scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

scholarly journals Preserving the rule of law in the era of artificial intelligence (AI)

2021 ◽  
Author(s):  
Stanley Greenstein
Keyword(s):  
Artificial Intelligence ◽  
Rule Of Law ◽  
Academic Discipline ◽  
Decision Makers ◽  
Human Flourishing ◽  
The Rule Of Law ◽  
Human Decision ◽  
Technological Developments ◽  
Made In ◽  
National Borders

AbstractThe study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society.

Artificial Intelligence for Decision-Makers

2019 ◽  
Vol 17 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Viktor H. Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools ◽  
Jel Classifications

ABSTRACT We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns. JEL Classifications: A29; C44; C45; D81; M41.

scholarly journals Artificial Intelligence, Algorithmic Pricing, and Collusion

2020 ◽  
Vol 110 (10) ◽  
pp. 3267-3297 ◽  
Author(s):  
Emilio Calvano ◽  
Giacomo Calzolari ◽  
Vincenzo Denicolò ◽  
Sergio Pastorello
Keyword(s):  
Artificial Intelligence ◽  
Price Competition ◽  
Decision Makers ◽  
Q Learning ◽  
Oligopoly Model ◽  
Goods And Services ◽  
Human Decision ◽  
Demand Changes

Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L13)

scholarly journals El término Educación Física en la postmodernidad: contribución de algunas perspectivas fenomenológicas (The term Physical Education in postmodernity: contribution of some phenomenological perspectives)

Retos ◽  
2019 ◽  
pp. 795-801
Author(s):  
Felipe Nicolás Mujica Johnson
Keyword(s):  
Physical Education ◽  
20Th Century ◽  
Human Body ◽  
Edmund Husserl ◽  
The Body ◽  
Edith Stein ◽  
Max Scheler ◽  
Cartesian Dualism ◽  
Living Body ◽  
Siglo Xx

La modernidad nos dejó como legado el descubrimiento de la subjetividad y una concepción mecanicista del cuerpo humano. Así, diferentes filósofos de la escuela fenomenológica han conservado el patrimonio de la subjetividad y han trabajado para superar el dualismo cartesiano. Sin embargo, los avances filosóficos por múltiples razones no siempre fecundan en el campo pedagógico, de modo que es fundamental promover una constante reflexión filosófica en el contexto de la Educación Física. Por esa razón, se ha desarrollado este ensayo que tiene por objetivo analizar la perspectiva del cuerpo humano en la corriente fenomenológica alemana del siglo XX, en concreto, en las ideas de Edmund Husserl, Max Scheler y Edith Stein. Por medio del estudio de sus diferentes obras, se puede mencionar que hay un concepto clave en sus ideas, que es el del cuerpo vivo. Este cuerpo expresaría el espíritu o el sujeto encarnado, que experimenta la realidad a través de su ser psicofísico. Entonces, el ser humano tendría en todas sus experiencias un cuerpo vivo pero, a su vez, también físico o material. Por consiguiente, de este análisis se concluye que para superar la concepción mecanicista del cuerpo en la Educación Física, no es necesario cambiar el nombre a dicha disciplina pedagógica, sino comprender que la materia del ser humano es inseparable de su subjetividad.Abstract: Modernity left us the discovery of subjectivity and a mechanistic conception of the human body as a legacy. Thus, different philosophers of the phenomenological school have preserved the patrimony of subjectivity and have worked to overcome the Cartesian dualism. However, philosophical advances do not always fertilize the pedagogical field for many reasons, so it is essential to promote a constant philosophical reflection in the context of Physical Education. For this reason, this essay has been developed with the aim to analyze the perspective of the human body in the German phenomenological stream of the 20th century, in particular, in the ideas of Edmund Husserl, Max Scheler, and Edith Stein. Through the study of their different works, it can be mentioned that there is a key concept in their ideas, i.e. the living body. This body would express the spirit or the embodied subject, who experiences reality through his/her psychophysical being. Then, the human being would have, in all his/her experiences, a living body, but also physical or material. Therefore, from this analysis it is concluded that to overcome the mechanistic conception of the body in Physical Education, it is not necessary to change the name of said pedagogical discipline, but to understand that the human being's matter is inseparable from its subjectivity.

