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  1. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
    This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...)
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  • Natural language interfaces and strategic computing.Geoffrey K. Pullum - 1987 - AI and Society 1 (1):47-58.
    Modern weaponry is often too complex for unaided human operation, and is largely or totally controlled by computers. But modern software, particularly artificial intelligence software, exhibits such complexity and inscrutability that there are grave dangers associated with its use in non-benign applications. Recent efforts to make computer systems more accessible to military personnel through natural language processing systems, as proposed in the Strategic Computing Initiative of the Defense Advanced Research Projects Agency, increases rather than decreases the dangers of unpredictable behavior. (...)
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  • Accountability in a computerized society.Helen Nissenbaum - 1996 - Science and Engineering Ethics 2 (1):25-42.
    This essay warns of eroding accountability in computerized societies. It argues that assumptions about computing and features of situations in which computers are produced create barriers to accountability. Drawing on philosophical analyses of moral blame and responsibility, four barriers are identified: 1) the problem of many hands, 2) the problem of bugs, 3) blaming the computer, and 4) software ownership without liability. The paper concludes with ideas on how to reverse this trend.
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  • Deductive program verification (a practitioner's commentary).David A. Nelson - 1992 - Minds and Machines 2 (3):283-307.
    A proof of ‘correctness’ for a mathematical algorithm cannot be relevant to executions of a program based on that algorithm because both the algorithm and the proof are based on assumptions that do not hold for computations carried out by real-world computers. Thus, proving the ‘correctness’ of an algorithm cannot establish the trustworthiness of programs based on that algorithm. Despite the (deceptive) sameness of the notations used to represent them, the transformation of an algorithm into an executable program is a (...)
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  • The PROTEVS approach: A short presentation of background, principles and methods. [REVIEW]Siv Friis - 1995 - AI and Society 9 (2-3):193-207.
    Prototyping is not a new approach to computer-based information system development. It is just one technique among many used in system design. What might be new is for what purpose prototyping is used. The purpose could be to achieve a more user controlled system development and to give the future users a tool that will enable them to fully participate in not only the work with requirements specifications, but also in the actual systems design and organisational change. This paper describes (...)
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  • Philosophical aspects of program verification.James H. Fetzer - 1991 - Minds and Machines 1 (2):197-216.
    A debate over the theoretical capabilities of formal methods in computer science has raged for more than two years now. The function of this paper is to summarize the key elements of this debate and to respond to important criticisms others have advanced by placing these issues within a broader context of philosophical considerations about the nature of hardware and of software and about the kinds of knowledge that we have the capacity to acquire concerning their performance.
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  • The philosophy of computer science.Raymond Turner - 2013 - Stanford Encyclopedia of Philosophy.
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  • Ciencia de la computación y filosofía: unidades de análisis del software.Juan Manuel Durán - 2018 - Principia 22 (2):203-227.
    Una imagen muy generalizada a la hora de entender el software de computador es la que lo representa como una “caja negra”: no importa realmente saber qué partes lo componen internamente, sino qué resultados se obtienen de él según ciertos valores de entrada. Al hacer esto, muchos problemas filosóficos son ocultados, negados o simplemente mal entendidos. Este artículo discute tres unidades de análisis del software de computador, esto es, las especificaciones, los algoritmos y los procesos computacionales. El objetivo central es (...)
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