big data for development: applications and techniques , 2016, ArXiv.
[16] K. LaGrandeur. Emotion, Artificial Intelligence, and Ethics , 2015 .
[17] S. Sastry,et al. Security and Privacy Issues with Health Care Information Technology , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[18] Rajnikant Sharma,et al. Learning-Based Adversarial Agent Detection and Identification in Cyber Physical Systems Applied to Autonomous Vehicular Platoon , 2019, 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS).
[19] Xin Wu,et al. Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph , 2018, Transportation Research Part C: Emerging Technologies.
[20] Kui Ren,et al. Adversarial Attacks and Defenses in Deep Learning , 2020, Engineering.
[21] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[22] Max David Champneys,et al. On the vulnerability of data-driven structural health monitoring models to adversarial attack , 2020 .
[23] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[24] Lynne E. Parker,et al. Creation of the National Artificial Intelligence Research and Development Strategic Plan , 2018, AI Mag..
[25] Karen Yeung,et al. AI Governance by Human Rights-Centred Design, Deliberation and Oversight: An End to Ethics Washing , 2019, SSRN Electronic Journal.
[26] Junaid Qadir,et al. Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey , 2019, IEEE Access.
[27] C. Robert. Superintelligence: Paths, Dangers, Strategies , 2017 .
[28] A. Aljabali,et al. Graduate students reported practices regarding the issue of informed consent and maintaining of data confidentiality in a developing country , 2020, Heliyon.
[29] Rama Chellappa,et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.
[30] Daniel L. Marino,et al. An Adversarial Approach for Explainable AI in Intrusion Detection Systems , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.
[31] Roman V. Yampolskiy,et al. The technological singularity , 2017 .
[32] D. Massey. American Apartheid: Segregation and the Making of the Underclass , 1990, American Journal of Sociology.
[33] F. Kreuter,et al. Collecting Survey and Smartphone Sensor Data With an App: Opportunities and Challenges Around Privacy and Informed Consent , 2020, Social Science Computer Review.
[34] Joshua A. Kroll. Accountability in Computer Systems , 2020 .
[35] Michele Meroni,et al. Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt , 2020, Environmental Research Letters.
[36] Jonathan H. Chen,et al. Planning for the Known Unknown: Machine Learning for Human Healthcare Systems , 2020, The American journal of bioethics : AJOB.
[37] Michael Riegler,et al. Automatic detection of passable roads after floods in remote sensed and social media data , 2019, Signal Process. Image Commun..
[38] Timnit Gebru,et al. Oxford Handbook on AI Ethics Book Chapter on Race and Gender , 2019, ArXiv.
[39] Mohsen Guizani,et al. Smart Cities: A Survey on Data Management, Security, and Enabling Technologies , 2017, IEEE Communications Surveys & Tutorials.
[40] Chandan Singh,et al. Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.
[41] K. Crawford. Artificial Intelligence's White Guy Problem , 2016 .
[42] L. Floridi,et al. Data ethics , 2021, Effective Directors.
[43] M. Shamim Hossain,et al. B5G and Explainable Deep Learning Assisted Healthcare Vertical at the Edge: COVID-I9 Perspective , 2020, IEEE Network.
[44] Ribana Roscher,et al. Explainable Machine Learning for Scientific Insights and Discoveries , 2019, IEEE Access.
[45] Christopher T. Lowenkamp,et al. False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" , 2016 .
[46] W. Ramsey,et al. The Cambridge Handbook of Artificial Intelligence , 2014 .
[47] Benjamin Turnbull,et al. Robustness Evaluations of Sustainable Machine Learning Models against Data Poisoning Attacks in the Internet of Things , 2020, Sustainability.
[48] Erik Poll,et al. Adversarial Examples on Object Recognition: A Comprehensive Survey , 2020, ArXiv.
[49] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[50] David S. Melnick,et al. International evaluation of an AI system for breast cancer screening , 2020, Nature.
[51] Vinay P. Namboodiri,et al. U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[52] Luigi V. Mancini,et al. Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques , 2018, ArXiv.
[53] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[54] Agusti Solanas,et al. The pursuit of citizens' privacy: a privacy-aware smart city is possible , 2013, IEEE Communications Magazine.
