FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yu, Normen, Carreon, Luciana, Tan, Gang, Tizpaz-Niari, Saeid
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Predicting Fairness of ML Software Configurations
by: Herrera, Salvador Robles, et al.
Published: (2024)

NeuFair: Neural Network Fairness Repair with Dropout
by: Dasu, Vishnu Asutosh, et al.
Published: (2024)

Fairness Testing through Extreme Value Theory
by: Monjezi, Verya, et al.
Published: (2025)

Metamorphic Debugging for Accountable Software
by: Tizpaz-Niari, Saeid, et al.
Published: (2024)

Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory
by: Tizpaz-Niari, Saeid, et al.
Published: (2024)

Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations
by: Akash, Ranit Debnath, et al.
Published: (2025)

On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation
by: Monjezi, Verya, et al.
Published: (2026)

FairSense: Long-Term Fairness Analysis of ML-Enabled Systems
by: She, Yining, et al.
Published: (2025)

An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software
by: Gogani-Khiabani, Sina, et al.
Published: (2025)

Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching
by: Peng, Kewen, et al.
Published: (2025)

Data vs. Model Machine Learning Fairness Testing: An Empirical Study
by: Shome, Arumoy, et al.
Published: (2024)

Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
by: Zhou, Kacy, et al.
Published: (2024)

On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
by: Srinivas, Dananjay, et al.
Published: (2023)

Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems
by: Du, Chengwen, et al.
Published: (2024)

Assessing the Use of AutoML for Data-Driven Software Engineering
by: Calefato, Fabio, et al.
Published: (2023)

Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
by: Chen, Zhenpeng, et al.
Published: (2024)

Applications and Challenges of Fairness APIs in Machine Learning Software
by: Das, Ajoy, et al.
Published: (2025)

Fairness Improvement with Multiple Protected Attributes: How Far Are We?
by: Chen, Zhenpeng, et al.
Published: (2023)

FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
by: Kim, Brian Hyeongseok, et al.
Published: (2024)

Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
by: Vijayan, Naveen Edapurath
Published: (2025)

Risk Estimation in Differential Fuzzing via Extreme Value Theory
by: Baez, Rafael, et al.
Published: (2025)

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
by: Openja, Moses, et al.
Published: (2025)

MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search
by: Wang, Zhaohui, et al.
Published: (2024)

Concolic Testing on Individual Fairness of Neural Network Models
by: Huang, Ming-I, et al.
Published: (2025)

A Systematic Mapping on Software Fairness: Focus, Trends and Industrial Context
by: Nepomuceno, Kessia, et al.
Published: (2025)

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?
by: Voria, Gianmario, et al.
Published: (2024)

Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
by: Cheng, Chih-Hong, et al.
Published: (2024)

Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
by: Pant, Aastha, et al.
Published: (2024)

Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations
by: Gohar, Usman, et al.
Published: (2024)

Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph
by: Nainani, Jatin, et al.
Published: (2025)

A Catalog of Fairness-Aware Practices in Machine Learning Engineering
by: Voria, Gianmario, et al.
Published: (2024)

iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
by: Le, Dat, et al.
Published: (2026)

Expert-Driven Monitoring of Operational ML Models
by: Leest, Joran, et al.
Published: (2024)

Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
by: Sah, Chandan Kumar, et al.
Published: (2026)

How Fair is Software Fairness Testing?
by: Barcomb, Ann, et al.
Published: (2026)

Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging
by: Feng, Siyuan, et al.
Published: (2024)

Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
by: Xiao, Yisong, et al.
Published: (2023)

Legal Aspects for Software Developers Interested in Generative AI Applications
by: Herbold, Steffen, et al.
Published: (2024)

An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack
by: Arabat, Ali, et al.
Published: (2025)

A Feature-Driven Framework for Software Fault Prediction
by: Ghazi, Ahmad Nauman, et al.
Published: (2026)