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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Thu 18 May 2023 14:45 - 14:52 at Meeting Room 104 - AI bias and fairness Chair(s): Amel Bennaceur

We present the Seldonian Toolkit, which enables software engineers to integrate provably safe and fair machine learning algorithms into their systems. Software systems that use data and machine learning are routinely deployed in a wide range of settings, ranging from medical applications, autonomous vehicles, the criminal justice system, and hiring processes. These systems, however, can produce unsafe and unfair behavior, such as suggesting potentially fatal medical treatments, making racist or sexist predictions, or facilitating radicalization and polarization. To reduce these undesirable behaviors, software engineers need the ability to easily integrate their machine-learning-based systems with domain-specific safety and fairness requirements defined by domain experts, such as doctors and hiring managers. The Seldonian Toolkit provides special machine learning algorithms that enable software engineers to incorporate such expert-defined requirements of safety and fairness into their systems, while provably guaranteeing those requirements will be satisfied. A video demonstrating the Seldonian Toolkit is available at https://youtu.be/wHR-hDm9jX4/.

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 (Gary) 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