We aim to design a fairness-aware allocation approach to maximize the geographical diversity and ... more We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and create a synergistic intervention strategy that gives vulnerable populations higher priority in medical resource distribution. This prevention-centered strategy seeks a balance between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation. Preprint. Under review.
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing the unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can generalize to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.
This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objec... more This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objective black-box optimization (BBO). While BBO literature includes various promising sampling techniques, there is still a lack of comprehensive investigations of the existing research across the vast scope of BBO problems. We first classify BBO problems into two categories: engineering design and algorithm design optimization and discuss their challenges. We then critically discuss and analyze the adaptive modelbased sampling techniques focusing on key acquisition functions. We elaborate on the shortcomings of the variance-based sampling techniques for engineering design problems. Moreover, we provide in-depth insights on the impact of the discretization schemes on the performance of acquisition functions. We emphasize the importance of dynamic discretization for distance-based exploration and introduce EEPA + , an improved variant of a previously proposed Pareto-based sampling technique. Our empirical analyses reveal the effectiveness of variance-based techniques for algorithm design and distance-based methods for engineering design optimization problems.
Nowadays, colleges and universities use predictive analytics in a variety of ways to increase stu... more Nowadays, colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potentials for predictive analytics, there exist two major barriers to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. Missing Values are the frequent latent causes behind many data analysis challenges. While many education-related studies addressed the challenges of missing data, l...
We aim to design a fairness-aware allocation approach to maximize the geographical diversity and ... more We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and seek a synergistic intervention strategy that gives vulnerable populations with higher priority when distributing medical resources. This prevention-centered strategy is a trade-off between geographical coverage and social group fairness. The ...
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been defined, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible) manually explore the combinatorial space of different measures before they can decide which combination is preferred based on the context, the use case, and regulations. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that automatically discovers the correlations and trade-offs between different pairs of measures for a given context. Our framework dramatically reduces the exploration space by finding a small subset of measures that represent othe...
Nowadays, colleges and universities use predictive analytics in a variety of ways to increase stu... more Nowadays, colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potentials for predictive analytics, there exist two major barriers to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. Missing Values are the frequent latent causes behind many data analysis challenges. While many education-related studies addressed the challenges of missing data, l...
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been defined, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible) manually explore the combinatorial space of different measures before they can decide which combination is preferred based on the context, the use case, and regulations. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that automatically discovers the correlations and trade-offs between different pairs of measures for a given context. Our framework dramatically reduces the exploration space by finding a small subset of measures that represent othe...
We aim to design a fairness-aware allocation approach to maximize the geographical diversity and ... more We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and create a synergistic intervention strategy that gives vulnerable populations higher priority in medical resource distribution. This prevention-centered strategy seeks a balance between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation. Preprint. Under review.
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing the unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can generalize to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.
This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objec... more This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objective black-box optimization (BBO). While BBO literature includes various promising sampling techniques, there is still a lack of comprehensive investigations of the existing research across the vast scope of BBO problems. We first classify BBO problems into two categories: engineering design and algorithm design optimization and discuss their challenges. We then critically discuss and analyze the adaptive modelbased sampling techniques focusing on key acquisition functions. We elaborate on the shortcomings of the variance-based sampling techniques for engineering design problems. Moreover, we provide in-depth insights on the impact of the discretization schemes on the performance of acquisition functions. We emphasize the importance of dynamic discretization for distance-based exploration and introduce EEPA + , an improved variant of a previously proposed Pareto-based sampling technique. Our empirical analyses reveal the effectiveness of variance-based techniques for algorithm design and distance-based methods for engineering design optimization problems.
Nowadays, colleges and universities use predictive analytics in a variety of ways to increase stu... more Nowadays, colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potentials for predictive analytics, there exist two major barriers to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. Missing Values are the frequent latent causes behind many data analysis challenges. While many education-related studies addressed the challenges of missing data, l...
We aim to design a fairness-aware allocation approach to maximize the geographical diversity and ... more We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and seek a synergistic intervention strategy that gives vulnerable populations with higher priority when distributing medical resources. This prevention-centered strategy is a trade-off between geographical coverage and social group fairness. The ...
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been defined, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible) manually explore the combinatorial space of different measures before they can decide which combination is preferred based on the context, the use case, and regulations. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that automatically discovers the correlations and trade-offs between different pairs of measures for a given context. Our framework dramatically reduces the exploration space by finding a small subset of measures that represent othe...
Nowadays, colleges and universities use predictive analytics in a variety of ways to increase stu... more Nowadays, colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potentials for predictive analytics, there exist two major barriers to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. Missing Values are the frequent latent causes behind many data analysis challenges. While many education-related studies addressed the challenges of missing data, l...
It is of critical importance to be aware of the historical discrimination embedded in the data an... more It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been defined, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible) manually explore the combinatorial space of different measures before they can decide which combination is preferred based on the context, the use case, and regulations. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that automatically discovers the correlations and trade-offs between different pairs of measures for a given context. Our framework dramatically reduces the exploration space by finding a small subset of measures that represent othe...
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