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Journal articles on the topic 'Black-box Systems'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Dendy, J. E. "Black box multigrid for systems." Applied Mathematics and Computation 19, no. 1-4 (1986): 57–74. http://dx.doi.org/10.1016/0096-3003(86)90096-2.

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Iezzoni, Lisa I. "'Black Box' Medical Information Systems." JAMA 265, no. 22 (1991): 3006. http://dx.doi.org/10.1001/jama.1991.03460220094040.

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3

Petrick, Elizabeth R. "Building the Black Box: Cyberneticians and Complex Systems." Science, Technology, & Human Values 45, no. 4 (2019): 575–95. http://dx.doi.org/10.1177/0162243919881212.

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In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected

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Kasianiuk, Krzysztof. "White box, black box and self-organization." Kybernetes 45, no. 1 (2016): 126–40. http://dx.doi.org/10.1108/k-02-2015-0057.

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Purpose – The purpose of this paper is to present two system identification models – “white box” and “black box” – as useful tools that help understand self-organization processes within and outside the organizations facilitated by leaders. Every leader is presented as a “systems designer” who plays a fundamental role in the process of self-organization, both within and outside the organization under study. Design/methodology/approach – First, “white box” and “black box” system identification models are presented as a basis for an integrated model of the “system” and its “environment.” Next, t

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Maio, Ronald F. "EMS systems: Opening the ‘Black Box’." Annals of Emergency Medicine 22, no. 4 (1993): 730–31. http://dx.doi.org/10.1016/s0196-0644(05)81858-x.

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Zagceta Álvarez, María Teresa, José de Jesús Medel Juárez, and Rosaura Palma Orozco. "Black box systems identification with estimation." Revista Facultad de Ingeniería Universidad de Antioquia, no. 72 (February 12, 2014): 35–46. http://dx.doi.org/10.17533/udea.redin.14828.

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In a black box system the internal parameters are not observables between input-output relationships. An approach is made knowing as estimation, based on the second probability moment. These results with a functional error and the innovation process are needed into identification structure. This process is known as an adaptive filter. Achieving a good convergence level with respect to the reference signal illustratively exemplified into simulation. The DC motor dynamical behavior is described by an adaptive filter without knowing the internal operation. Results compared with a methodology cons

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Neves, Francisco, Ricardo Vilaça, and José Pereira. "Detailed black-box monitoring of distributed systems." ACM SIGAPP Applied Computing Review 21, no. 1 (2021): 24–36. http://dx.doi.org/10.1145/3477133.3477135.

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Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, interapplication traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box moni

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Karabacak, Kerim. "A Grey-Box Model of a DC/DC Boost Converter for PV Energy Systems." International Transactions on Electrical Energy Systems 2024 (March 20, 2024): 1–18. http://dx.doi.org/10.1155/2024/3559456.

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This paper presents a grey-box model of a DC/DC boost converter for PV energy systems. The proposed model contains a white-box model part and a black-box model part together to prepare a better model for the PV boost converter. The white-box model part is used for knowledge of the circuit by mathematical equations since the black-box model part is used for unknown parameters such as temperature and electromagnetic interference. The black-box part of the proposed model is created by a nonlinear system identification of a real boost converter circuit with an artificial neural network. The precis

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Karadas, Georgiana, and Osman M. Karatepe. "Unraveling the black box." Employee Relations 41, no. 1 (2019): 67–83. http://dx.doi.org/10.1108/er-04-2017-0084.

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Purpose The purpose of this paper is to investigate the potential mediators that operate in the black box between high-performance work systems (HPWS) and employee outcomes. Design/methodology/approach The relationships the authors developed were assessed via data obtained from a time-lagged sample of customer-contact employees and their direct supervisors in the Romanian hotel industry. The study employed bias-corrected bootstrapping analysis to gauge the mediating effects. Findings The findings reveal that psychological capital mediates the impact of HPWS on work engagement. As hypothesized,

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Plimmer, Geoff, Jane Bryson, and Stephen T. T. Teo. "Opening the black box." Personnel Review 46, no. 7 (2017): 1434–51. http://dx.doi.org/10.1108/pr-10-2016-0275.

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Purpose The purpose of this paper is to explore how HIWS may shape organisational capabilities, in particular organisational ambidexterity (OA) – the ability to be both adaptable to the wider world, and internally aligned so that existing resources are used well. Given the demands on public agencies to manage conflicting objectives, and to do more with less in increasingly complex environments, this paper improves our understanding of how HIWS can contribute to public sector performance. The paper sheds light inside the black box of the HIWS/organisational performance link. Design/methodology/

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Komargodski, Ilan, Moni Naor, and Eylon Yogev. "White-Box vs. Black-Box Complexity of Search Problems." Journal of the ACM 66, no. 5 (2019): 1–28. http://dx.doi.org/10.1145/3341106.