[55] Patrick Lin,et al. Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence , 2017 .
[56] Baskar Ganapathysubramanian,et al. An explainable deep machine vision framework for plant stress phenotyping , 2018, Proceedings of the National Academy of Sciences.
[57] Michael Anderson,et al. Guest Editors' Introduction: Machine Ethics , 2006, IEEE Intelligent Systems.
[58] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[59] Fadi Al-Turjman,et al. Applications of Artificial Intelligence and Machine learning in smart cities , 2020, Comput. Commun..
[60] Insoo Sohn,et al. Defense against neural trojan attacks: A survey , 2021, Neurocomputing.
[61] Ender Konukoglu,et al. Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images. , 2019, European journal of radiology.
[62] Joao Marques-Silva,et al. On Relating Explanations and Adversarial Examples , 2019, NeurIPS.
[63] Miao Pan,et al. Robust Truth Discovery against Data Poisoning in Mobile Crowdsensing , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).
[64] Christopher Meek,et al. Adversarial learning , 2005, KDD '05.
[65] Raja Chatila,et al. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems , 2019, Robotics and Well-Being.
[66] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[67] Myunghwa Kang,et al. Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) – cutting edge , 2020 .
[68] Tim Menzies,et al. Omni: automated ensemble with unexpected models against adversarial evasion attack , 2020, Empir. Softw. Eng..
[69] Tian Liu,et al. FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications , 2020, IEEE Transactions on Industrial Informatics.
[70] H. Penny Nii,et al. The Handbook of Artificial Intelligence , 1982 .
[71] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[72] Scott M. Lundberg,et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery , 2018, Nature Biomedical Engineering.
[73] Vijay Arya,et al. Model Extraction Warning in MLaaS Paradigm , 2017, ACSAC.
[74] Asaf Shabtai,et al. When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures , 2019, 2020 International Joint Conference on Neural Networks (IJCNN).
[75] Jai Kotia,et al. Risk Susceptibility of Brain Tumor Classification to Adversarial Attacks , 2019, ICMMI.
[76] N. Sri Madhava Raja,et al. Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images , 2020, Pattern Recognit. Lett..
[77] Tao Liu,et al. SIN2: Stealth infection on neural network — A low-cost agile neural Trojan attack methodology , 2018, 2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
[78] Fabio Roli,et al. Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues , 2013, Inf. Sci..
[79] Wentao Zhao,et al. Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method , 2019 .
[80] Saurabh Shintre,et al. Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning , 2018, ArXiv.
[81] Lev V. Utkin,et al. Counterfactual explanation of machine learning survival models , 2020, Informatica.
[82] Murat Kantarcioglu,et al. A survey of game theoretic approach for adversarial machine learning , 2019, WIREs Data Mining Knowl. Discov..
[83] Alberto Ferreira de Souza,et al. Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).
[84] Mohsen Guizani,et al. Trust-Based Cloud Machine Learning Model Selection for Industrial IoT and Smart City Services , 2020, IEEE Internet of Things Journal.
[85] Ryan Calo,et al. Artificial Intelligence Policy: A Primer and Roadmap , 2017 .
[86] Miles Brundage,et al. Limitations and risks of machine ethics , 2014, J. Exp. Theor. Artif. Intell..
[87] Haichao Zhang,et al. Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training , 2019, NeurIPS.
[88] James Bailey,et al. Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems , 2019, Pattern Recognit..
[89] Enrico Costanza,et al. Evaluating saliency map explanations for convolutional neural networks: a user study , 2020, IUI.
[90] Fabio Roli,et al. Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries , 2019, ITASEC.
[91] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[92] Sanjay Chawla,et al. Reinforcement Learning with Explainability for Traffic Signal Control , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).
[93] John Danaher,et al. The Threat of Algocracy: Reality, Resistance and Accommodation , 2016, Philosophy & Technology.
[94] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[95] Peng Li,et al. CUDA Implementation of Deformable Pattern Recognition and its Application to MNIST Handwritten Digit Database , 2010, 2010 20th International Conference on Pattern Recognition.
[96] Minh N. Do,et al. Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-Adaptive Vehicle Detectors for Traffic Video Analysis , 2019, CVPR Workshops.
