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Relevant bibliographies by topics / Algorithmic decision systems

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Academic literature on the topic 'Algorithmic decision systems'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "Algorithmic decision systems"

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Waldman, Ari, and Kirsten Martin. "Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions." Big Data & Society 9, no. 1 (2022): 205395172211004. http://dx.doi.org/10.1177/20539517221100449.

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The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions of the legitimacy

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Dawson, April. "Algorithmic Adjudication and Constitutional AI—The Promise of A Better AI Decision Making Future?" SMU Science and Technology Law Review 27, no. 1 (2024): 11. http://dx.doi.org/10.25172/smustlr.27.1.3.

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Algorithmic governance is when algorithms, often in the form of AI, make decisions, predict outcomes, and manage resources in various aspects of governance. This approach can be applied in areas like public administration, legal systems, policy-making, and urban planning. Algorithmic adjudication involves using AI to assist in or decide legal disputes. This often includes the analysis of legal documents, case precedents, and relevant laws to provide recommendations or even final decisions. The AI models typically used in these emerging decision-making systems use traditionally trained AI syste

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Herzog, Lisa. "Algorithmisches Entscheiden, Ambiguitätstoleranz und die Frage nach dem Sinn." Deutsche Zeitschrift für Philosophie 69, no. 2 (2021): 197–213. http://dx.doi.org/10.1515/dzph-2021-0016.

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Abstract In more and more contexts, human decision-making is replaced by algorithmic decision-making. While promising to deliver efficient and objective decisions, algorithmic decision systems have specific weaknesses, some of which are particularly dangerous if data are collected and processed by profit-oriented companies. In this paper, I focus on two problems that are at the root of the logic of algorithmic decision-making: (1) (in)tolerance for ambiguity, and (2) instantiations of Campbell’s law, i. e. of indicators that are used for “social decision-making” being subject to “corruption pr

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Kleinberg, Jon, and Manish Raghavan. "Algorithmic monoculture and social welfare." Proceedings of the National Academy of Sciences 118, no. 22 (2021): e2018340118. http://dx.doi.org/10.1073/pnas.2018340118.

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As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate

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Duran, Sergi Gálvez. "Opening the Black-Box in Private-Law Employment Relationships: A Critical Review of the Newly Implemented Spanish Workers’ Council’s Right to Access Algorithms." Global Privacy Law Review 4, Issue 1 (2023): 17–30. http://dx.doi.org/10.54648/gplr2023003.

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Article 22 of the General Data Protection Regulation (GDPR) provides individuals with the right not to be subject to automated decisions. In this article, the author questions the extent to which the legal framework for automated decision-making in the GDPR is attuned to the employment context. More specifically, the author argues that an individual’s right may not be the most appropriate approach to contesting artificial intelligence (AI) based decisions in situations involving dependency contracts, such as employment relationships. Furthermore, Article 22 GDPR derogations rarely apply in the

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Morchhale, Yogesh. "Ethical Considerations in Artificial Intelligence: Addressing Bias and Fairness in Algorithmic Decision-Making." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31693.

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The expanding use of artificial intelligence (AI) in decision-making across a range of industries has given rise to serious ethical questions about prejudice and justice. This study looks at the moral ramifications of using AI algorithms in decision-making and looks at methods to combat prejudice and advance justice. The study investigates the underlying causes of prejudice in AI systems, the effects of biased algorithms on people and society, and the moral obligations of stakeholders in reducing bias, drawing on prior research and real-world examples. The study also addresses new frameworks a

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Starke, Christopher, Janine Baleis, Birte Keller, and Frank Marcinkowski. "Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature." Big Data & Society 9, no. 2 (2022): 205395172211151. http://dx.doi.org/10.1177/20539517221115189.

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Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thor

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Hoeppner, Sven, and Martin Samek. "Procedural Fairness as Stepping Stone for Successful Implementation of Algorithmic Decision-Making in Public Administration: Review and Outlook." AUC IURIDICA 70, no. 2 (2024): 85–99. http://dx.doi.org/10.14712/23366478.2024.24.

