Ensinando um robô a julgar: pragmática, discricionariedade e vieses no uso de aprendizado de máquina no judiciário

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Ciências Jurídicas. Direito.Este Trabalho de conclusão de curso explora algumas das questões relativas à implementação de ferramentas de aprendizado de máquina na administração da justiça. Em um primeiro momento, o trabalho discute as peculiaridades do aprendizado de máquina em face de outros modelos estatísticos, bem como quais são os requisitos necessários para que tais algoritmos possam ser utilizados na performance de argumentação jurídica (legal reasoning). Em seguida, resgatam-se algumas das discussões da teoria do direito e demonstra-se como elas estão relacionadas à filosofia da linguagem. Defende-se que tanto Hans Kelsen, quanto Herbert Hart, ainda que partam de caminhos diferentes, chegam ao problema do decisionismo e que isso é uma consequência do paradigma por eles adotado, a saber, a filosofia analítica. Adiante, discute-se como linguagens artificiais são capazes de assimilar o âmbito pragmático de linguagens naturais e de que forma processos decisórios humanos e algorítmicos são afetados por vieses. Por fim, elencam-se três tipos de uso mais recorrentes do aprendizado de máquina no judiciário.This final paper goes on some of the issues surrounding the usage of machine learning tools in the administration of justice. At first, are brought some of the peculiarities of machine learning in the face of other statistical models, as well as the prerequisites for such algorithms to perform legal reasoning. Then, some of the discussions of the theory of law are rescued and it is demonstrated how they relate to the philosophy of language. It is argued that both Hans Kelsen and Herbert Hart, though starting from different outsets, come to the problem of decisionism and that this is a consequence of their elected paradigm, namely the analytic philosophy. Ahead, it is discussed how artificial languages ​​are able to assimilate the pragmatic dimension of natural languages ​​and how human and algorithmic decision-making processes are affected by heuristics and biases. Finally, it is presented three most recurring ways of implementation of machine learning into the judiciary

Food groups and risk of coronary heart disease, stroke and heart failure : a systematic review and dose-response meta-analysis of prospective studies

Background: Despite growing evidence for food-based dietary patterns' potential to reduce cardiovascular disease risk, knowledge about the amounts of food associated with the greatest change in risk of specific cardiovascular outcomes and about the quality of meta-evidence is limited. Therefore, the aim of this meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups (whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages [SSB]) and the risk of coronary heart disease (CHD), stroke and heart failure (HF). Methods: We conducted a systematic search in PubMed and Embase up to March 2017 for prospective studies. Summary risk ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random effects model for highest versus lowest intake categories, as well as for linear and non-linear relationships. Results: Overall, 123 reports were included in the meta-analyses. An inverse association was present for whole grains (RRCHD: 0.95 (95% CI: 0.92-0.98), RRHF: 0.96 (0.95-0.97)), vegetables and fruits (RRCHD: 0.97 (0.96-0.99), and 0.94 (0.90-0.97); RRstroke: 0.92 (0.86-0.98), and 0.90 (0.84-0.97)), nuts (RRCHD: 0.67 (0.43-1.05)), and fish consumption (RRCHD: 0.88 (0.79-0.99), RRstroke: 0.86 (0.75-0.99), and RRHF: 0.80 (0.67-0.95)), while a positive association was present for egg (RRHF: 1.16 (1.03-1.31)), red meat (RRCHD: 1.15 (1.08-1.23), RRstroke: 1.12 (1.06-1.17), RRHF: 1.08 (1.02-1.14)), processed meat (RRCHD: 1.27 (1.09-1.49), RRstroke: 1.17 (1.02-1.34), RRHF: 1.12 (1.05-1.19)), and SSB consumption (RRCHD: 1.17 (1.11-1.23), RRstroke: 1.07 (1.02-1.12), RRHF: 1.08 (1.05-1.12)) in the linear dose-response meta-analysis. There were clear indications for non-linear dose-response relationships between whole grains, fruits, nuts, dairy, and red meat and CHD. Conclusion: An optimal intake of whole grains, vegetables, fruits, nuts, legumes, dairy, fish, red and processed meat, eggs and SSB showed an important lower risk of CHD, stroke, and HF

The accumulation of deficits approach to describe frailty

The advancing age of the participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study was the incentive to investigate frailty as a major parameter of ageing. The aim of this study was to develop a multidimensional tool to measure frailty in an ageing, free-living study population. The "accumulation of deficits approach" was used to develop a frailty index (FI) to characterize a sub-sample (N = 815) of the EPIC-Potsdam (EPIC-P) study population regarding the aging phenomenon. The EPIC-P frailty index (EPIC-P-FI) included 32 variables from the following domains: health, physical ability, psychosocial and physiological aspects. P-values were calculated for the linear trend between sociodemographic and life style variables and the EPIC-P-FI was calculated using regression analysis adjusted for age. The relationship between the EPIC-P-FI and age was investigated using fractional polynomials. Some characteristics such as age, education, time spent watching TV, cycling and a biomarker of inflammation (C-reactive protein) were associated with frailty in men and women. Interestingly, living alone, having no partner and smoking status were only associated with frailty in men, and alcohol use and physical fitness (VO2max) only in women. The generated, multidimensional FI, adapted to the EPIC-P study, showed that this cohort is a valuable source for further exploration of factors that promote healthy ageing