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Ajunwa, Ifeoma. "The “black box” at work." Big Data & Society 7, no. 2 (2020): 205395172096618. http://dx.doi.org/10.1177/2053951720938093.

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An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to algorithmic processes of evaluations which will ma

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Hu, Ran, Nan Ma, Bing Li, et al. "Black-Box Modelling of Active Distribution Network Devices Based on Neural ODEs." Journal of Physics: Conference Series 2826, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2826/1/012029.

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Abstract The morphological structure and dynamic characteristics of power distribution systems are rapidly evolving due to the widespread application of distributed renewable energy and the rapid advancement of power electronics technology. The stability analysis of these complex new distribution systems depends on electromagnetic transient (EMT) simulation. However, the intellectual property rights protection by manufacturers leads to many distribution network devices that can only use black-box models with missing parameters, which challenges the simulation. This reduces the accuracy of the

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Leshkevich, Tatiana G. "Metaphors of the digital age and the Black Box Problem." Philosophy of Science and Technology 27, no. 1 (2022): 34–48. http://dx.doi.org/10.21146/2413-9084-2022-27-1-34-48.

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The article discusses the effects of the digital era, the driver of which is AI. The main goal is to focus on the Black Box Problem, “opacity of AI” and the possibility of Malicious Use of Artificial Intelligence. Three interconnected directions are interfaced. Firstly, in the context of the analysis of the digital age, the potential of metaphors is used, which makes it possible to describe digital transformations figuratively. Secondly, due to the growing demand for high technologies, the negative consequences of using AI are considered and a number of paradoxes of scientific and technolog

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Chen, Bin, Yiduo Wang, Rongxiao Wang, et al. "The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion." Symmetry 12, no. 2 (2020): 254. http://dx.doi.org/10.3390/sym12020254.

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With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of modeling approach is called white-box modeling; however, when the internal structure and characteristics of some systems are hard to understand, the black-box modeling based on statistic and data-modeling is often used. For most complex real systems, a single modeling approach can hardly describe the target system accurately. In this paper, we firstly

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Pedreschi, Dino, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, and Franco Turini. "Meaningful Explanations of Black Box AI Decision Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9780–84. http://dx.doi.org/10.1609/aaai.v33i01.33019780.

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Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated

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Moss, Robert. "POMDPStressTesting.jl: Adaptive Stress Testing for Black-Box Systems." Journal of Open Source Software 6, no. 60 (2021): 2749. http://dx.doi.org/10.21105/joss.02749.

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Panigutti, Cecilia, Alan Perotti, André Panisson, Paolo Bajardi, and Dino Pedreschi. "FairLens: Auditing black-box clinical decision support systems." Information Processing & Management 58, no. 5 (2021): 102657. http://dx.doi.org/10.1016/j.ipm.2021.102657.

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Brindel, P., B. Dany, O. Leclerc, and E. Desurvire. "‘Black box’ optical regenerator for RZ transmission systems." Electronics Letters 35, no. 6 (1999): 480. http://dx.doi.org/10.1049/el:19990357.

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Pasupuleti, Murali Krishna. "Auditing Black-Box AI Systems Using Counterfactual Explanations." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 598–608. https://doi.org/10.62311/nesx/rphcr20.

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Abstract: The widespread deployment of black-box artificial intelligence (AI) systems in high-stakes domains such as healthcare, finance, and criminal justice has intensified the demand for transparent and accountable decision-making. This study investigates the utility of counterfactual explanations as a method for auditing opaque AI models. By answering the question of what minimal change in input would alter a model’s output, counterfactuals offer a practical and interpretable means of understanding algorithmic decisions. The methodology employs Random Forest and Neural Network classifiers

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Koga, Kazuki, and Kazuhiro Takemoto. "Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification." Algorithms 15, no. 5 (2022): 144. http://dx.doi.org/10.3390/a15050144.

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Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we pr

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King, Jason, Ben Smith, and Laurie Williams. "Audit Mechanisms in Electronic Health Record Systems." International Journal of Computational Models and Algorithms in Medicine 3, no. 2 (2012): 23–42. http://dx.doi.org/10.4018/jcmam.2012040102.

