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Thesis (M.S. in Systems Engineering)--Naval Postgraduate School, September 2009. / Thesis Advisor(s): Goshorn, Rachel ; Goshorn, Deborah. "September 2009." Description based on title screen as viewed on November 5, 2009. Author(s) subject terms: Smart Sensor Networks, Artificial Intelligence, Distributed Artificial Intelligence, Multiagent Systems, Network-centric Warfare, Network-centric Operations, Systems Engineering, Network-centric Systems Engineering, System of Systems. Includes bibliographical references (p. 85-89). Also available in print.

Law and technology regularly conflict. The reasons for this are several and complex. Some conflicts are trivial and straightforwardly resolvable. Others, such as the creation of artificial minds, are not. History indicates that when law and technology conflict; both systems can adapt—often over periods of time—to new social circumstances and continue performing their societal functions. Simply: law and technology co-evolve. However, if the legal system is to retain its autonomous role in society, what are its adaptive limits in the context of profound, and perhaps unprecedented, technological changes? My thesis addresses the question of whether, and if so, to what extent, the legal system can respond to ‘conflicts’ with increasingly complex and legally problematic technological change. It draws on theories of legal and social evolution—particularly the Social Systems Theory (SST) of Niklas Luhmann—to explore the notion of a ‘lag’ in the legal system’s ability to respond to technological changes and ‘shocks’. It evaluates the claim that the legal system’s ‘lagged’ response to technological change is a deficit of its functioning. ‘Lag’ may be both good and bad. It allows the law to be self-referential while also limiting its effectiveness in controlling other sub-systems. Thus there is an implicit intersystemic trade-off. The hypothesis here: ‘lag’ is an endogenous legal advantage that helps to ensure the legal system’s autonomy, as well as the continuity of legal processes that help ameliorate potentially harmful or undesirable outcomes of science and technology on society and the individual. The legal system can adjust to technological change. However, it can only adjust its internal operations, which takes time and is constrained by the need to maintain legal autonomy—or in SST terms—sits autopoiesis. The signs of this adjustment are the conceptual evolution of legal concepts and processes related to new technological changes and risks, among other things. A close reading of Anglo-American legal history and jurisprudence supports this. While legal systems are comparatively inflexible in response to new technologies—due to doctrinal ossification and reliance upon precedent and analogy in legal reasoning—an alternative outcome is possible: the disintegration of the boundary between law and technology and the consequential loss of legal autonomy. The disintegration of this boundary would consequentially reduce society’s capacity to mediate and regulate technological change, thus diminishing the autopoiesis of the legal system. A change of this kind would be signalled by what some identify as the emergence of a technological ordering—or a ‘rule of technology’—displacing and potentially subsuming the rule of law. My thesis evaluates evidence for these two scenarios—the self-renewing capacity of the legal system, on the one hand, or its disintegration in response to technological change, on the other. These opposing scenarios are evaluated using a social ontological study of technology generally, and a case study using Artificial Intelligence (AI) specifically, to identify and predict the co- evolutionary dynamics of the law/technology relationship and assess the extent to which the legal system can shape, and be shaped by, technological change. In assessing this situation, this thesis explores the nature of AI, its benefits and drawbacks, and argues that its proliferation may require a corresponding shift in the fundamental mechanics of law. As AI standardises across industries and social sub-systems, centralised authorities such as government agencies, corporations, and indeed legal systems, may lose the ability to coordinate and regulate the activities of disparate persons through ex post regulatory means. Consequentially, there is a pressing need to understand not just how AI interfaces with existing legal frameworks, but how legal systems must pre-adapt to oncoming, and predominately unexplored, legal challenges. This thesis argues that AI is an autopoietic technology, and that there is thus a corresponding need to understand its intersystemic effects if there is to be an effective societal governance regime for it. This thesis demonstrates that SST provides us with the shared theoretical grammar to start and sustain this dialogue.

Тема магистерской диссертации: Применение искусственного интеллекта при обработке анкетных данных. Магистерская диссертация выполнена на 98 страницах, содержит 13 таблиц, 30 рисунков, 62 использованных источника. Актуальность темы обусловлена большими трудозатратами и нерелевантными результатами обработки анкетных данных. Целью работы является автоматизация процесса отбора анкетных данных в дистрибутиве Python Anaconda с использованием алгоритмов машинного обучения. Задачи работы:  изучить системы искусственного интеллекта;  рассмотреть программное обеспечение для систем искусственного интеллекта;  создать и обучить классификатор для сортировки анкетных данных;  оценить экономическую эффективность создания проекта. Объект исследования  система сбора и обработки анкетных данных отдела диспетчеризации ВШЭМ УрФУ. Предмет исследования  автоматизация процесса ранжирования анкетных данных по релевантности. В первой главе рассматривается обработка данных с использованием систем искусственного интеллекта. Вторая глава посвящена разработке методики использования систем искусственного интеллекта при обработке анкетных данных. В третьей главе представлены системы искусственного интеллекта при сборе и обработке анкетных данных Результаты работы: практическим результатом работы стал разработанный классификатор, который определяет для заполненной анкеты: будет ли она учтена для анализа эффективности учебного процесса. / Theme of the master's thesis: Application of artificial intelligence in the processing of personal data. The master's thesis is done on 98 pages, contains of 13 tables, 30 figures, 62 literature sources. The relevance of the topic is due to the high labor costs and irrelevant results of the personal data processing. The purpose of the work is to automate the process of selecting personal data in the Python Anaconda distribution using machine learning algorithms. Objectives of work:  to explore artificial intelligence systems;  to consider software for artificial intelligence systems;  to create and train a classifier for the personal data sorting;  to evaluate the economic effectiveness of the project. The object of the study is the system for personal data collecting and processing of the dispatch department of the Higher School of Economics of UrFU. The subject of the research is the automation of the process of ranking the questionnaire data by relevance. The first chapter deals with the processing of data using artificial intelligence systems. The second chapter is devoted to the development of methods for the use of artificial intelligence systems in the processing of personal data. The third chapter presents artificial intelligence systems for the collection and processing of personal data The results of the work: the practical result of the work was the developed classifier, which defines for the completed questionnaire: it would be taken into account for impact analysis of the educational process.