[97] Shravya Shetty,et al. Reply to: Transparency and reproducibility in artificial intelligence , 2020, Nature.
[98] E. A. vanZoonen. Privacy concerns in smart cities , 2016 .
[99] Joanna J. Bryson,et al. The Artificial Intelligence of the Ethics of Artificial Intelligence , 2020 .
[100] Christopher Leckie,et al. Defending Distributed Classifiers Against Data Poisoning Attacks , 2020, ArXiv.
[101] Data protection and privacy: Data protection and democracy , 2020 .
[102] Fang Deng,et al. Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty , 2020, Neurocomputing.
[103] Wei Wu,et al. Traffic Anomaly Detection via Perspective Map based on Spatial-temporal Information Matrix , 2019, CVPR Workshops.
[104] José M. Alonso,et al. Building Cognitive Cities with Explainable Artificial Intelligent Systems , 2017, CEx@AI*IA.
[105] Syed Ali Hassan,et al. Machine Learning in IoT Security: Current Solutions and Future Challenges , 2019, IEEE Communications Surveys & Tutorials.
[106] Avron Barr,et al. The Handbook of Artificial Intelligence , 1982 .
[107] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[108] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[109] Lingkun Fu,et al. DoS Attack Energy Management Against Remote State Estimation , 2018, IEEE Transactions on Control of Network Systems.
[110] Ernest Davis,et al. Ethical guidelines for a superintelligence , 2015, Artif. Intell..
[111] Jichen Zhu,et al. Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation , 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).
[112] Salah Sukkarieh,et al. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..
[113] Xiaohui Liang,et al. Security and Privacy in Smart City Applications: Challenges and Solutions , 2017, IEEE Communications Magazine.
[114] Michael Mattioli,et al. Big data, bigger dilemmas: A critical review , 2015, J. Assoc. Inf. Sci. Technol..
[115] Qi Tian,et al. Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[116] Amit Kumar Sikder,et al. Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.
[117] Tony Doyle,et al. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2017, Inf. Soc..
[118] Notes from the AI frontier: Tackling bias in AI (and in humans) , 2019 .
[119] Varol Akman,et al. Introduction to the special issue on philosophical foundations of artificial intelligence , 2000, J. Exp. Theor. Artif. Intell..
[120] Peter Volgyesi,et al. Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks , 2019, 2019 Resilience Week (RWS).
[121] Mohan Li,et al. Deep Reinforcement Learning for Partially Observable Data Poisoning Attack in Crowdsensing Systems , 2020, IEEE Internet of Things Journal.
[122] Benjamin C. M. Fung,et al. Security and privacy challenges in smart cities , 2018 .
[123] Jun Wang,et al. A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).
[124] Junaid Qadir,et al. Secure and Robust Machine Learning for Healthcare: A Survey , 2020, IEEE Reviews in Biomedical Engineering.
[125] Cat Drew,et al. Data science ethics in government , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[126] David J. Gunkel. The Machine Question: Critical Perspectives on AI, Robots, and Ethics , 2012 .
[127] Sandra Wachter,et al. Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR , 2018, Comput. Law Secur. Rev..
[128] Kevin Fu,et al. Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving , 2019, CCS.
[129] Kemal Davaslioglu,et al. Generative Adversarial Learning for Spectrum Sensing , 2018, 2018 IEEE International Conference on Communications (ICC).
[130] Johannes Stallkamp,et al. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition , 2012, Neural Networks.
[131] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[132] Aleix M. Martinez,et al. The AR face database , 1998 .
[133] Majdi Maabreh,et al. Parameters optimization of deep learning models using Particle swarm optimization , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).
[134] Rich Caruana,et al. Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation , 2017, AIES.
[135] Marta R. Costa-jussa,et al. MT-Adapted Datasheets for Datasets: Template and Repository , 2020, ArXiv.
[136] Harsh Kupwade Patil,et al. Big Data Security and Privacy Issues in Healthcare , 2014, 2014 IEEE International Congress on Big Data.
[137] Mianxiong Dong,et al. DeSVig: Decentralized Swift Vigilance Against Adversarial Attacks in Industrial Artificial Intelligence Systems , 2020, IEEE Transactions on Industrial Informatics.
