Imagen de Portada

Maturity Framework for Enhancing Machine Learning Quality

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Castelli, Angelantonio, Chouliaras, Georgios Christos, Goldenberg, Dmitri
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Computers and Society Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

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

A Conceptual Framework for Ethical Evaluation of Machine Learning Systems
por: Gupta, Neha R., et al.
Publicado: (2024)

Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
por: Taraghi, Mina, et al.
Publicado: (2024)

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

Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
por: Tulili, Tien Rahayu, et al.
Publicado: (2026)

Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
por: McGregor, Sean, et al.
Publicado: (2025)

Carbon Footprint Evaluation of Code Generation through LLM as a Service
por: Vartziotis, Tina, et al.
Publicado: (2025)

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

BacPrep: Lessons from Deploying an LLM-Based Bacalaureat Assessment Platform
por: Dumitran, Adrian-Marius, et al.
Publicado: (2025)

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

Automated Reproducibility Has a Problem Statement Problem
por: Snelleman, Thijs, et al.
Publicado: (2025)

Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide
por: Dzhusupova, Rimma, et al.
Publicado: (2023)

To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
por: McGregor, Sean, et al.
Publicado: (2024)

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

FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
por: Yu, Normen, et al.
Publicado: (2024)

Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing
por: Bourrée, Jade Garcia, et al.
Publicado: (2023)

Mining patterns in syntax trees to automate code reviews of student solutions for programming exercises
por: Van Petegem, Charlotte, et al.
Publicado: (2024)

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
por: Dascalescu, Stefan, et al.
Publicado: (2025)

An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping
por: Xia, Boming, et al.
Publicado: (2024)

The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
por: White, Matt, et al.
Publicado: (2024)

Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Project
por: Yim, Keun Soo
Publicado: (2024)

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
por: Guo, Hanxi, et al.
Publicado: (2025)

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

Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
por: Raheem, Mira, et al.
Publicado: (2025)

Rethinking Technological Readiness in the Era of AI Uncertainty
por: Browne, S. Tucker, et al.
Publicado: (2025)

Measuring Agents in Production
por: Pan, Melissa Z., et al.
Publicado: (2025)

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs
por: Sikand, Samarth, et al.
Publicado: (2025)

NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims
por: Vishwarupe, Varad, et al.
Publicado: (2026)

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

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

Language model developers should report train-test overlap
por: Zhang, Andy K, et al.
Publicado: (2024)

Green AI in Action: Strategic Model Selection for Ensembles in Production
por: Nijkamp, Nienke, et al.
Publicado: (2024)

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
por: Seedat, Nabeel, et al.
Publicado: (2022)

Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
por: Jewitt, James, et al.
Publicado: (2026)

Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
por: Zhang, Dawen, et al.
Publicado: (2023)

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

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

Optimizing Travel Itineraries with AI Algorithms in a Microservices Architecture: Balancing Cost, Time, Preferences, and Sustainability
por: Barua, Biman, et al.
Publicado: (2024)

Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
por: Xuan, Wei, et al.
Publicado: (2025)