scholarly journals Leadership in Higher Education – coping with AI and the turbulence of our times

2018 ◽  
Vol 12 (1) ◽  
pp. 683-694
Author(s):  
Mariana Nicolae ◽  
Elena E. Nicolae
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Big Data ◽  
Decision Makers ◽  
Human Intelligence ◽  
Higher Education System ◽  
Leadership In Higher Education ◽  
National Media ◽  
The World ◽  
Human Decision

Abstract Today’s world is clearly fractured whether we are looking at it through economic, political, cultural or educational lenses. This is in no way something new. The world has always been in this state, but the speed with which it reacted to real or perceived threats and tried to change accordingly was barely perceivable and, therefore, easier to adopt and adapt to. Today those changes happen with incredible speed and our reactions to them may not be informed or educated and are usually taken by leaders who are, at best, controversial and at worst obviously partial to their own, petty interests against the greater public good they vowed to serve. What can higher education do in such a world? Artificial intelligence (AI) is making huge progress and, although education at all levels is lagging behind in meaningfully adopting AI and working with it, the educational system is expected to react to a world divided by the fear of AI using big data, claiming jobs, and ushering in the era of loss of human supremacy or by the glorification of AI which is only a tool, fast developing indeed, but permanently controlled by human intelligence. Even if that human intelligence is concentrated into fewer and fewer human decision makers thus contributing to the already huge gap of inequality existing in today’s world. The present paper will explore issues related to the way in which the leadership of higher education chooses to handle today’s challenges and will use the home university of the authors to illustrate what happens in Romanian universities. The discussion will be informed by the authors’ own experience in the higher education system as well as by an analysis of various discourses and narratives belonging to different stakeholders, discussing those issues in various inter/national media. The paper will offer some recommendations.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Kontrast und Wissen

2009 ◽  
Vol 54 (1) ◽  
pp. 69-102
Author(s):  
Robin Rehm
Keyword(s):  
19Th Century ◽  
Edmund Husserl ◽  
Specific Knowledge ◽  
Max Scheler ◽  
Black And White ◽  
Black Circle ◽  
Black Cross ◽  
Definition Of ◽  
And Function ◽  
The 19Th Century

Kasimir Malewitschs suprematistische Hauptwerke ›Schwarzes Quadrat‹, ›Schwarzer Kreis‹ und ›Schwarzes Kreuz‹ von 1915 setzen sich aus schwarzen Formen auf weißem Grund zusammen. Der Typus des Schwarzweißbildes weist überraschende Parallelen zu den bildlichen Wahrnehmungsinstrumenten auf, die vom ausgehenden 18. bis Anfang des 20. Jahrhunderts in den Experimenten der Farbenlehre, physiologischen Optik und Psychologie verwendet worden sind. Die vorliegende Studie untersucht diese Parallelen in drei Schritten: Zunächst erfolgt eine allgemeine Charakterisierung des Schwarzweißbildes mit Hilfe des Kontrastbegriffs von Edmund Husserl. Des weiteren wird die Entstehung und Funktion des schwarzweißen Kontrastbildes in den Wissenschaften des 19. Jahrhunderts typologisch herausgearbeitet. Unter Berücksichtigung des Wissensbegriffs von Max Scheler wird abschließend die Spezifik des Wissens eruiert, das die Schwarzweißbilder sowohl in der Malerei Malewitschs als auch in den genannten Wissenschaften generieren. Malevich’s main Suprematist works, such as ›Black Square‹, ›Black Circle‹, and ›Black Cross‹ from 1915, consist of black shapes on white ground. Surprisingly this series of shapes strongly resembles scientific black-and-white images used for research on colour theory, physiological optics, and psychology throughout the 19th century. This paper examines the parallels between Malevich’s paintings and the scientific drawings in three steps: It first characterizes black-and-white images in general, using Edmund Husserl’s definition of the term ›contrast‹. Secondly, the paper investigates the development and function of black-and-white images as tools of perception in the sciences. It finally discusses the specific knowledge generated through Malevich’s art and through scientific black-and-white images, following Max Scheler’s phenomenological identification of knowledge.

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