[138] Cuntai Guan,et al. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[139] Junaid Qadir,et al. Securing Machine Learning (ML) in the Cloud:A Systematic Review of Cloud ML Security , 2020 .
[140] Yogesh Kumar Dwivedi,et al. Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework , 2020, Information systems frontiers : a journal of research and innovation.
[141] Charles R. Farrar,et al. STRUCTURAL HEALTH MONITORING AT LOS ALAMOS NATIONAL LABORATORY , 2000 .
[142] Ankur Srivastava,et al. A Survey on Neural Trojans , 2020, 2020 21st International Symposium on Quality Electronic Design (ISQED).
[143] Richard Bowden,et al. A Survey of Deep Learning Applications to Autonomous Vehicle Control , 2019, IEEE Transactions on Intelligent Transportation Systems.
[144] Koushik Nagasubramanian,et al. Plant disease identification using explainable 3D deep learning on hyperspectral images , 2019, Plant Methods.
[145] Ryan Calo,et al. There is a blind spot in AI research , 2016, Nature.
[146] Dimitar Filev,et al. Explainable Density-Based Approach for Self-Driving Actions Classification , 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).
[147] Silvia Tulli,et al. Explainability in Autonomous Pedagogical Agents , 2020, AAAI.
[148] Zhangyang Wang,et al. Practical Solutions for Machine Learning Safety in Autonomous Vehicles , 2019, SafeAI@AAAI.
[149] Wen-Chuan Lee,et al. Trojaning Attack on Neural Networks , 2018, NDSS.
[150] Tribhuvanesh Orekondy,et al. Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks , 2020, ICLR.
[151] Leonid Stoimenov,et al. Semantic Technologies in e-government: Toward Openness and Transparency , 2018 .
[152] Trevor Hastie,et al. Transparency and reproducibility in artificial intelligence , 2020, Nature.
[153] Dorin Comaniciu,et al. No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks , 2021, IEEE Transactions on Medical Imaging.
[154] Elisa Bertino,et al. Data Transparency with Blockchain and AI Ethics , 2019, ACM J. Data Inf. Qual..
[155] Yi Zhou,et al. Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble , 2020, ArXiv.
[156] Steven Bramhall,et al. QLIME-A Quadratic Local Interpretable Model-Agnostic Explanation Approach , 2020 .
[157] Joachim Fabini,et al. Explainability and Adversarial Robustness for RNNs , 2019, 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService).
[158] Dawn M. Tilbury,et al. Explanations and Expectations: Trust Building in Automated Vehicles , 2018, HRI.
[159] Ya Li,et al. Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[160] John F. Canny,et al. Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[161] Suresh Venkatasubramanian,et al. Auditing black-box models for indirect influence , 2016, Knowledge and Information Systems.
[162] Khalil Khan,et al. Intelligent Fusion of Deep Features for Improved Waste Classification , 2020, IEEE Access.
[163] Anna Jobin,et al. The global landscape of AI ethics guidelines , 2019, Nature Machine Intelligence.
[164] Paula Boddington,et al. Towards a Code of Ethics for Artificial Intelligence , 2017, Artificial Intelligence: Foundations, Theory, and Algorithms.
[165] Ajay Chander,et al. Creation of User Friendly Datasets: Insights from a Case Study concerning Explanations of Loan Denials , 2019, ArXiv.
[166] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[167] F. Richard Yu,et al. A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.
[168] Benjamin Edwards,et al. Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations , 2018 .
[169] Juan-Carlos Cano,et al. Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues , 2018, Sensors.
[170] Scott Cheng‐Hsin Yang,et al. Explainable Artificial Intelligence via Bayesian Teaching , 2017 .
[171] J. Savulescu,et al. Moral Enhancement and Artificial Intelligence: Moral AI? , 2015 .
[172] Daniel Cullina,et al. Enhancing robustness of machine learning systems via data transformations , 2017, 2018 52nd Annual Conference on Information Sciences and Systems (CISS).
[173] J. Donath. Ethical Issues in Our Relationship with Artificial Entities , 2020 .
