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Thu 1 May 2025 14:00 - 14:15 at 215 - SE for AI 3 Chair(s): Lina Marsso

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination—examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age—is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance.

Facing the above issues, we first identify that enhancing the global phase consistently improves overall testing effectiveness compared to enhancing the local phase. This motivates us to propose Genetic-Random Fairness Testing (GRFT), an effective and efficient method. In the global phase, we use a genetic algorithm to guide the search for more global discriminatory instances. In the local phase, we apply a light random search to explore the neighbors of these instances, avoiding time-consuming computations. Additionally, based on the fitness score, we also propose a straightforward yet effective repair approach. For a thorough evaluation, we conduct extensive experiments involving 6 testing methods, 5 datasets, 261 models (including 5 naively trained, 64 repaired, and 192 quantized for on-device deployment), and sixteen combinations of sensitive attributes, showing the superior performance of GRFT and our repair method.

Thu 1 May

Displayed time zone: Eastern Time (US & Canada) change

14:00 - 15:30
SE for AI 3Research Track / SE in Society (SEIS) / Journal-first Papers at 215
Chair(s): Lina Marsso École Polytechnique de Montréal
14:00
15m
Talk
Dissecting Global Search: A Simple yet Effective Method to Boost Individual Discrimination Testing and RepairSE for AI
Research Track
Lili Quan Tianjin University, Li Tianlin NTU, Xiaofei Xie Singapore Management University, Zhenpeng Chen Nanyang Technological University, Sen Chen Nankai University, Lingxiao Jiang Singapore Management University, Xiaohong Li Tianjin University
Pre-print
14:15
15m
Talk
FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per ViolationSE for AI
Research Track
Yang Sun Singapore Management University, Chris Poskitt Singapore Management University, Kun Wang Zhejiang University, Jun Sun Singapore Management University
Link to publication DOI Pre-print File Attached
14:30
15m
Talk
MARQ: Engineering Mission-Critical AI-based Software with Automated Result Quality AdaptationSE for AIArtifact-FunctionalArtifact-AvailableArtifact-Reusable
Research Track
Uwe Gropengießer Technical University of Darmstadt, Elias Dietz Technical University of Darmstadt, Florian Brandherm Technical University of Darmstadt, Achref Doula Technical University of Darmstadt, Osama Abboud Munich Research Center, Huawei, Xun Xiao Munich Research Center, Huawei, Max Mühlhäuser Technical University of Darmstadt
14:45
15m
Talk
An Empirical Study of Challenges in Machine Learning Asset ManagementSE for AI
Journal-first Papers
Zhimin Zhao Queen's University, Yihao Chen Queen's University, Abdul Ali Bangash Software Analysis and Intelligence Lab (SAIL), Queen's University, Canada, Bram Adams Queen's University, Ahmed E. Hassan Queen’s University
15:00
15m
Talk
A Reference Model for Empirically Comparing LLMs with HumansSE for AI
SE in Society (SEIS)
Kurt Schneider Leibniz Universität Hannover, Software Engineering Group, Farnaz Fotrousi Chalmers University of Technology and University of Gothenburg, Rebekka Wohlrab Chalmers University of Technology
15:15
7m
Talk
Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-PracticeSE for AI
Journal-first Papers
Bentley Oakes Polytechnique Montréal, Michalis Famelis Université de Montréal, Houari Sahraoui DIRO, Université de Montréal
DOI Pre-print File Attached
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