Optimal Uncertainty Quantification

We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. Although OUQ optimization problems are extremely large, we show that under general conditions they have finite-dimensional reductions. As an application, we develop \emph{Optimal Concentration Inequalities} (OCI) of Hoeffding and McDiarmid type. Surprisingly, these results show that uncertainties in input parameters, which propagate to output uncertainties in the classical sensitivity analysis paradigm, may fail to do so if the transfer functions (or probability distributions) are imperfectly known. We show how, for hierarchical structures, this phenomenon may lead to the non-propagation of uncertainties or information across scales. In addition, a general algorithmic framework is developed for OUQ and is tested on the Caltech surrogate model for hypervelocity impact and on the seismic safety assessment of truss structures, suggesting the feasibility of the framework for important complex systems. The introduction of this paper provides both an overview of the paper and a self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository Research Papers). See SIAM Review for higher quality figure

Dietary determinants of changes in waist circumference adjusted for body mass index - a proxy measure of visceral adiposity

Background Given the recognized health effects of visceral fat, the understanding of how diet can modulate changes in the phenotype “waist circumference for a given body mass index (WCBMI)”, a proxy measure of visceral adiposity, is deemed necessary. Hence, the objective of the present study was to assess the association between dietary factors and prospective changes in visceral adiposity as measured by changes in the phenotype WCBMI. Methods and Findings We analyzed data from 48,631 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Anthropometric measurements were obtained at baseline and after a median follow-up time of 5.5 years. WCBMI was defined as the residuals of waist circumference regressed on body mass index, and annual change in WCBMI (¿WCBMI, cm/y) was defined as the difference between residuals at follow-up and baseline, divided by follow-up time. The association between energy, energy density (ED), macronutrients, alcohol, glycemic index (GI), glycemic load (GL), fibre and ¿WCBMI was modelled using centre-specific adjusted linear regression, and random-effects meta-analyses to obtain pooled estimates. Men and women with higher ED and GI diets showed significant increases in their WCBMI, compared to those with lower ED and GI [1 kcal/g greater ED predicted a ¿WCBMI of 0.09 cm (95% CI 0.05 to 0.13) in men and 0.15 cm (95% CI 0.09 to 0.21) in women; 10 units greater GI predicted a ¿WCBMI of 0.07 cm (95% CI 0.03 to 0.12) in men and 0.06 cm (95% CI 0.03 to 0.10) in women]. Among women, lower fibre intake, higher GL, and higher alcohol consumption also predicted a higher ¿WCBMI. Conclusions Results of this study suggest that a diet with low GI and ED may prevent visceral adiposity, defined as the prospective changes in WCBMI. Additional effects may be obtained among women of low alcohol, low GL, and high fibre intake

Prediction of circulating adipokine levels based on body fat compartments and adipose tissue gene expression

BACKGROUND: Adipokines are hormones secreted from adipose tissue (AT), and a number of them have been established as risk factors for chronic diseases. However, it is not clear whether and to what extent adiposity, gene expression, and other factors determine their circulating levels. OBJECTIVES: To assess to what extent adiposity, as measured by the amount of subcutaneous AT (SAT) and visceral AT (VAT) using magnetic resonance imaging, and gene expression levels in SAT determine plasma concentrations of the adipokines adiponectin, leptin, soluble leptin receptor, resistin, interleukin 6, and fatty acid-binding protein 4 (FABP4). METHODS: We performed a cross-sectional analysis of 156 participants from the EPIC Potsdam cohort study and analyzed multiple regression models and partial correlation coefficients. RESULTS: For leptin and FABP4 concentrations, 81 and 45% variance were explained by SAT mass, VAT mass, and gene expression in SAT in multivariable regression models. For the remaining adipokines, AT mass and gene expression explained <16% variance of plasma concentrations. Gene expression in SAT was a less important predictor compared to AT mass. SAT mass was a better predictor than VAT mass for leptin (partial correlation r = 0.81, 95% confidence interval 0.75–0.86, vs. r = 0.58, 95% confidence interval 0.46–0.67), while differences between AT compartments were small for the other adipokines. CONLUSIONS: While plasma levels of leptin and FABP4 can be explained in a large and medium part by the amount of AT and SAT gene expression, surprisingly, these predictors explained only little variance for all other investigated adipokines

Plasma Fetuin-A Levels and the Risk of Type 2 Diabetes

OBJECTIVE—The liver-secreted protein fetuin-A induces insulin resistance in animals, and circulating fetuin-A is elevated in insulin resistance and fatty liver in humans. We investigated whether plasma fetuin-A levels predict the incidence of type 2 diabetes in a large prospective, population-based study

Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center