Achieving Causal Fairness in Machine Learning

Fairness is a social norm and a legal requirement in today\u27s society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders. The goal of this dissertation is to study fairness in machine learning, define appropriate fairness notions, and develop novel discrimination mitigation approaches from a causal perspective. Based on Pearl\u27s structural causal model, we propose to formulate discrimination as causal effects of the sensitive attribute on the decision. We consider different types of causal effects to cope with different situations, including the path-specific effect for direct/indirect discrimination, the counterfactual effect for group/individual discrimination, and the path-specific counterfactual effect for general cases. In the attempt to measure discrimination, the unidentifiable situations pose an inevitable barrier to the accurate causal inference. To address this challenge, we propose novel bounding methods to accurately estimate the strength of unidentifiable fairness notions, including path-specific fairness, counterfactual fairness, and path-specific counterfactual fairness. Based on the estimation of fairness, we develop novel and efficient algorithms for learning fair classification models. Besides classification, we also investigate the discrimination issues in other machine learning scenarios, such as ranked data analysis

Nonparametric Bayes modeling for case control studies with many predictors: Marginal Genetic Effects in Family Studies

It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects

The crime-commission process of sexual offences on London trains (SOLT): offending in plain sight, not just at night

This thesis explores sexual offences that are committed on London trains, which has seen an increase over the past 3 years (BTP, 2018). This research aims to produce a detailed and comprehensive descriptive account of sexual offences on London trains (SOLT), utilising psychological and criminological theoretical constructs, for example, crime script theory and narrative criminology, to understand the commission of such offences. The initial study conducted with proactive officers from the British Transport Police (BTP) (N = 14), provided preliminary findings in relation to the offence specific characteristics and behaviours of SOLT that related to situational and environmental factors. A further study was conducted with a sample of convicted offenders to understand how they make sense of themselves and their actions as perpetrators. Key factors were their post hoc rationalisations for their behaviours and insights regarding how these factors influence their decision-making. The final study of archival police records explored the importance of spatial and temporal factors relating to the behaviours of individuals committing sexual offences in the train environment. Offence characteristics were interrogated to explore relationships between variables, to distinguish between the different sexual offending behaviours for the different offence types. This thesis adds to existing knowledge of how psychological theories can be employed to inform the policing approach and practice to SOLT, as well as adding to the wider literature on sex offending. This research identifies how the complexities of the political, organisational and individual factors impact on the outcome of policing strategies to address SOLT. To complement this new theoretical model, the findings presented in this thesis also provide useful direction for strategic thinking and operational practice within BTP

Modelling fruit and vegetable consumption : a comparative study of two cities with high and low consumption

The importance of the consumption of a diet rich in fruit and vegetables, for overall health, has been highlighted by the UK government in recent years. Consumption of fruit and vegetables is considerably lower in Scotland than in other parts of the UK. Behavioural change is most likely to come about through a comprehensive understanding of the range of factors influencing fruit and vegetable consumption, and the nature of the interaction of these factors. With variations in fruit and vegetable consumption by place and social class, this study focused on an area of low fruit and vegetable consumption in Scotland, and compared this to an area of high fruit and vegetable consumption in England, with socio-economic profile matched. The determinants of consumption, and their interrelationship, were investigated using qualitative information from focus group discussions, and quantitative data from a structured questionnaire. Multivariate models of fruit and vegetables consumption were developed, using log linear analysis, logistic regression and discriminant analysis. The models developed identified significant differences between fruit and vegetable consumption behaviour. Fruit consumption was mainly influenced by sociodemographic variables,in particulars mokers tatus. The impact of place and social class was substantial, when these variables were considered in interaction with the other socio-demographic variables. In contrast, vegetable consumption was influenced by motivational and attitudinal factors. Of these, the extent to which vegetables satisfied `convenience' expectations, and `hedonic motivations' were the most important influences, critical to vegetable consumption. The findings also suggest that the development of a generic model of food choice may not be an achievable goal, since the models of these two (similar) foodstuffs are so different. Strategies to promote fruit and vegetable consumption, must address the different characteristics, and priorities, of low fruit and vegetable consumers

