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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Thu 18 May 2023 13:45 - 14:00 at Meeting Room 104 - AI bias and fairness Chair(s): Amel Bennaceur

Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers design fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.

Thu 18 May

Displayed time zone: Hobart change

13:45 - 15:15
AI bias and fairnessDEMO - Demonstrations / Technical Track / Journal-First Papers at Meeting Room 104
Chair(s): Amel Bennaceur The Open University, UK
13:45
15m
Talk
Towards Understanding Fairness and its Composition in Ensemble Machine Learning
Technical Track
Usman Gohar Dept. of Computer Science, Iowa State University, Sumon Biswas Carnegie Mellon University, Hridesh Rajan Iowa State University
Pre-print
14:00
15m
Talk
Fairify: Fairness Verification of Neural Networks
Technical Track
Sumon Biswas Carnegie Mellon University, Hridesh Rajan Iowa State University
Pre-print
14:15
15m
Talk
Leveraging Feature Bias for Scalable Misprediction Explanation of Machine Learning Models
Technical Track
Jiri Gesi University of California, Irvine, Xinyun Shen University of California, Irvine, Yunfan Geng University of California, Irvine, Qihong Chen University of California, Irvine, Iftekhar Ahmed University of California at Irvine
14:30
15m
Talk
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks
Technical Track
Verya Monjezi University of Texas at El Paso, Ashutosh Trivedi University of Colorado Boulder, Gang Tan Pennsylvania State University, Saeid Tizpaz-Niari University of Texas at El Paso
Pre-print
14:45
7m
Talk
Seldonian Toolkit: Building Software with Safe and Fair Machine Learning
DEMO - Demonstrations
Austin Hoag Berkeley Existential Risk Initiative, James E. Kostas University of Massachusetts, Bruno Castro da Silva University of Massachusetts, Philip S. Thomas University of Massachusetts, Yuriy Brun University of Massachusetts
Pre-print Media Attached
14:52
7m
Talk
What Would You do? An Ethical AI Quiz
DEMO - Demonstrations
Wei Teo Monash University, Ze Teoh Monash University, Dayang Abang Arabi Monash University, Morad Aboushadi Monash University, Khairenn Lai Monash University, Zhe Ng Monash University, Aastha Pant Monash Univeristy, Rashina Hoda Monash University, Kla Tantithamthavorn Monash University, Burak Turhan University of Oulu
Pre-print Media Attached
15:00
7m
Talk
Search-Based Fairness Testing for Regression-Based Machine Learning Systems
Journal-First Papers
Anjana Perera Oracle Labs, Australia, Aldeida Aleti Monash University, Kla Tantithamthavorn Monash University, Jirayus Jiarpakdee Monash University, Australia, Burak Turhan University of Oulu, Lisa Kuhn Monash University, Katie Walker Monash University
Link to publication DOI
15:07
7m
Talk
FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes
Journal-First Papers
Kewen Peng North Carolina State University, Tim Menzies North Carolina State University, Joymallya Chakraborty North Carolina State University
Link to publication Pre-print