Search Results for author: Abhisek Dash

Found 6 papers, 1 papers with code

Two-Face: Adversarial Audit of Commercial Face Recognition Systems

no code implementations17 Nov 2021 Siddharth D Jaiswal, Karthikeya Duggirala, Abhisek Dash, Animesh Mukherjee

Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance.

Face Detection Face Recognition

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces

no code implementations30 Jan 2021 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi

Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.

Fairness

A Network-centric Framework for Auditing Recommendation Systems

no code implementations7 Feb 2019 Abhisek Dash, Animesh Mukherjee, Saptarshi Ghosh

In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users.

Recommendation Systems

Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries

1 code implementation22 Oct 2018 Abhisek Dash, Anurag Shandilya, Arindam Biswas, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty

Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.

Extractive Summarization Fairness +1

Image Clustering without Ground Truth

no code implementations25 Oct 2016 Abhisek Dash, Sujoy Chatterjee, Tripti Prasad, Malay Bhattacharyya

Given multiple such clustering solutions, it is a challenging task to obtain an ensemble of these solutions.

Clustering Ensemble Image Clustering

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