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Algorithmic decision-making (ADM) is becoming more and more prevalent in everyday life. Due to their promise of producing faster, better, and less biased decisions, automated and data-driven processes also receive increasing attention in many different administrative settings. However, as a result of human mistakes ADM also poses the threat of producing unfair outcomes. Looming algorithmic discrimination can undermine the legitimacy of administrative decision-making. While lawyers and lawmakers face the age-old question of regulation, many decision-makers tasked with designing ADM for and impl

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Saxena, Devansh, Karla Badillo-Urquiola, Pamela J. Wisniewski, and Shion Guha. "A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–41. http://dx.doi.org/10.1145/3476089.

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Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between human discretion, bureaucratic processes, and algorithmic decision-making by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework

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Zerilli, John, Alistair Knott, James Maclaurin, and Colin Gavaghan. "Algorithmic Decision-Making and the Control Problem." Minds and Machines 29, no. 4 (2019): 555–78. http://dx.doi.org/10.1007/s11023-019-09513-7.

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AbstractThe danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts h

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Dissertations / Theses on the topic "Algorithmic decision systems"

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Shcherban, V. Yu. "Algorithmic and programmatic providing decision systems of linear equalizations." Thesis, Київський національний університет технологій та дизайну, 2019. https://er.knutd.edu.ua/handle/123456789/14586.

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Böhnlein, Toni [Verfasser]. "Algorithmic Decision-Making in Multi-Agent Systems: Votes and Prices / Toni Böhnlein." München : Verlag Dr. Hut, 2018. http://d-nb.info/1164294113/34.

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Björklund, Pernilla. "The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain." Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442122.

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The healthcare sector has become a key area for the development and application of new technology and, not least, Artificial Intelligence (AI). New reports are constantly being published about how this algorithm-based technology supports or performs various medical tasks. These illustrates the rapid development of AI that is taking place within healthcare and how algorithms are increasingly involved in systems and medical devices designed to support medical decision-making. The digital revolution and the advancement of AI technologies represent a step change in the way healthcare may be deliv

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Fairley, Andrew. "Information systems for tactical decision making." Thesis, University of Liverpool, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241479.

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Weingartner, Stephan G. "System development : an algorithmic approach." Virtual Press, 1987. http://liblink.bsu.edu/uhtbin/catkey/483077.

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The subject chosen to develop this thesis project on is developing an algorithm or methodology for system selection. The specific problem studied involves a procedure to determine anion computer system alternative is the best choice for a given user situation.The general problem to be addressed is the need for one to choose computing hardware, software, systems, or services in a -Logical approach from a user perspective, considering cost, performance and human factors. Most existing methods consider only cost and performance factors, combining these factors in ad hoc, subjective fashions to re

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Manongga, D. H. F. "Using genetic algorithm-based methods for financial analysis." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320950.

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Bacak, Hikmet Ozge. "Decision Making System Algorithm On Menopause Data Set." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12612471/index.pdf.

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Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process

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Wan, Min. "Decision diagram algorithms for logic and timed verification." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077981&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268242250&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2008.<br>Includes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 166-170). Also issued in print.

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Raboun, Oussama. "Multiple Criteria Spatial Risk Rating." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED066.

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La thèse est motivée par une étude de cas intéressante liée à l’évaluation du risque nucléaire. Le cas d’étude consiste à évaluer l’impact d’un accident nucléaire survenu dans le milieu marin. Ce problème comporte des caractéristiques spatiales, différents enjeux économiques et environnementaux, des connaissances incomplètes sur les potentiels acteurs et un nombre élevé de scénarios d’accident possibles. Le cas d’étude a été résolu en utilisant différentes techniques d’analyse décisionnelle telles que la comparaison des loteries et les outils MCDA (Multiple Criteria Decision Analysis).Une nouv

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Schroeder, Pascal. "Performance guaranteeing algorithms for solving online decision problems in financial systems." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0143.

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Cette thèse contient quelques problèmes de décision financière en ligne et des solutions. Les problèmes sont formulés comme des problèmes en ligne (OP) et des algorithmes en ligne (OA) sont créés pour résoudre. Comme il peut y avoir plusieurs OAs pour le même OP, il doit y avoir un critère afin de pouvoir faire des indications au sujet de la qualité d’un OA. Dans cette thèse ces critères sont le ratio compétitif (c), la différence compétitive (cd) et la performance numérique. Un OA qui a un c ou cd plus bas qu’un autre est à préférer. Un OA qui possède le c le plus petit est appelé optimal. No

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Books on the topic "Algorithmic decision systems"

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Tijms, H. C. Stochastic models: An algorithmic approach. Wiley, 1994.

