265 citations · 354 across the 7 of their papers we have counts for
4 papers · 1 filter
Bias Mitigation Post-processing for Individual and Group Fairness
Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide +3
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both in…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…
Automated Test Generation to Detect Individual Discrimination in AI Models
Aniya Agarwal, Pranay Lohia, Seema Nagar +2
Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisio…
Efficiently Processing Workflow Provenance Queries on SPARK
Rajmohan C, Pranay Lohia, Himanshu Gupta +3
In this paper, we investigate how we can leverage Spark platform for efficiently processing provenance queries on large volumes of workflow provenance data. We focus on processing…