[174] R. Kitchin,et al. The ethics of smart cities and urban science , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[175] Christian Poellabauer,et al. Real-Time Adversarial Attacks , 2019, IJCAI.
[176] Alastair R. Beresford,et al. Ethical issues in research using datasets of illicit origin , 2017, Internet Measurement Conference.
[177] Jiming Chen,et al. Smart community: an internet of things application , 2011, IEEE Communications Magazine.
[178] Dmitry Goldgof,et al. Mitigating Adversarial Attacks on Medical Image Understanding Systems , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
[179] Baosen Zhang,et al. Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks , 2019, e-Energy.
[180] Quan Z. Sheng,et al. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey , 2019 .
[181] Steffen Staab,et al. Bias in data‐driven artificial intelligence systems—An introductory survey , 2020, WIREs Data Mining Knowl. Discov..
[182] Francisco Herrera,et al. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.
[183] Steven Euijong Whang,et al. A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective , 2018, IEEE Transactions on Knowledge and Data Engineering.
[184] Hiroyuki Shindo,et al. Interpretable Adversarial Perturbation in Input Embedding Space for Text , 2018, IJCAI.
[185] Jake Ryland Williams,et al. BuzzFace: A News Veracity Dataset with Facebook User Commentary and Egos , 2018, ICWSM.
[186] J. McNutt,et al. Smart Cities, Transparency, Civic Technology and Reinventing Government , 2018 .
[187] David J. Hand,et al. Aspects of Data Ethics in a Changing World: Where Are We Now? , 2018, Big Data.
[188] Yalin E. Sagduyu,et al. Spectrum Data Poisoning with Adversarial Deep Learning , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).
[189] Valentina Zantedeschi,et al. Efficient Defenses Against Adversarial Attacks , 2017, AISec@CCS.
[190] Damith Chinthana Ranasinghe,et al. STRIP: a defence against trojan attacks on deep neural networks , 2019, ACSAC.
[191] Katina Michael,et al. Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems , 2019, Proc. IEEE.
[192] Hui Tian,et al. Data Auditing for the Internet of Things Environments Leveraging Smart Contract , 2020, FCS.
[193] Ramez Elmasri,et al. Issues in data fusion for healthcare monitoring , 2008, PETRA '08.
[194] Anna Romanou,et al. The necessity of the implementation of Privacy by Design in sectors where data protection concerns arise , 2017, Comput. Law Secur. Rev..
[195] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.
[196] Michael J. Lyons,et al. Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.
[197] L. Floridi,et al. The ethics of AI in health care: A mapping review. , 2020, Social science & medicine.
[198] Jinjun Chen,et al. Differential Privacy Techniques for Cyber Physical Systems: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[199] Suresh Venkatasubramanian,et al. Disentangling Influence: Using Disentangled Representations to Audit Model Predictions , 2019, NeurIPS.
[200] Arash Rahnama,et al. An Adversarial Approach for Explaining the Predictions of Deep Neural Networks , 2020, ArXiv.
[201] Jaegul Choo,et al. Visual Analytics for Explainable Deep Learning , 2018, IEEE Computer Graphics and Applications.
[202] Stefano Ricciardi,et al. An integrated VR/AR framework for user-centric interactive experience of cultural heritage: The ArkaeVision project , 2019, Digit. Appl. Archaeol. Cult. Heritage.
[203] Nello Cristianini,et al. Can Machines Read our Minds? , 2019, Minds and Machines.
[204] Ruqiang Yan,et al. Generative adversarial networks for data augmentation in machine fault diagnosis , 2019, Comput. Ind..
[205] Vitaly Shmatikov,et al. Auditing Data Provenance in Text-Generation Models , 2018, KDD.
[206] Johannes Stallkamp,et al. Detection of traffic signs in real-world images: The German traffic sign detection benchmark , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[207] Howard Bauchner,et al. Data Sharing: An Ethical and Scientific Imperative. , 2016, JAMA.
[208] Peter A. Flach,et al. Explainability fact sheets: a framework for systematic assessment of explainable approaches , 2019, FAT*.
[209] Amos J. Storkey,et al. Data Augmentation Generative Adversarial Networks , 2017, ICLR 2018.
