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Thu 12 Nov 2020 01:00 - 01:02 at Virtual room 1 - Fairness

Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairness of these models so that no discrimination is made based on protected attribute (e.g., race, sex, age) while decision making. Algorithms have been developed to measure unfairness and mitigate them to a certain extent. In this paper, we have focused on the empirical evaluation of fairness and mitigations on real-world machine learning models. We have created a benchmark of 40 top-rated models from Kaggle used for 5 different tasks, and then using a comprehensive set of fairness metrics, evaluated their fairness. Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance. We have found that some model optimization techniques result in inducing unfairness in the models. On the other hand, although there are some fairness control mechanisms in machine learning libraries, they are not documented. The mitigation algorithm also exhibit common patterns such as mitigation in the post-processing is often costly (in terms of performance) and mitigation in the pre-processing stage is preferred in most cases. We have also presented different trade-off choices of fairness mitigation decisions. Our study suggests future research directions to reduce the gap between theoretical fairness aware algorithms and the software engineering methods to leverage them in practice.

Thu 12 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

01:00 - 01:02
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Research Papers
Sumon BiswasIowa State University, USA, Hridesh RajanIowa State University, USA
Link to publication DOI Pre-print Media Attached
01:03 - 01:04
Fairway: A Way to Build Fair ML Software
Research Papers
Joymallya ChakrabortyNorth Carolina State University, USA, Suvodeep MajumderNorth Carolina State University, USA, Zhe YuNorth Carolina State University, USA, Tim MenziesNorth Carolina State University, USA
01:05 - 01:06
Repairing Confusion and Bias Errors for DNN-Based Image Classifiers
Student Research Competition
Yuchi TianColumbia University
01:07 - 01:08
Towards Automated Verification of Smart Contract Fairness
Research Papers
Ye LiuNanyang Technological University, Singapore, Yi LiNanyang Technological University, Singapore, Shang-Wei LinNanyang Technological University, Singapore, Rong ZhaoNanyang Technological University, Singapore
DOI Pre-print
01:09 - 01:30
Conversations on Fairness
Paper Presentations
Joymallya ChakrabortyNorth Carolina State University, USA, Sumon BiswasIowa State University, USA, Ye LiuNanyang Technological University, Singapore, Yi LiNanyang Technological University, Singapore, Yuchi TianColumbia University, M: Christian BirdMicrosoft Research