Divya Venugopalan | National Institute of Technology Karnataka,Surathkal (original) (raw)
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Papers by Divya Venugopalan
Building fair recommender systems is a challenging and extremely important area of study due to i... more Building fair recommender systems is a challenging and extremely important area of study due to its immense impact on society. We focus on two commonly accepted notions of fairness for machine learning models powering such recommender systems, namely equality of opportunity and equalized odds. These measures of fairness make sure that equally "qualified" (or "unqualified") candidates are treated equally regardless of their protected attribute status (such as gender or race). In this paper, we propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias, which commonly plagues data generated from recommendation systems. Our algorithms are model agnostic in the sense that they depend only on the final scores provided by a model, making them easily applicable to virtually all web-scale recommender systems. We conduct extensive simulations as well as real-world experiments to show the efficacy of our a...
ArXiv, 2021
Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Faceb... more Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based o...
Building fair recommender systems is a challenging and extremely important area of study due to i... more Building fair recommender systems is a challenging and extremely important area of study due to its immense impact on society. We focus on two commonly accepted notions of fairness for machine learning models powering such recommender systems, namely equality of opportunity and equalized odds. These measures of fairness make sure that equally "qualified" (or "unqualified") candidates are treated equally regardless of their protected attribute status (such as gender or race). In this paper, we propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias, which commonly plagues data generated from recommendation systems. Our algorithms are model agnostic in the sense that they depend only on the final scores provided by a model, making them easily applicable to virtually all web-scale recommender systems. We conduct extensive simulations as well as real-world experiments to show the efficacy of our a...
ArXiv, 2021
Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Faceb... more Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based o...