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Inadequate audit mechanisms may result in undetected misuse of data in software-intensive systems. In the healthcare domain, electronic health record (EHR) systems should log the creating, reading, updating, or deleting of privacy-critical protected health information. The objective of this paper is to assess electronic health record audit mechanisms to determine the current degree of auditing for non-repudiation and to assess whether general audit guidelines adequately address non-repudiation. The authors analyzed the audit mechanisms of two open source EHR systems, OpenEMR and Tolven eCHR, a

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Dobslaw, Felix, Robert Feldt, and Francisco Gomes de Oliveira Neto. "Automated black-box boundary value detection." PeerJ Computer Science 9 (November 7, 2023): e1625. http://dx.doi.org/10.7717/peerj-cs.1625.

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Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for vari

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JIANG, YING, SHAN-SHAN HOU, JIN-HUI SHAN, LU ZHANG, and BING XIE. "AN APPROACH TO TESTING BLACK-BOX COMPONENTS USING CONTRACT-BASED MUTATION." International Journal of Software Engineering and Knowledge Engineering 18, no. 01 (2008): 93–117. http://dx.doi.org/10.1142/s0218194008003556.

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Component Based Software Development (CBSD) is gaining popularity in recent years. In this way of software development, software components, which are typically black-box components, are intensively reused to construct new systems. To ensure the quality of software systems composed of black-box components, a primary concern is how to ensure the quality of black-box components. Thus, adequate testing of those black-box components that will be reused is a necessary step in CBSD. However, due to the unavailability of the source code of black-box components, ensuring test adequacy becomes one of t

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072..art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072.art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “

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Shijubo, Junya, Masaki Waga, and Kohei Suenaga. "Probabilistic Black-Box Checking via Active MDP Learning." ACM Transactions on Embedded Computing Systems 22, no. 5s (2023): 1–26. http://dx.doi.org/10.1145/3609127.

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We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established black-box checking (BBC) technique to address stochastic behavior. Traditional BBC primarily involves iteratively identifying an input that breaches the system’s specifications by executing the following three phases: the learning phase to construct an automaton approximating the black box’s behavior, the synthesis phase to identify a candidate counterexample from the learned automaton, and the validation phase to validate the obtained can

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Li, Jing, Yanru Feng, and Mengli Wang. "Semantic Web-Driven Targeted Adversarial Attack on Black Box Automatic Speech Recognition Systems." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–23. http://dx.doi.org/10.4018/ijswis.360651.

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The susceptibility of Deep Neural Networks (DNNs) to adversarial attacks in Automatic Speech Recognition (ASR) systems has drawn significant attention. Most work focuses on white-box methods, but the assumption of full transparency of model architecture and parameters is unrealistic in real-world scenarios. Although several targeted black-box attack methods have been proposed in recent years, due to the complexity of ASR systems, they primarily rely on query-based approaches with limited search capabilities, leading to low success rates and noticeable noise. To address this, we propose DE-grad

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Al-Kubaisi, Nada Abdulla. "The Black Box of Implementing Strategic Decisions." International Journal of Customer Relationship Marketing and Management 14, no. 1 (2023): 1–21. http://dx.doi.org/10.4018/ijcrmm.323200.

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In trying to strike the necessary balance between the two main processes of strategic decision (i.e., between making and implementing decisions), many researchers have moved their focus toward strategic implementation, which has become a growing trend in the strategic decision literature. Nonetheless, the strategic decision implementation process remains a mysterious black box, and researchers are still looking for an answer to the challenging question of “What are the core activities in implementing strategic decisions?” Therefore, the purpose of this paper is to plug this gap in the literatu

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Qin, Zengyi, Dawei Sun, and Chuchu Fan. "Sablas: Learning Safe Control for Black-Box Dynamical Systems." IEEE Robotics and Automation Letters 7, no. 2 (2022): 1928–35. http://dx.doi.org/10.1109/lra.2022.3142743.

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Hang, Jie, Keji Han, Hui Chen, and Yun Li. "Ensemble adversarial black-box attacks against deep learning systems." Pattern Recognition 101 (May 2020): 107184. http://dx.doi.org/10.1016/j.patcog.2019.107184.

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Nugent, Conor, and Pádraig Cunningham. "A Case-Based Explanation System for Black-Box Systems." Artificial Intelligence Review 24, no. 2 (2005): 163–78. http://dx.doi.org/10.1007/s10462-005-4609-5.

