Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.Comment: This paper has been accepted for publication at ACM KDD 201

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

Dietary fat and not calcium supplementation or dairy product consumption is associated with changes in anthropometrics during a randomized, placebo-controlled energy-restriction trial

Insufficient calcium intake has been proposed to cause unbalanced energy partitioning leading to obesity. However, weight loss interventions including dietary calcium or dairy product consumption have not reported changes in lipid metabolism measured by the plasma lipidome

Effects of dairy intake on weight maintenance

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Dairy Consumption and the Incidence of Hyperglycemia and the Metabolic Syndrome: Results from a French prospective study, Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR)

International audienceOBJECTIVE: In the French Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) cohort, cross-sectional analyses have shown that a higher consumption of dairy products and calcium are associated with a lower prevalence of the metabolic syndrome (MetS). We assess the influence of dairy products on 9-year incident MetS and on impaired fasting glycemia and/or type 2 diabetes (IFG/T2D). RESEARCH DESIGN AND METHODS: Men and women who completed a food frequency questionnaire at baseline and after 3 years were studied (n = 3,435). Logistic regression models were used to study associations between the average year 0 and year 3 consumption of milk and dairy products, cheese, dietary calcium density, and incident MetS and IFG/T2D after adjusting for 1) sex, age, alcohol, smoking, physical activity, fat intake and 2) additionally for BMI. Associations between dairy products and continuous variables were studied by repeated-measures ANCOVA, using the same covariates. RESULTS: Dairy products other than cheese, and dietary calcium density, were inversely associated with incident MetS and IFG/T2D; cheese was negatively associated with incident MetS. All three parameters were associated with lower diastolic blood pressure, and with a lower BMI gain. Higher cheese intake and calcium density were associated with a lower increase in waist circumference and lower triglyceride levels. Calcium density was also associated with a lower systolic blood pressure and a lower 9-year increase in plasma triglyceride levels. CONCLUSIONS: A higher consumption of dairy products and calcium was associated with a lower 9-year incidence of MetS and IFG/T2D in a large cohort drawn from the general population

Evo-devo of human adolescence: beyond disease models of early puberty

Despite substantial heritability in pubertal development, much variation remains to be explained, leaving room for the influence of environmental factors to adjust its phenotypic trajectory in the service of fitness goals. Utilizing evolutionary development biology (evo-devo), we examine adolescence as an evolutionary life-history stage in its developmental context. We show that the transition from the preceding stage of juvenility entails adaptive plasticity in response to energy resources, other environmental cues, social needs of adolescence and maturation toward youth and adulthood. Using the evolutionary theory of socialization, we show that familial psychosocial stress fosters a fast life history and reproductive strategy rather than early maturation being just a risk factor for aggression and delinquency. Here we explore implications of an evolutionary-developmental-endocrinological-anthropological framework for theory building, while illuminating new directions for research

Dynamic Mechanisms of Cell Rigidity Sensing: Insights from a Computational Model of Actomyosin Networks

Cells modulate themselves in response to the surrounding environment like substrate elasticity, exhibiting structural reorganization driven by the contractility of cytoskeleton. The cytoskeleton is the scaffolding structure of eukaryotic cells, playing a central role in many mechanical and biological functions. It is composed of a network of actins, actin cross-linking proteins (ACPs), and molecular motors. The motors generate contractile forces by sliding couples of actin filaments in a polar fashion, and the contractile response of the cytoskeleton network is known to be modulated also by external stimuli, such as substrate stiffness. This implies an important role of actomyosin contractility in the cell mechano-sensing. However, how cells sense matrix stiffness via the contractility remains an open question. Here, we present a 3-D Brownian dynamics computational model of a cross-linked actin network including the dynamics of molecular motors and ACPs. The mechano-sensing properties of this active network are investigated by evaluating contraction and stress in response to different substrate stiffness. Results demonstrate two mechanisms that act to limit internal stress: (i) In stiff substrates, motors walk until they exert their maximum force, leading to a plateau stress that is independent of substrate stiffness, whereas (ii) in soft substrates, motors walk until they become blocked by other motors or ACPs, leading to submaximal stress levels. Therefore, this study provides new insights into the role of molecular motors in the contraction and rigidity sensing of cells