[210] Xiaogang Wang,et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[211] Mennatallah El-Assady,et al. explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning , 2019, IEEE Transactions on Visualization and Computer Graphics.
[212] Fabrício Benevenuto,et al. Explainable Machine Learning for Fake News Detection , 2019, WebSci.
[213] Richard O. Sinnott,et al. Decentralized Big Data Auditing for Smart City Environments Leveraging Blockchain Technology , 2019, IEEE Access.
[214] Kyarash Shahriari,et al. IEEE standard review — Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems , 2017, 2017 IEEE Canada International Humanitarian Technology Conference (IHTC).
[215] Gilles Perrouin,et al. Ethical Adversaries , 2020, SIGKDD Explor..
[216] Inioluwa Deborah Raji,et al. Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing , 2020, AIES.
[217] J. Hutchins. The Oxford handbook of ethics of war , 2020 .
[218] Juan-José Boté,et al. Reusing Data Technical and Ethical Challenges , 2019 .
[219] Trevor J. M. Bench-Capon,et al. Ethical approaches and autonomous systems , 2020, Artif. Intell..
[220] S. Bony,et al. SIRTA, a ground-based atmospheric observatory for cloud and aerosol research , 2005 .
[221] Hyungheon Kim,et al. A Study on the Security Threats and Privacy Policy of Intelligent Video Surveillance System Considering 5G Network Architecture , 2020, 2020 International Conference on Electronics, Information, and Communication (ICEIC).
[222] Qian Zhu,et al. Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses , 2019, Sensors.
[223] Medhat Moussa,et al. Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends , 2020, IEEE Transactions on Intelligent Transportation Systems.
[224] James H. Moor,et al. The Nature, Importance, and Difficulty of Machine Ethics , 2006, IEEE Intelligent Systems.
[225] C. Allen,et al. Moral Machines: Teaching Robots Right from Wrong , 2008 .
[226] Thilo Hagendorff,et al. The Ethics of AI Ethics: An Evaluation of Guidelines , 2019, Minds and Machines.
[227] Matthias Braun,et al. Own Data? Ethical Reflections on Data Ownership , 2020, Philosophy & Technology.
[228] Alexey Potapov,et al. Universal empathy and ethical bias for artificial general intelligence , 2013, J. Exp. Theor. Artif. Intell..
[229] Chandan Singh,et al. Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.
[230] Ghassan Hamarneh,et al. Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks , 2018, MLCN/DLF/iMIMIC@MICCAI.
[231] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[232] Cristina Conati,et al. Exploring the Need for Explainable Artificial Intelligence (XAI) in Intelligent Tutoring Systems (ITS) , 2019, IUI Workshops.
[233] Kouichi Sakurai,et al. One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.
[234] Qianmu Li,et al. Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection , 2020, IEEE Transactions on Information Forensics and Security.
[235] Abhishek Verma,et al. Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).
[236] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[237] Ali Davoudi,et al. Detection of False-Data Injection Attacks in Cyber-Physical DC Microgrids , 2017, IEEE Transactions on Industrial Informatics.
[238] Anton Korinek. Integrating Ethical Values and Economic Value to Steer Progress in Artificial Intelligence , 2019, The Oxford Handbook of Ethics of AI.
[239] Germain Forestier,et al. Adversarial Attacks on Deep Neural Networks for Time Series Classification , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[240] Patrick Lin,et al. Robot Ethics: The Ethical and Social Implications of Robotics , 2011 .
[241] Madhulika Srikumar,et al. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI , 2020, SSRN Electronic Journal.
[242] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[243] Daniel Gillblad,et al. Adversarial representation learning for synthetic replacement of private attributes , 2020, ArXiv.
[244] Davinia Hernández Leo,et al. Ethics in educational technology research: Informing participants on data sharing risks , 2019, Br. J. Educ. Technol..
[245] Virginia Dignum,et al. Ethics in artificial intelligence: introduction to the special issue , 2018, Ethics and Information Technology.
[246] Alan Bundy,et al. Preparing for the future of Artificial Intelligence , 2016, AI & SOCIETY.
[247] Atul Prakash,et al. Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[248] Xing Xie,et al. Explainable Recommendation through Attentive Multi-View Learning , 2019, AAAI.
