Seldonian Toolkit: Building Software with Safe and Fair Machine Learning
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 MayDisplayed 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 15mTalk | 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 15mTalk | Fairify: Fairness Verification of Neural Networks Technical Track Pre-print | ||
14:15 15mTalk | 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 15mTalk | 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 7mTalk | 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 7mTalk | 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 7mTalk | 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 7mTalk | 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 | ||