activity
20192021
most citedMaking Fair ML Software using Trustworthy Explanation

28 citations · 36 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2021

Bias in Machine Learning Software: Why? How? What to do?

Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies

Increasingly, software is making autonomous decisions in case of criminal sentencing, approving credit cards, hiring employees, and so on. Some of these decisions show bias and adv…

cs.SE202028 cited

Making Fair ML Software using Trustworthy Explanation

Joymallya Chakraborty, Kewen Peng, Tim Menzies

Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this softwar…

cs.SE2020

Fairway: A Way to Build Fair ML Software

Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu +1

Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a b…

cs.SE20198 cited

Predicting Breakdowns in Cloud Services (with SPIKE)

Jianfeng Chen, Joymallya Chakraborty, Philip Clark +3

Maintaining web-services is a mission-critical task where any down-time means loss of revenue and reputation (of being a reliable service provider). In the current competitive web…

cs.SE2019

Software Engineering for Fairness: A Case Study with Hyperparameter Optimization

Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid +1

We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic sin…