ABSTRACT Context: in recent years, studies have sought to analyze how intelligence and knowledge management processes are understood and applied in the context of public management, environments in which processes appear as a point to be explored to enhance decision-making quality. Objective: to analyze how public managers apply intelligence and knowledge management aiming at a higher decision quality. Method: based on a defined and validated research protocol, interviews were conducted with seventeen public managers in southern Brazil. For the analysis, the qualitative comparative analysis technique using fuzzy sets was applied. Results: the results suggest the importance of effective data, information, and knowledge management for the decision-making quality of public managers, demonstrating that the absence of decision-making quality is directly related to the absence or little use of knowledge management and intelligence elements in the public management. Conclusion: in addition to analyzing conditions and proposing ways to lead to greater quality in decision making by public managers, it was possible to contribute to the theme of knowledge management and intelligence in public management, as well as to benefit the government with paths to be consolidated and better explored.
Occupational fraud is a global problem that affects public and private organizations. One of the most common fraudulent activities in these organizations is corruption. Unfortunately, Puerto Rico is no exception. A review of the literature revealed that occupational fraud has increased in Puerto Rico since the 1980's. The main purpose of this article is to share some findings as purported by an investigation on corruption conducted in municipalities. Recommendations are offered to the government of Puerto Rico to prevent and combat corruption acts, according to the perception held by a group of external auditors who conducted compliance audits in the municipalities. The research was carried out mainly through a qualitative approach. It was intended to obtain a deep knowledge of an unfrequently research topic, which included the use of semi-structured interview conducted on experts, as the main strategy for data collection. ; El fraude ocupacional es un problema mundial que afecta las organizaciones públicas y privadas. Uno de los tipos principales de fraude ocupacional lo constituyen los actos de corrupción. Según la literatura, estos actos han aumentado en Puerto Rico a partir de la década de los 80. El propósito principal de este artículo es compartir algunos hallazgos relacionados con una investigación realizada sobre la corrupción en los municipios. Se ofrecen recomendaciones al gobierno de Puerto Rico para prevenir y combatir los actos de corrupción, de acuerdo con la percepción sostenida por un grupo de auditores externos, que realizaron auditorías de cumplimiento. La investigación se realizó principalmente mediante un enfoque cualitativo. Se pretendió obtener un conocimiento profundo de un tema poco estudiado, que incluyó el uso de la técnica de la entrevista semiestructurada realizada a expertos en el tema como estrategia principal para la recolección de datos.
Over the past few decades, management models have become important in the organizational field. Well implemented models provide benefits in all areas of organization and strengthening the company in the market. The present paper performs an analysis of the ACADEMIA DE BELLEZA YVONNE, therefore a management model is proposed according to the need of the organization to be a certifier of biosafety. The characteristics of the creation of the model for the company correspond to the case study and the use of descriptive research. The research is organized systemic with the aim of understanding the variables on the general, legal and stakeholder environment. In addition, headquarters managers are interviewed to learn about the organization's current problems. The findings allow the ACADEMIA to have the basis for solving some of the problems. These problems are related to the goal of becoming a certifier of biosafety, among which the need to implement and evaluate a series of proposed indicators stands out. These indicators belong to a management model that relates knowledge management and the balanced scorecard. In addition to the above, the proposed model has the necessary elements so that other types of organizations in the sector can replicate it ; Desde hace unas décadas los modelos de gestión han tomado importancia en el campo organizacional. Los modelos bien implementados proporcionan beneficios en todas las áreas de la organización y el fortalecimiento de la compañía en el mercado. Este trabajo presenta un modelo de gestión para que la ACADEMIA DE BELLEZA YVONNE se convierta en certificadora de bioseguridad. Las características de la creación del modelo para la empresa corresponden al estudio de caso y al uso de la investigación descriptiva. La investigación se ordena de manera sistémica con el objetivo de comprender las variables sobre el entorno general, legal y de los grupos de interés. Además, se entrevista a los administradores de la sede para conocer los problemas coyunturales de la organización. Los hallazgos permiten que la ACADEMIA tenga las bases para la solución de algunos de los problemas. Estos problemas se encuentran relacionados con el objetivo de convertirse en una certificadora de bioseguridad, entre los que destacan la necesidad de implementar y evaluar una serie de indicadores propuestos. Estos indicadores pertenecen a un modelo de gestión que relaciona la gestión del conocimiento y el cuadro de mando integral. Aunado a lo anterior, el modelo propuesto cuenta con los elementos necesarios para que otros tipos de organizaciones del sector puedan replicarlo. ; Maestría
Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant. ; Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligenceand from the European Union's Horizon 2020 research and innovationprogram under grant agreement 826078 (Feature Cloud). This publi-cation reflects only the authors' view and the European Commissionis not responsible for any use that may be made of the informationit contains; Natalia Díaz-Rodríguez is supported by the Spanish Gov-ernment Juan de la Cierva Incorporación contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Auderesearch leader grant; Javier Del Ser acknowledges funding supportfrom the Basque Government through the ELKARTEK program (3KIAproject, KK-2020/00049) and the consolidated research group MATH-MODE (ref. T1294-19); Wojciech Samek acknowledges funding Support from the European Union's Horizon 2020 research and innovationprogram under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579),Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via theToronto General and Western Hospital Foundation.
