On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity

Impact of current, National Dietary Guidelines and alternative diets on greenhouse gas emissions in Argentina

Diets have become an increasingly important driver of environmental pressures due to greenhouse gas emissions (GHGE), land use and other indicators of environmental impact associated with food production. In the present study we analyse the GHGE and the potential climate change mitigation through dietary changes in a country with high beef consumption, to contribute to the debate on what constitutes a healthy and sustainable diet. Data collected in the National Survey of Household Income and Expenditure 2012/2013 was used to estimate the composition of the current diet in Argentina, and four dietary scenarios were developed following the nutritional recommendations of the National Dietary Guidelines (NDG). We found that the GHGE related to the current Argentinian diet are very high (5.48 ± 1.71 kg CO 2 -eq/person/day), with beef production contributing to the largest share of emissions (71%). The NDG suggest a 50% reduction of total daily intake of meats compared to current consumption, which, if adopted, would reduce GHGE in 28%, to 3.95 ± 0.96. Further reductions in GHGE appear possible while maintaining a healthy and balanced diet. The scenarios with non-ruminant meats and lacto-ovo vegetarian lead to similar GHGE, 2.11 ± 0.41 and 1.73 ± 0.37 kg CO 2 -eq/day/person, respectively; and the vegan diet results in the lowest, 1.47 ± 0.34 kg CO 2 -eq/day/person. Indicators for nutrient efficiencies were also developed. All nutrient efficiencies decreased in diets with bovine meat with respect to the non-ruminant, vegetarian and vegan ones. The results of this study therefore indicate that a set of dietary changes would significantly contribute to lower GHGE. Argentina's NDG should include the environmental impacts of food consumption with the aim of raising consumer awareness.Fil: Arrieta, Ezequiel Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Gonzalez, Alejandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales.; Argentin

A Structural Misclassifcation Model to Estimate the Impact of Physician Incentives on Healthcare Utilization

The issue of over-utilization of medical procedures has generated strong debate in the United States. It is well acknowledged that, in the agency relationship between physicians and patients, the informational advantage gives doctors an incentive to deviate from the appropriate treatment as defined for a patient's health status, thus incurring over- or under- utilization. However, the empirical consequence of this problem has not been adequately considered. In particular, physician agency breaks the correspondence between appropriate treatment and observed treatment, generating a problem whose characteristics and efects on estimation are analogous to a classifcation error. However, the error is non-random. Empirical literature that does not consider the misclassifcation problem understates the impact of clinical and non-clinical factors on healthcare utilization. This paper proposes a structural misclassification model in which the physician behavior is modeled to characterize the structure of the measurement error. The model captures the interaction between a physician's incentives and a patient's health status, and returns consistent estimators. It also lets us identify the degree of deviation from appropriate treatment (misclassifcation probability) due to physician incentives, and to compute risk-adjusted utilization rates based on clinical factors only. The model is applied to the cesarean section deliveries performed in the state of New Jersey during the 1999-2002 period. Our results show a moderate but growing rate of non-clinically required c-sections of around 3.2%. We conclude that the growth of the c-section rates in New Jersey over these years is explained mainly by non-clinical factors

Sistema de información geográfica para valuadores de bienes inmuebles

Trabajo Final de Graduación (Licenciatura en Ingeniería en Construcción) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Construcción, 2018.El propósito de este proyecto es establecer la estructura de datos adecuada que debe tener un sistema de información geográfica, desde el punto de vista específico de la valoración de bienes inmuebles. En la actualidad los valuadores necesitan de nuevas tecnologías que les permitan el manejo adecuado de la información que sirve de base para los avalúos. Los sistemas de información geográfica son una de las formas más adecuadas para el manejo de grandes cantidades de datos referenciados geográficamente. La estructura del sistema de información geográfica es producto del análisis de los datos necesarios que, según las Normas Internacionales de Valuación, son la base para la realización de las relaciones que establecen el valor más adecuado de un bien inmueble por el método comparativo. Se muestran los principios teóricos de la valuación inmobiliaria y con base en ellos se realiza la recolección de la información necesaria para la integración de la base de datos y su posterior integración en un sistema de información geográfica. Finalmente, se estableció un sistema de información geográfica para valuadores de bienes inmuebles, específicamente de la zona del cantón central de Cartago en sus distritos primero y segundo

Greenhouse gas emissions and energy efficiencies for soybeans and maize cultivated in different agronomic zones: A case study of Argentina

Of all human activities, agriculture has one of the highest environmental impacts, particularly related to Greenhouse Gas (GHG) emissions, energy use and land use change. Soybean and maize are two of the most commercialized agricultural commodities worldwide. Argentina contributes significantly to this trade, being the third major producer of soybeans, the first exporter of soymeal and soybean oil, and the third exporter of maize. Despite the economic importance of these crops and the products derived, there are very few studies regarding GHG emissions, energy use and efficiencies associated to Argentinean soybean and maize production. Therefore, the aim of this work is to determine the carbon and energy footprint, as well as the carbon and energy efficiencies, of soybeans and maize produced in Argentina, by analyzing 18 agronomic zones covering an agricultural area of 1.53 million km2. Our results show that, for both crops, the GHG and energy efficiencies at the Pampean region were significantly higher than those at the extra-Pampean region. The national average for production of soybeans in Argentina results in 6.06 ton/ton CO2-eq emitted to the atmosphere, while 0.887 ton of soybean were produced per GJ of energy used; and for maize 5.01 ton/ton CO2-eq emitted to the atmosphere and 0.740 ton of maize were produced per each GJ of energy used. We found that the large differences on yields, GHGs and energy efficiencies between agronomic regions for soybean and maize crop production are mainly driven by climate, particularly mean annual precipitation. This study contributes for the first time to understand the carbon and energy footprint of soybean and maize production throughout several agronomic zones in Argentina. The significant differences found in the productive efficiencies questions on the environmental viability of expanding the agricultural frontier to less suitable lands for crop production.Fil: Arrieta, Ezequiel Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Cuchietti, Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Cabrol, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Humanidades. Universidad Nacional de Córdoba. Instituto de Humanidades; ArgentinaFil: Gonzalez, Alejandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentin

Obesity in Latin America, a scoping review of public health prevention strategies and an overview of their impact on obesity prevention

Objective: To describe the strategies implemented in seventeen Latin American countries for obesity prevention and to provide an overview of their impact. Design: A thorough search of strategies and their impact was done through an Internet search, governmental webpages, reports and research articles in English, Spanish and Portuguese. Setting: Latin America (not including the Caribbean countries). Participants: Any. Results: The Ministry of Health is the main oversight for obesity prevention, with six countries having a specific structure for this. Regular obesity monitoring occurs in a few countries, and thirteen countries have a national obesity prevention plan. The main regulations being implemented/designed are front-of-package labelling (sixteen countries), school environment (fifteen countries), school nutrition education (nine countries), promotion of physical activity level (nine countries) and sugar-sweetened beverage tax (eight countries). All countries have dietary guidelines. The main community-based programmes being implemented are school meals (seventeen countries), complementary nutrition (eleven countries), nutrition education (fourteen countries), promotion of physical activity (nine countries) and healthy environments (nine countries). Most of these strategies have not been evaluated. The few with positive results have used a coordinated, multi-disciplinary and multi-sector approach, with legislation and executive-level support. Conclusions: Important obesity prevention strategies are being implemented in the seventeen Latin American countries included in the present review. However, few have been evaluated to assess their impact on preventing obesity. This information can help assess that actions can be generalised to other countries within the region and can help inform how to prevent obesity in different settings