[249] Bin Yu,et al. Daytime Arctic Cloud Detection Based on Multi-Angle Satellite Data With Case Studies , 2008 .
[250] Chenglin Miao,et al. Attack under Disguise: An Intelligent Data Poisoning Attack Mechanism in Crowdsourcing , 2018, WWW.
[251] Ellen P. Goodman. Smart City Ethics: The Challenge to Democratic Governance in the Oxford Handbook of Ethics of AI (edited by Markus D. Dubber, Frank Pasquale, and Sunit Das) , 2020, The Oxford Handbook of Ethics of AI.
[252] Timnit Gebru,et al. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.
[253] Stephen Flowerday,et al. Duplicitous social media and data surveillance: An evaluation of privacy risk , 2020, Comput. Secur..
[254] Geza Joos,et al. On the Accuracy Versus Transparency Trade-Off of Data-Mining Models for Fast-Response PMU-Based Catastrophe Predictors , 2012, IEEE Transactions on Smart Grid.
[255] Junaid Qadir,et al. Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward , 2019, IEEE Communications Surveys & Tutorials.
[256] Alexandros G. Dimakis,et al. Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification , 2018, MLSys.
[257] Zhiyuan Tan,et al. SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application , 2019, J. Netw. Comput. Appl..
[258] Plamen Angelov,et al. Towards Explainable Deep Neural Networks (xDNN) , 2019, Neural Networks.
[259] Kemal Davaslioglu,et al. Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning , 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).
[260] Andrew L. Beam,et al. Adversarial attacks on medical machine learning , 2019, Science.
[261] Avi Goldfarb,et al. The Economics of Artificial Intelligence , 2019 .
[262] D. Massey. American Apartheid: Segregation and the Making of the Underclass , 1993 .
[263] Lalana Kagal,et al. Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
[264] Weifeng Chen,et al. A Lightweight And privacy-preserving public cloud auditing scheme without bilinear pairings in smart cities , 2019, Comput. Stand. Interfaces.
[265] Fabio Roli,et al. Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? , 2020, ArXiv.
[266] Helmut Krcmar,et al. Semantic Web Technologies for Explainable Machine Learning Models: A Literature Review , 2019, PROFILES/SEMEX@ISWC.
[267] A. Martínez,et al. The AR face databasae , 1998 .
[268] J. Horvat. THE ETHICS OF ARTIFICIAL INTELLIGENCE , 2016 .
[269] Chenglin Miao,et al. Towards Data Poisoning Attacks in Crowd Sensing Systems , 2018, MobiHoc.
[270] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[271] Ankur Srivastava,et al. Neural Trojans , 2017, 2017 IEEE International Conference on Computer Design (ICCD).
[272] Yujie Li,et al. Adaptive Square Attack: Fooling Autonomous Cars With Adversarial Traffic Signs , 2021, IEEE Internet of Things Journal.
[273] Hui Wu,et al. Protecting Intellectual Property of Deep Neural Networks with Watermarking , 2018, AsiaCCS.
[274] Solon Barocas,et al. Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices , 2019, SSRN Electronic Journal.
[275] Nicola Conci,et al. How Deep Features Have Improved Event Recognition in Multimedia , 2019, ACM Trans. Multim. Comput. Commun. Appl..
[276] Aleksander Madry,et al. On Evaluating Adversarial Robustness , 2019, ArXiv.
[277] Nicola Conci,et al. Natural disasters detection in social media and satellite imagery: a survey , 2019, Multimedia Tools and Applications.
[278] Andrew L. Beam,et al. Adversarial Attacks Against Medical Deep Learning Systems , 2018, ArXiv.
[279] Fabrizio Falchi,et al. Cross-resolution face recognition adversarial attacks , 2020, Pattern Recognit. Lett..
[280] Seoung Bum Kim,et al. Intelligent traffic control for autonomous vehicle systems based on machine learning , 2020, Expert Syst. Appl..
[281] Samuel Marchal,et al. PRADA: Protecting Against DNN Model Stealing Attacks , 2018, 2019 IEEE European Symposium on Security and Privacy (EuroS&P).