Asymptotic Treatment for Multinomial Models and Applications

In this study, we derived a highly convenient chi-square test to replace the Ftests for an Analysis of Variance (ANOVA)-like inference for multinomial models. To obtain these results we started by studying limit distributions in models with compact parameter space. Based on these results we obtained confidence ellipsoids and simultaneous confidence intervals for models with limit normal distributions. Next, we studied the covariance matrices of the limit normal distributions for the multinomial models. This was a transition between the previous general results and those on the inference for multinomial models in which we considered the chi-square tests, confidence regions and non-linear statistics, namely log-linear model, with two numerical applications to those models. Our approach overcame the hierarchical restrictions assumed to analyze multidimensional contingency table. Also, by application for our research, we developed Discriminant Analysis (DA) for samples extracted from a random variable which can only take a finite number of values so a sample constituted by such values will have a multinomial distribution M( jn;p), with n being the sample size and p the probabilities of having the different values. Using DA in connection with Statistical Decision Theory (SDT), since we aim at minimizing the average cost associated with decisions, we derived a rule that minimizes the assignment costs. Our results were applied to data on Human Immunodeficiency Virus (HIV) treatments, and classified patients into Treated or Naive populations at an accuracy of 71.06%, despite the peculiar nature of our dataset. We also were able to show that our discrimination procedure was consistent

The Advanced Applications For Optical Coherence Tomography In Skin Imaging

Optical coherence tomography (OCT), based on the principle of interferometry, is a fast and non-invasive imaging modality, which has been approved by FDA for dermatologic applications. OCT has high spatial resolution up to micrometer scale compared to traditional ultrasound imaging. In addition, OCT can provide real-time cross-sectional images with 1 to 2 mm penetration depth, which makes it an ideal imaging technique to assess the skin micro-morphology and pathology without any tissue removal. Many studies have investigated the possibilities of using OCT to evaluate dermatologic conditions, such as skin cancer, dermatitis, psoriasis, and skin damages. Hence, OCT has tremendous potential to provide skin histological and pathological information and assist differential diagnosis of various skin diseases. In this study, we used a swept-source OCT with 1305 nm central wavelength to explore its advanced applications in dermatology. This dissertation consists of four major research projects. First, we explored the feasibility of OCT imaging for assisting real-time visualization in skin biopsy. We showed that OCT could be used to guide and track a needle insertion in mouse skin in real-time. The structure of skin and the movement of needle can be clearly seen on the OCT images without any time delay during the procedures. Next, we tested the concept of performing the punch biopsy using OCT hand-held probe attached to a piercing tip in a phantom. We proved that using the OCT is a reliable technique to delineate the margin of lesion in phantom. And it is possible to perform the punch biopsy with the OCT probe. Second, we tested the performance of contrast-enhanced OCT in melanoma detection in an in vitro study. Melanoma is the most lethal type of skin cancer. Early detection could significantly improve the long-term survival rate of patients. In this initial study, a contrast agent (Gal3-USGNPs) is developed by conjugating melanoma biomarker (Gal3) to ultra-small gold nanoparticles (USGNPs). We showed that the contrast agent can differentiate B16 melanoma cells from normal skin keratinocytes in vitro. To avoid systemic administration of USGNPs, the third project continues to explore the enhanced topical delivery of USGNPs. In this study, we used OCT to monitor the topical delivery of nanoparticles on pig skin over time. And the diffusion and penetration of USGNPs in skin can be improved by applying chemical and physical enhancers such as DMSO and sonophoresis. Finally, in addition to image the cross-sectional structure of skin, we also aim to extract quantitative information from OCT images. The skin optical properties such as attenuation coefficient can be measured from OCT images. We measured and compared the skin attenuation coefficient in the skin of forehead and lateral hip, the skin of three different age groups, and the skin of three different Fitzpatrick types. The statistical analysis showed that epidermis has much higher attenuation coefficient than dermis. And the skin type V & VI have a relatively lower attenuation coefficient than the other skin types. These studies could aid the detection of skin cancer using imaging techniques and provide some new insights into the future applications of OCT in dermatology

The Effect of Gender and Narcotic or Stimulant Abuse on Drug-Related Locus of Control

Substance use disorders cause significant neurological damage, cognitive impairment, and maladaptive behaviors that negatively affect a person\u27s quality of life. The purpose of this study was to explore the effect gender and primary drugs have on locus of control. Generalized expectancy theory helped to explain the behavior of patients diagnosed with substance use disorders and their inability to control ongoing drug use. The research question focused on to what extent drug-related locus of control scores differ by primary drug (narcotic vs. stimulant), gender (male vs. female), and their interaction. Data measuring locus of control from 553 participants provided a subset of 410 participants who identified narcotics or stimulants as their primary drug. A 2x2 full factorial ANOVA was conducted. The results of this study indicated there is a significant interaction between primary drug use and gender. The results could have positive social change implications for the addiction field because of the value of understanding the interdependency of internal-external thought processes related to drug use, the ability to change stigma associated with addiction and gender, and the value of understanding the need for individualized treatment as locus of control shifts from external to internal. It is recommended that the drug-related locus of control instrument become part of treatment protocol along with evidence-based interventions