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Yang, E., A. Daghstani, D. C. Kaelber, and M. Ikezuagu. "Implementing Black Box Warnings (BBWs) in Health Information Systems." Applied Clinical Informatics 03, no. 01 (2012): 124–34. http://dx.doi.org/10.4338/aci-2011-10-ra-0063.

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SummaryObjective: To develop a practical approach for implementing clinical decision support (CDS) for medication black box warnings (BBWs) into health information systems (HIS).Methods: We reviewed all existing medication BBWs and organized them into a taxonomy that identifies opportunities and challenges for implementing CDS for BBWs into HIS.Results: Of the over 400 BBWs that currently exist, they can be organized into 4 categories with 9 sub-categories based on the types of information contained in the BBWs, who should be notified, and potential actions to that could be taken by the person

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Montiel, Oscar, Oscar Castillo, Patricia Melin, and Roberto Sepúlveda. "Black box evolutionary mathematical modeling applied to linear systems." International Journal of Intelligent Systems 20, no. 2 (2004): 293–311. http://dx.doi.org/10.1002/int.20067.

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Iezzoni, L. I. "'Black box' medical information systems. A technology needing assessment." JAMA: The Journal of the American Medical Association 265, no. 22 (1991): 3006–7. http://dx.doi.org/10.1001/jama.265.22.3006.

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Leenashruthi, H. M. Kusuma N. A. Meghana P. Michelle Johnson Monisha M. U. "SUSPICIOUS HUMAN ACTIVITY TRACKING AI CAMERA USING BLACK BOX." International Journal For Technological Research In Engineering 11, no. 5 (2024): 95–98. https://doi.org/10.5281/zenodo.10487275.

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The rapid advancements in artificial intelligence (AI) have paved the way for innovative applications in surveillance and security systems. This paper introduces a novel approach to suspicious human activity tracking using an AI-powered camera system integrated with a black box for enhanced functionality and privacy preservation. The proposed system employs deep learning algorithms to analyze real-time video streams and detect anomalies in human behavior that may indicate potential security threats. The AI camera utilizes convolutional neural network (CNN) architecture for efficient object det

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Maaß, Jürgen, and Wolfgang Schlöglmann. "THE MATHEMATICAL WORLD IN THE BLACK BOX-SIGNIFICANCE OF THE BLACK BOX AS A MEDIUM OF MATHEMATIZING." Cybernetics and Systems 19, no. 4 (1988): 295–309. http://dx.doi.org/10.1080/01969728808902171.

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Mansour, Nizar, Emna Gara, and Chiha Gaha. "Getting inside the black box." Personnel Review 43, no. 4 (2014): 490–514. http://dx.doi.org/10.1108/pr-03-2013-0052.

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Purpose – The purpose of this paper is to explore, and eventually unlocking, the “black box” problem by addressing the potential mediating role of human capital and organizational commitment in the relationship between high performance work systems (HPWS) and perceived firm performance in the Tunisian financial industry. Design/methodology/approach – Based on the strategic human resource management (SHRM) theory, the authors developed a model that links HPWS to perceived organizational performance through human capital and employee organizational commitment. Data collected from 351 respondents

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Nazat, Sazid, Osvaldo Arreche, and Mustafa Abdallah. "On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems." Sensors 24, no. 11 (2024): 3515. http://dx.doi.org/10.3390/s24113515.

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The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the be

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Midian, Paul. "Perspectives on Penetration Testing — Black Box vs. White Box." Network Security 2002, no. 11 (2002): 10–12. http://dx.doi.org/10.1016/s1353-4858(02)11009-9.

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Limayem, Moez, Probir Banerjee, and Louis Ma. "Impact of GDSS: Opening the black box." Decision Support Systems 42, no. 2 (2006): 945–57. http://dx.doi.org/10.1016/j.dss.2005.08.004.

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TSAI, W. T., I. A. ZUALKERNAN, and SHEKHAR KIRANI. "PRAGMATIC TESTING METHODS FOR EXPERT SYSTEMS." International Journal on Artificial Intelligence Tools 02, no. 02 (1993): 181–217. http://dx.doi.org/10.1142/s0218213093000114.

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Effective testing of expert systems is an important but difficult task. In this paper we discuss why testing of expert systems is hard. For testing expert systems, testing techniques from conventional software engineering are adapted as a solution to these problems. We discuss the feasibility and application of black-box, white-box, and life-cycle testing techniques to expert systems. Black-box techniques include random, input partition and output partition testing. White-box techniques include path based partition, cause-effect graph, dynamic-flow, data-flow, and ablation testing. The use of

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Bolitho, Jane. "Inside the restorative justice black box." International Review of Victimology 23, no. 3 (2017): 233–55. http://dx.doi.org/10.1177/0269758017714549.