[282] Jorg Henkel,et al. Hierarchical Classification for Constrained IoT Devices: A Case Study on Human Activity Recognition , 2020, IEEE Internet of Things Journal.
[283] Priyadarshini Panda,et al. Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness , 2018 .
[284] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[285] Trevor Darrell,et al. Textual Explanations for Self-Driving Vehicles , 2018, ECCV.
[286] Sherali Zeadally,et al. Physical Layer Security for the Smart Grid: Vulnerabilities, Threats, and Countermeasures , 2019, IEEE Transactions on Industrial Informatics.
[287] Shoshana Zuboff. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power , 2019 .
[288] Rachel K. E. Bellamy,et al. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias , 2018, ArXiv.
[289] K. Levy,et al. The Future of Work in the Age of AI , 2020 .
[290] Eric M. S. P. Veith,et al. Explainable Reinforcement Learning: A Survey , 2020, CD-MAKE.
[291] Francesco Solera,et al. Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.
[292] Janine S. Hiller,et al. Smart Cities, Big Data, and the Resilience of Privacy , 2016 .
[293] Ben Green. The Smart Enough City , 2019 .
[294] Tribhuvanesh Orekondy,et al. Knockoff Nets: Stealing Functionality of Black-Box Models , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[295] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[296] Thomas Ploug. In Defence of informed consent for health record research - why arguments from ‘easy rescue’, ‘no harm’ and ‘consent bias’ fail , 2020, BMC medical ethics.
[297] Michael Anderson,et al. Machine Ethics , 2011 .
[298] Xiang Zhang,et al. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks , 2020, NeurIPS.
[299] Eamonn J. Keogh,et al. The UCR time series archive , 2018, IEEE/CAA Journal of Automatica Sinica.
[300] Panagiotis Tsarchopoulos,et al. Smart cities and cloud computing: lessons from the STORM CLOUDS experiment , 2016 .
[301] Siddique Latif,et al. Caveat Emptor: The Risks of Using Big Data for Human Development , 2019, IEEE Technology and Society Magazine.
[302] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.
[303] S. Baum. A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy , 2017 .
[304] Corrado Aaron Visaggio,et al. Adversarial deep learning for energy management in buildings , 2019, SummerSim.
[305] Fabio Roli,et al. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.
[306] M. Deakin,et al. The First Two Decades of Smart-City Research: A Bibliometric Analysis , 2017 .
[307] Fei Hu,et al. Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter , 2014, IEEE Transactions on Control of Network Systems.
[308] Wei Yu,et al. Smart city: The state of the art, datasets, and evaluation platforms , 2017, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).
[309] M. Haeffelin,et al. Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning , 2020, Atmospheric Chemistry and Physics.
[310] Sharad Goel,et al. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.
[311] David A. Wagner,et al. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text , 2018, 2018 IEEE Security and Privacy Workshops (SPW).
[312] Qiang Huang,et al. GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks , 2020, IEEE Transactions on Knowledge and Data Engineering.
[313] Kush R. Varshney,et al. On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products , 2016, Big Data.
[314] J. Henrich,et al. The Moral Machine experiment , 2018, Nature.
[315] Araz Taeihagh,et al. Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities , 2019, Sustainability.
[316] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[317] Yong Yu,et al. Improved dynamic remote data auditing protocol for smart city security , 2017, Personal and Ubiquitous Computing.
[318] Dino Pedreschi,et al. FairLens: Auditing Black-box Clinical Decision Support Systems , 2020, Inf. Process. Manag..
[319] John Danaher,et al. Robots, law and the retribution gap , 2016, Ethics and Information Technology.
[320] Salvatore J. Stolfo,et al. Casting out Demons: Sanitizing Training Data for Anomaly Sensors , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).
[321] Stefan Wermter,et al. Explainable Goal-driven Agents and Robots - A Comprehensive Review , 2020, ACM Comput. Surv..
[322] Nicola Conci,et al. Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case , 2020, ArXiv.
[323] Steve Torrance,et al. Special issue on ethics and artificial agents , 2008, AI & SOCIETY.
[324] Susan B. Newman,et al. Improving informed consent: Stakeholder views , 2017, AJOB empirical bioethics.
[325] S. Han,et al. Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. , 2019, JAMA dermatology.