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Szapiro, Tomasz, and Janusz Kacprzyk, eds. Collective Decisions: Theory, Algorithms And Decision Support Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84997-9.

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Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. Springer London, 2013.

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Koukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. UMIST, 1996.

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A, Grundel Don, Murphey Robert, and Pardalos P. M. 1954-, eds. Theory and algorithms for cooperative systems. World Scientific, 2004.

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Zilio, Daniel C. Physical database design decision algorithms and concurrent reorganization for parallel database systems. University of Toronto, Dept. of Computer Science, 1998.

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Center, Langley Research, ed. An algorithm for integrated subsystem embodiment and system synthesis. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Center, Langley Research, ed. An algorithm for integrated subsystem embodiment and system synthesis. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed

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Topolsky, Nikolay, and Valeriy Vilisov. Methods, models and algorithms in security systems: machine learning, robotics, insurance, risks, control. Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02072-2.

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The monograph examines topical issues of decision support and management in safety systems for fire and emergency situations through the use of innovative approaches and tools for operations research, artificial intelligence, robotics and management methods in organizational systems. The monograph is intended for faculty, researchers, graduate students (adjuncts) and doctoral students, as well as for undergraduates, students and listeners of educational organizations, all those who are interested in the problems of decision support and management in security systems.

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Book chapters on the topic "Algorithmic decision systems"

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Prestwich, Steve D., S. Armagan Tarim, Roberto Rossi, and Brahim Hnich. "Neuroevolutionary Inventory Control in Multi-Echelon Systems." In Algorithmic Decision Theory. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04428-1_35.

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Kumova, Bora İ., and Hüseyin Çakır. "Algorithmic Decision of Syllogisms." In Trends in Applied Intelligent Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13025-0_4.

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Rubel, Alan, Adam Pham, and Clinton Castro. "Agency Laundering and Algorithmic Decision Systems." In Information in Contemporary Society. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15742-5_56.

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Mody, Uchit, Mahir Mehta, Udit Mody, Soham Shah, Deev Patel, and Kavita Kelkar. "Comparative Analysis of Reinforcement Learning Algorithms for Decision Making in Algorithmic Trading." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2179-8_31.

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Seeger, Tessa. "Axiomatic and Algorithmic Study on Different Areas of Collective Decision Making." In Multi-Agent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20614-6_27.

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Zoomer, Thijmen, Dolf van der Beek, Coen van Gulijk, and Jan Harmen Kwantes. "Algorithmic Management and Occupational Safety: The End Does not Justify the Means." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40997-4_12.

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Acharya, Shubhashree, Harshal Anil Salunkhe, and M. Mathiyarasan. "Navigating Financial Waters: Exploring the Intersection of Algorithmic Trading and Market Liquidity Dynamics." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67890-5_48.

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De Stefano, Valerio. "Algorithmic Bosses and What to Do About Them: Automation, Artificial Intelligence and Labour Protection." In Studies in Systems, Decision and Control. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45340-4_7.

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Miller, Gloria J. "Artificial Intelligence Project Success Factors—Beyond the Ethical Principles." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98997-2_4.

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AbstractThe algorithms implemented through artificial intelligence (AI) and big data projects are used in life-and-death situations. Despite research that addresses varying aspects of moral decision-making based upon algorithms, the definition of project success is less clear. Nevertheless, researchers place the burden of responsibility for ethical decisions on the developers of AI systems. This study used a systematic literature review to identify five categories of AI project success factors in 17 groups related to moral decision-making with algorithms. It translates AI ethical principles in

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Baumeister, Jan, Bernd Finkbeiner, Frederik Scheerer, Julian Siber, and Tobias Wagenpfeil. "Stream-Based Monitoring of Algorithmic Fairness." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90643-5_4.