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This paper is concerned with why and how restorative justice works to alleviate the emotional effects of crime on victims. It posits a new explanation for the ‘aha’ moment; the turning point seen in some, though not all, restorative justice conferences where longstanding, negative emotions and beliefs that have persistently dogged a victim since the crime event, affecting their ability to enjoy the same everyday activities as in their pre-crime daily life, are seemingly eliminated. Focusing on victim experiences, an in-depth analysis of 20 cases collected as part of an empirical study into pos

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Chaudhary, Gyandeep. "Unveiling the Black Box: Bringing Algorithmic Transparency to AI." Masaryk University Journal of Law and Technology 18, no. 1 (2024): 93–122. http://dx.doi.org/10.5817/mujlt2024-1-4.

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Overall, algorithmic transparency is an important aspect of responsible AI development and deployment. Ensuring that AI systems are transparent and accountable will help build trust and confidence in these systems and ensure that they are used ethically and effectively. Artificial intelligence (AI) has emerged as a cutting-edge domain that is fundamentally redefining different areas of daily experiences, such as health care, transport, finance, education, and others. The systems are not created for making a judgment like human judgment of natural language, spotting patterns and problem-solving

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Heiker, Mathias, Matthias Kraume, Anica Mertins, Tim Wawer, and Sandra Rosenberger. "Biogas Plants in Renewable Energy Systems—A Systematic Review of Modeling Approaches of Biogas Production." Applied Sciences 11, no. 8 (2021): 3361. http://dx.doi.org/10.3390/app11083361.

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Biogas production is a relevant component in renewable energy systems. The paper addresses modeling approaches from an energy system, as well as from a process optimization, point of view. Model approaches of biogas production show different levels of detail. They can be classified as white, gray, and black box, or bottom-up and top-down approaches. On the one hand, biogas modeling can supply dynamic information on the anaerobic digestion process, e.g., to predict biogas yields or to optimize the anaerobic digestion process. These models are characterized by a bottom-up approach with different

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Borcherding, Anne, Martin Morawetz, and Steffen Pfrang. "Smarter Evolution: Enhancing Evolutionary Black Box Fuzzing with Adaptive Models." Sensors 23, no. 18 (2023): 7864. http://dx.doi.org/10.3390/s23187864.

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Smart production ecosystems are a valuable target for attackers. In particular, due to the high level of connectivity introduced by Industry 4.0, attackers can potentially attack individual components of production systems from the outside. One approach to strengthening the security of industrial control systems is to perform black box security tests such as network fuzzing. These are applicable, even if no information on the internals of the control system is available. However, most security testing strategies assume a gray box setting, in which some information on the internals are availabl

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Cabitza, Federico, Andrea Campagner, Chiara Natali, Enea Parimbelli, Luca Ronzio, and Matteo Cameli. "Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting." Machine Learning and Knowledge Extraction 5, no. 1 (2023): 269–86. http://dx.doi.org/10.3390/make5010017.

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The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system’s usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To addres

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Kolyubin, Sergey, Denis Efimov, Vladimir Nikiforov, and Alexey Bobtsov. "Control of Nonlinear Systems Using Multiple Model Black-Box Identification." IFAC Proceedings Volumes 46, no. 23 (2013): 582–87. http://dx.doi.org/10.3182/20130904-3-fr-2041.00163.

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Iyer, Padmavathi, and Amirreza Masoumzadeh. "Learning Relationship-Based Access Control Policies from Black-Box Systems." ACM Transactions on Privacy and Security 25, no. 3 (2022): 1–36. http://dx.doi.org/10.1145/3517121.

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Access control policies are crucial in securing data in information systems. Unfortunately, often times, such policies are poorly documented, and gaps between their specification and implementation prevent the system users, and even its developers, from understanding the overall enforced policy of a system. To tackle this problem, we propose the first of its kind systematic approach for learning the enforced authorizations from a target system by interacting with and observing it as a black box. The black-box view of the target system provides the advantage of learning its overall access contr

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Bak, Stanley, Sergiy Bogomolov, Parasara Sridhar Duggirala, Adam R. Gerlach, and Kostiantyn Potomkin. "Reachability of Black-Box Nonlinear Systems after Koopman Operator Linearization." IFAC-PapersOnLine 54, no. 5 (2021): 253–58. http://dx.doi.org/10.1016/j.ifacol.2021.08.507.

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