Relational learning and fairness

This thesis will focus on relational learning in the modeling of text and user roles in networks, and the relative treatment of individuals as related to algorithmic fairness. With the exponential growth in social network data, the need for models of user interaction data is growing. This work presents a model which agglomerates users into archetypes based on topical modeling of the contents of their interactions. It further proposes models and a fairness metric for the creation of classifiers for individuals which control for the relative treatment of individualsStatistic

Certified Monotonic Neural Networks

Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensure monotonicity, which can be too restrictive/complicated, or enforce monotonicity by adjusting the learning process, which cannot provably guarantee the learned model is monotonic on selected features. In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem.This provides a new general approach for learning monotonic neural networks with arbitrary model structures. Our method allows us to train neural networks with heuristic monotonicity regularizations, and we can gradually increase the regularization magnitude until the learned network is certified monotonic. Compared to prior works, our approach does not require human-designed constraints on the weight space and also yields more accurate approximation. Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks

Tax Structure and Government Expenditures under Tax Equity Norms

We augment a standard tax model by concerns about tax equity: people get upset when labour is taxed more heavily than capital. Even the slightest concern for tax equity invalidates the common recommendation for small open economies that capital should remain tax-exempt. This holds for exogenous as well as for endogenous government expenditures and irrespective of whether concerns with tax equity only cause emotional discomfort or also impact on work incentives. If concerns with tax equity get more intense, the economy may choose higher taxes on labour and move to the downward sloped part of its Laffer curve. For endogenous government spending, stronger concerns with tax equity may call for a larger size of the public sector.justice, fairness, taxation, policy mix

Smooth Monotonic Networks

Monotonicity constraints are powerful regularizers in statistical modelling. They can support fairness in computer supported decision making and increase plausibility in data-driven scientific models. The seminal min-max (MM) neural network architecture ensures monotonicity, but often gets stuck in undesired local optima during training because of vanishing gradients. We propose a simple modification of the MM network using strictly-increasing smooth non-linearities that alleviates this problem. The resulting smooth min-max (SMM) network module inherits the asymptotic approximation properties from the MM architecture. It can be used within larger deep learning systems trained end-to-end. The SMM module is considerably simpler and less computationally demanding than state-of-the-art neural networks for monotonic modelling. Still, in our experiments, it compared favorably to alternative neural and non-neural approaches in terms of generalization performance

The Relationship Between In-school Suspension and the Academic Achievement of Middle School African American Males

The purpose of this quantitative, correlational study was to investigate the relationship between time assigned to in-school suspension and the math and reading scores on the 2013-2014 Georgia Criterion Referenced Competency Tests (CRCT) for 6th to 8th grade regular education, African American male students. Archival data from school databases were used for this study. Following IRB approval and with permission from each district superintendent, in-school suspension and CRCT score data were collected for 6th to 8th grade regular education, African American male students who had been assigned to 1 or more days of in-school suspension, sampled from 30 middle schools throughout the state of Georgia for a total sample size of 1546 students. Time assigned to in-school suspension, where students guilty of rules violations are temporarily partitioned from their classmates, served as the predictor variable in this research effort. As viewed through the lens of Critical Race Theory and Expectancy Theory, this study centered the statistical analysis on African American middle school male students due to research strongly indicating that students in this subgroup are currently experiencing discipline disproportionalities and growing achievement gaps. Scores on the reading and math CRCT, a collection of standardized tests used to assess grade-level mastery of reading and mathematics learning objectives, served as the criterion variable. Statistical analysis used separate Spearman\u27s rho correlation (ρ) analysis (also referred to as Spearman rank correlation coefficient or Spearman rs) to determine that there was a statistically significant relationship between the time assigned to in-school suspension and scores on the reading CRCT (rs = -.123, p \u3c .0005) and math CRCT (rs = -.142, p \u3c .0005)

The theory and pedagody of semantic inconsistency in critical reasoning

One aspect of critical reasoning is the analysis and appraisal of claims and arguments. A typical problem, when analysing and appraising arguments, is inconsistent statements. Although several inconsistencies may have deleterious effects on rationality and action, not all of them do. As educators, we also have an obligation to teach this evaluation in a way that does justice to our normal reasoning practices and judgements of inconsistency. Thus, there is a need to determine the acceptable inconsistencies from those that are not, and to impart that information to students. We might ask: What is the best concept of inconsistency for critical reasoning and pedagogy? While the answer might appear obvious to some, the history of philosophy shows that there are many concepts of “inconsistency”, the most common of which comes from classical logic and its reliance on opposing truth-values. The current exemplar of this is the standard truth functional account from propositional logic. Initially, this conception is shown to be problematic, practically, conceptually and pedagogically speaking. Especially challenging from the classical perspective are the concepts of ex contradictione quodlibet and ex falso quodlibet. The concepts may poison the well against any notion of inconsistency, which is not something that should be done unreflectively. Ultimately, the classical account of inconsistency is rejected. In its place, a semantic conception of inconsistency is argued for and demonstrated to handle natural reasoning cases effectively. This novel conception utilises the conceptual antonym theory to explain semantic contrast and gradation, even in the absence of non-canonical antonym pairs. The semantic conception of inconsistency also fits with an interrogative argument model that exploits inconsistency to display semantic contrast in reasons and conclusions. A method for determining substantive inconsistencies follows from this argument model in a 4 straightforward manner. The conceptual fit is then incorporated into the pedagogy of critical reasoning, resulting in a natural approach to reasoning which students can apply to practical matters of everyday life, which include inconsistency. Thus, the best conception of inconsistency for critical reasoning and its pedagogy is the semantic, not the classical.Philosophy Practical and Systematic TheologyD. Phi

Feeling in Character: Towards an Ethics of Emotion

This dissertation contends that emotions are subject to ethical assessment, not simply as motives or overt expressions, but in their own right. Emotions, I argue, are subject to assessment because they are aspects of a person\u27s character. Specifically, emotions involve voluntary acts of attention, which are due to habituation. These acts show character by manifesting certain stable, deeply-held desires called \u27concerns.\u27 This view, dubbed \u27Attentional Voluntarism,\u27 is opposed to the prevalent view, dubbed \u27Rationalism,\u27 that emotions are subject to assessment because of their propositional content. Rationalism is unable to account for certain kinds of irrational emotion, where one forms an unwarranted emotion to avoid anxiety and secure pleasure. It exaggerates how mature and adaptive these emotions are. Attentional Voluntarism, by contrast, accounts for the childish and even infantile character behind such emotions, because the relevant habits of attention may simply be the residue from previous developmental stages