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Abstract Automatic decision and prediction systems are increasingly deployed in applications where they significantly impact the livelihood of people, such as for predicting the creditworthiness of loan applicants or the recidivism risk of defendants. These applications have given rise to a new class of algorithmic-fairness specifications that require the systems to decide and predict without bias against social groups. Verifying these specifications statically is often out of reach for realistic systems, since the systems may, e.g., employ complex learning components, and reason over a large

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Conference papers on the topic "Algorithmic decision systems"

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Aminou, Loubna, Abdelaziz Daaif, Maha Soulami, Abderrahim Chalfaouat, and Mohamed Youssfi. "Converging human and algorithmic biases in the hiring decision-making process." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620077.

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Madamombe, Tinashe Malvern, Justice Kasiroori, Strinivasan Soondrasan Pillay, and Takunda John Chirau. "Algorithmic Folk Theories and Their Influence on Decision-Making in Data-Driven Monitoring and Evaluation Systems." In 2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF). IEEE, 2025. https://doi.org/10.1109/aicconf64766.2025.11064009.

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Tajima, Keito, Naoki Ichijo, Yuta Nakahara, Koshi Shimada, and Toshiyasu Mathushima. "An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10830978.

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Sherman, Jason A. F., Natalie M. Isenberg, John D. Siirola, and Chrysanthos E. Gounaris. "Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.142058.

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In this work, we present recent algorithmic and implementation advances of the nonconvex two-stage robust optimization solver PyROS. Our advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the formulations and/or initializations of the various subproblems used by the underlying cutting set algorithm, and extensions to the pre-implemented uncertainty set interfaces. The effectiveness of PyROS is demonstrated through the results of an original benchmarking study on a library of over 8,500 small-scale instances, with variations in the nonlin

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Roberge, P. R., and K. R. Trethewey. "An Indexing System of Corrosion Failures for Case-Based Reasoning." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96359.

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Abstract The difficulties associated with the committal of human expert knowledge to computers for the development of more effective knowledge-based systems have raised the possibility of mimicking the process of reasoning from previous experiences. Experts are known to make proficient decisions based more upon analogy with similar events and situations than the kind of sequential mechanisms used in many algorithmic approaches. For many years, both law and business schools have used cases as the foundation for knowledge in their respective disciplines. Computer reasoning by analogy, a techniqu

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Nusser, Jeffrey K., Darryl J. Stimson, Eric Herzberg, and Charles A. Babish. "Data-Driven Corrosion Prevention and Control Decisions for the USAF." In SSPC 2017 Greencoat. SSPC, 2017. https://doi.org/10.5006/s2017-00041.

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Abstract Corrosion significantly impacts1 safety, availability and sustainment costs of U.S. Air Force (AF) systems and equipment. System downtime due to corrosion maintenance decreases the availability of systems to perform their National defense mission and drives the need for more aircraft and associated logistics tail. In addition, the AF spends about $5.5 Billion per year, about 21 percent of the annual AF maintenance budget, on corrosion maintenance. This cost exceeds the annual Pentagon budget for the campaign against the Islamic State.2 Because of these significant impacts, AF leaders

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Zeiner, Herwig, Lucas Paletta, and Gustavo Vieira. "The Algorithmic Fairness Challenge in Decision-Making." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006103.

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The increasing use of automated decision-making systems has given rise to concerns about fairness. This paper examines the major principles of fairness in decision-making, and it discusses the challenges of implementing fairness principles in practice, such as the trade-offs between different types of fairness and the difficulty of measuring fairness. Finally, the paper puts forward several proposals for promoting fairness in decision-making. These include the use of transparent and explainable algorithms, the involvement of stakeholders in the design of decision-making systems, and the establ

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Plambeck, Swantje, Gorschwin Fey, Jakob Schyga, Johannes Hinckeldeyn, and Jochen Kreutzfeldt. "Explaining Cyber-Physical Systems Using Decision Trees." In 2022 2nd International Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems (CAADCPS). IEEE, 2022. http://dx.doi.org/10.1109/caadcps56132.2022.00006.

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Schecter, Aaron, Eric Bogert, and Nina Lauharatanahirun. "Algorithmic appreciation or aversion? The moderating effects of uncertainty on algorithmic decision making." In CHI '23: CHI Conference on Human Factors in Computing Systems. ACM, 2023. http://dx.doi.org/10.1145/3544549.3585908.

