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Working paper
Soul and Machine (Learning)
In: NYU Stern School of Business
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Working paper
Machine Learning, Ethics and Law
Recent revelations concerning data firm Cambridge Analytica's illegitimate use of the data of millions of Facebook users highlights the ethical and, relatedly, legal issues arising from the use of machine learning techniques. Cambridge Analytica is, or was – the revelations brought about its demise - a firm that used machine learning processes to try to influence elections in the US and elsewhere by, for instance, targeting 'vulnerable' voters in marginal seats with political advertising. Of course, there is nothing new about political candidates and parties employing firms to engage in political advertising on their behalf, but if a data firm has access to the personal information of millions of voters, and is skilled in the use of machine learning techniques, then it can develop detailed, fine-grained voter profiles that enable political actors to reach a whole new level of manipulative influence over voters. My focus in this paper is not with the highly publicised ethical and legal issues arising from Cambridge Analytic's activities but rather with some important ethical issues arising from the use of machine learning techniques that have not received the attention and analysis that they deserve. I focus on three areas in which machine learning techniques are used or, it is claimed, should be used, and which give rise to problems at the interface of law and ethics (or law and morality, I use the terms "ethics" and "morality" interchangeably). The three areas are profiling and predictive policing (Saunders et al. 2016), legal adjudication (Zeleznikow, 2017), and machines' compliance with legally enshrined moral principles (Arkin 2010). I note that here, as elsewhere, new and emerging technologies are developing rapidly making it difficult to predict what might or might not be able to be achieved in the future. For this reason, I have adopted the conservative stance of restricting my ethical analysis to existing machine learning techniques and applications rather than those that are the object of speculation or even informed extrapolation (Mittelstadt et al. 2015). This has the consequence that what I might regard as a limitation of machine learning techniques, e.g. in respect of predicting novel outcomes or of accommodating moral principles, might be thought by others to be merely a limitation of currently available techniques. After all, has not the history of AI recently shown the naysayers to have been proved wrong? Certainly, AI has seen some impressive results, including the construction of computers that can defeat human experts in complex games, such as chess and Go (Silver et al. 2017), and others that can do a better job than human medical experts at identifying the malignancy of moles and the like (Esteva et al. 2017). However, since by definition future machine learning techniques and applications are not yet with us the general claim that current limitations will be overcome cannot at this time be confirmed or disconfirmed on the basis of empirical evidence.
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Machine Learning: Models And Algorithms
In: Machine Learning: Models And Algorithms, Quantitative Analytics, 2018
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Machine Learning for Sociology
In: Annual Review of Sociology, Band 45, S. 27-45
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Malware and Machine Learning
In: Intelligent Methods for Cyber Warfare; Studies in Computational Intelligence, S. 1-42
Predicting Terrorism with Machine Learning: Lessons from "Predicting Terrorism: A Machine Learning Approach"
In: Peace economics, peace science and public policy, Band 24, Heft 4
ISSN: 1554-8597
This paper highlights how machine learning can help explain terrorism. We note that even though machine learning has a reputation for black box prediction, in fact, it can provide deeply nuanced explanations of terrorism. Moreover, machine learning is not sensitive to the sometimes heroic statistical assumptions necessary when parametric econometrics is applied to the study of terrorism. This increases the reliability of explanations while adding contextual nuance that captures the flavor of individualized case analysis. Nevertheless, this approach also gives us a sense of the replicability of results. We, therefore, suggest that it further expands the role of science in terrorism research.
The Application of Machine Learning to Education
The field of education is constantly seeking more innovative and effective methods of teaching foundational knowledge to students. Organizations in both secondary and post-secondary education groups offer Physics Education Groups that try to better teach fundamental physics concepts to students in both university and high school. There are also government agencies such as the Educational Quality and Accountability Office (EQAO) in Ontario, which provides standardized tests to students. Although these standardized tests do not test the full capabilities and thought processes of students, they still provide insights into how students learn. Data from standardized EQAO tests can be analyzed to obtain crucial information about how to improve education standards by showing where resources can be allocated more effectively. One of the most powerful tools that can be used to analyze data is machine learning, which can find patterns and correlations in data that the human eye cannot see. This experiment used the linear regression algorithm to find correlations in data obtained from grade 9 EQAO mathematics tests from 50 schools in Ontario. The algorithm analyzed how students with varying scores answered a multiple-choice questionnaire at the end of the exam, which included statements such as "I am able to answer difficult mathematics questions." Based on the variables output from the machine learning algorithm, the importance of each statement was then ranked; this ranking can then lead to insights into how students learn, and how to better utilize resources. This experiment has shown that an elementary application of machine learning can lead to valuable insights into student learning and that more should be done to better analyze the abundant data in the education field. Le domaine de l'éducation recherche continuellement des méthodes innovatrices et efficaces pour l'enseignement de connaissances fondamentales aux étudiants. Les organisations de niveau secondaire et post-secondaire offrent des groupes d'éducation en physique qui essaient d'améliorer l'enseignement des concepts fondamentaux en physique aux étudiants à l'université ainsi qu'au secondaire. Il y a également des agences gouvernementales telles que l'Office de la qualité et de la responsabilité en éducation (OQRE) en Ontario, qui offre des tests standardisés aux étudiants. Bien que ces tests standardisés n'évaluent pas la pleine capacité et le processus de réflexion des élèves, ils offrent tout de même un aperçu des méthodes d'apprentissage. Les données des tests standardisés de l'OQRE peuvent être analysés afin d'obtenir des renseignements essentiels quant à la façon d'améliorer les normes d'éducation en démontrant où les ressources peuvent être attribuées plus efficacement. Un des outils les plus puissants qui peut être utilisé pour analyser les données est le l'apprentissage automatique, ce qui peut trouver des tendances et des corrélations à travers les données que l'œil humain ne peut percevoir. Cette expérience a utilisé l'algorithme à régression linéaire afin de trouver des corrélations dans les données obtenues des tests de mathématique de l'OQRE pour la mième année dans 50 écoles en Ontario. L'algorithme a analysé comment les étudiants possédant un résultat différent ont répondu à un questionnaire de questions à choix multiples à la fin de l'examen incluant des énoncés tels que « Je suis en mesure de répondre à des questions mathématiques dif ciles. » Selon les variables produits par l'algorithme d'apprentissage automatique, l'importance de chaque énoncé fut classée; ce classement peut ainsi mener à une compréhension envers l'apprentissage de l'étudiant, et comment maximiser les ressources. Cette expérience a démontré qu'une application élémentaire de l'apprentissage automatique peut mener à de précieux renseignements sur l'apprentissage des étudiants et que plus d'efforts doivent être accomplis afin de faciliter l'analyse des nombreuses données retrouvées dans le domaine de l'éducation.
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Machine Learning and the Rule of Law
In: Law as Data, Santa Fe Institute Press, ed. M. Livermore and D. Rockmore, 2019(16)
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Machine Learning in Asset Management
In: JFDS: https://jfds.pm-research.com/content/2/1/10
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Working paper
Machine Learning in Artificial Intelligence
In: International Journal of Advanced Research in Engineering and Technology, Band 11(6), Heft 2020
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Machine Learning Algorithms: Overview
In: International Journal of Advanced Research in Engineering and Technology, Band 11(9), Heft 2020
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'Un'Fair Machine Learning Algorithms
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Working paper
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