| Bias in Machine Learning Software: Why? How? What to do? J Chakraborty, S Majumder, T Menzies Proceedings of the 2021 29th ACM Joint Meeting on European Software …, 2021 | 151 | 2021 |
| Fairway: A Way to Build Fair ML Software J Chakraborty, S Majumder, Z Yu, T Menzies Proceedings of the 2020 28th ACM Joint Meeting on European Software …, 2020 | 109 | 2020 |
| Investigating the effects of gender bias on GitHub N Imtiaz, J Middleton, J Chakraborty, N Robson, G Bai, E Murphy-Hill 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019 | 101* | 2019 |
| Making Fair ML Software using Trustworthy Explanation J Chakraborty, S Majumder, Z Yu, T Menzies The 35th IEEE/ACM International Conference on Automated Software Engineering, 2020 | 42 | 2020 |
| Software engineering for fairness: A case study with hyperparameter optimization J Chakraborty, T Xia, FM Fahid, T Menzies arXiv preprint arXiv:1905.05786, 2019 | 40 | 2019 |
| Algorithms for generating all possible spanning trees of a simple undirected connected graph: an extensive review M Chakraborty, S Chowdhury, J Chakraborty, R Mehera, RK Pal Complex & Intelligent Systems 5, 265-281, 2019 | 34 | 2019 |
| FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes K Peng, J Chakraborty, T Menzies IEEE Transactions on Software Engineering 49 (4), 2426–2439, 2022 | 20* | 2022 |
| Fair enough: Searching for sufficient measures of fairness S Majumder, J Chakraborty, GR Bai, KT Stolee, T Menzies ACM Transactions on Software Engineering and Methodology, 2023 | 17 | 2023 |
| Fair-SSL: Building fair ML Software with less data J Chakraborty, H Tu, S Majumder International Workshop on Equitable Data and Technology (FairWare ’22 ), 2022 | 15* | 2022 |
| Why software projects need heroes (lessons learned from 1100+ projects) S Majumder, J Chakraborty, A Agrawal, T Menzies arXiv preprint arXiv:1904.09954, 2019 | 13 | 2019 |
| Fair balance: Mitigating machine learning bias against multiple protected attributes with data balancing Z Yu arXiv preprint arXiv:2107.08310 17, 2021 | 10 | 2021 |
| Predicting breakdowns in cloud services (with SPIKE) J Chen, J Chakraborty, P Clark, K Haverlock, S Cherian, T Menzies Proceedings of the 2019 27th ACM joint meeting on european software …, 2019 | 8 | 2019 |
| FairBalance: Improving machine learning fairness on multiple sensitive attributes with data balancing Z Yu, J Chakraborty, T Menzies arXiv preprint Arxiv:2107.08310, 2021 | 7 | 2021 |
| Fairer machine learning software on multiple sensitive attributes with data preprocessing Z Yu, J Chakraborty, T Menzies arXiv preprint arXiv:2107.08310, 2021 | 3 | 2021 |
| Communication and Code Dependency Effects on Software Code Quality: An Empirical Analysis of Herbsleb Hypothesis S Majumder, J Chakraborty, A Agrawal, T Menzies arXiv preprint arXiv:1904.09954, 2019 | 1 | 2019 |
| When less is more: on the value of “co-training” for semi-supervised software defect predictors S Majumder, J Chakraborty, T Menzies Empirical Software Engineering 29 (2), 1-33, 2024 | | 2024 |
| Deciphering Ml Software Fairness J Chakraborty North Carolina State University, 2022 | | 2022 |
| FairBalance: How to Achieve Equalized Odds With Data Pre-processing Z Yu, J Chakraborty, T Menzies arXiv preprint arXiv:2107.08310, 2021 | | 2021 |
| FairWare 2022 S Alimadadi, C Bird, M Canellas, J Chakraborty, D Ford, L Hampton, ... | | |
Tim MenziesNorth Carolina State University, Computer Science, IEEE Fellow, ASE FellowVerified email at ncsu.edu