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Haid, Charlotte, Alicia Lang, and Johannes Fottner. "Explaining algorithmic decisions: design guidelines for explanations in User Interfaces." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003764.

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Artificial Intelligence (AI)-based decision support is becoming a growing issue in manufacturing and logistics. Users of AI-based systems have the claim to understand the decisions made by the systems. In addition, users like workers or managers, but also works councils in companies, demand transparency in the use of AI. Given this background, AI research faces the challenge of making the decisions of algorithmic systems explainable. Algorithms, especially in the field of AI, but also classical algorithms do not provide an explanation for their decision. To generate such explanations, new algo

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Reports on the topic "Algorithmic decision systems"

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Lewis, Dustin, Naz Modirzadeh, and Gabriella Blum. War-Algorithm Accountability. Harvard Law School Program on International Law and Armed Conflict, 2016. http://dx.doi.org/10.54813/fltl8789.

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In War-Algorithm Accountability (August 2016), we introduce a new concept—war algorithms—that elevates algorithmically-derived “choices” and “decisions” to a, and perhaps the, central concern regarding technical autonomy in war. We thereby aim to shed light on and recast the discussion regarding “autonomous weapon systems” (AWS). We define “war algorithm” as any algorithm that is expressed in computer code, that is effectuated through a constructed system, and that is capable of operating in relation to armed conflict. In introducing this concept, our foundational technological concern is the

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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-se

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Buchanan, Ben. The AI Triad and What It Means for National Security Strategy. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20200021.

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One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

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Ludwig, Jens, and Sendhil Mullainathan. Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29267.

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Kulhandjian, Hovannes. Smart Robot Design and Implementation to Assist Pedestrian Road Crossing. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2353.

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This research focuses on designing and developing a smart robot to assist pedestrians with road crossings. Pedestrian safety is a major concern, as highlighted by the high annual rates of fatalities and injuries. In 2020, the United States recorded 6,516 pedestrian fatalities and approximately 55,000 injuries, with children under 16 being especially vulnerable. This project aims to address this need by offering an innovative solution that prioritizes real-time detection and intelligent decision-making at intersections. Unlike existing studies that rely on traffic light infrastructure, our appr

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject

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Lagutin, Andrey, and Tatyana Sidorina. SYSTEM OF FORMATION OF PROFESSIONAL AND PERSONAL SELF-GOVERNMENT AMONG CADETS OF MILITARY INSTITUTES. Science and Innovation Center Publishing House, 2020. http://dx.doi.org/10.12731/self-government.

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When carrying out professional activities, officers of the VNG of the Russian Federation are often in difficult, stressful, emotionally stressful situations associated with the use of weapons as a particularly dangerous means of destruction. The right to use a weapon by an officer makes him responsible for its use. And therefore requires the officer to make a balanced optimal decision, which is associated with the risk and transience of events, and in which no mistake can be made, since the price of it can be someone's life. It is at such a moment that it is important that the officer has stab

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Hashemi, Sara, Hengameh Ferdosian, and Hadi Zamanian. Accuracy of artificial intelligence in CT interpretation in covid-19: a systematic review protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0048.

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Review question / Objective: The aim of this systematic review is to compare the accuracy of artificial intelligence algorithms with radiologist panels in CT interpretation in covid-19. Condition being studied: COVID-19 disease was reported as the cause of the outbreak of pneumonia at the end of 2019. One of the main complications of COVID-19 is pulmonary involvement which could be diagnosed by CT-scan dominantly. Because of the increasing rate of these patients along with considering patients in remote areas, CT interpretations are a heavy burden on radiologists. Therefore artificial intellig

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Baader, Franz, and Ralf Küsters. Matching Concept Descriptions with Existential Restrictions Revisited. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.98.

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An abridged version of this technical report has been submitted to KR 2000. Matching of concepts against patterns is a new inference task in Description Logics, which was originally motivated by applications of the CLASSIC system. Consequently, the work on this problem was until now mostly concerned with sublanguages of the Classic language, which does not allow for existential restrictions. Motivated by an application in chemical process engineering, which requires a description language with existential restrictions, this paper investigates the matching problem in Description Logics with exi

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