Cover Image

A Conceptual Framework for Ethical Evaluation of Machine Learning Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gupta, Neha R., Hullman, Jessica, Subramonyam, Hari
Format:	Preprint
Published:	2024
Subjects:	Computers and Society Artificial Intelligence Machine Learning Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Measuring Agents in Production
by: Pan, Melissa Z., et al.
Published: (2025)

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

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

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

Comparative Evaluation of Embedding Representations for Financial News Sentiment Analysis
by: Roy, Joyjit, et al.
Published: (2025)

Maturity Framework for Enhancing Machine Learning Quality
by: Castelli, Angelantonio, et al.
Published: (2025)

Are ChatGPT and Other Similar Systems the Modern Lernaean Hydras of AI?
by: Ioannidis, Dimitrios, et al.
Published: (2023)

Towards Equitable Agile Research and Development of AI and Robotics
by: Hundt, Andrew, et al.
Published: (2024)

Methodological Framework for Quantifying Semantic Test Coverage in RAG Systems
by: Broestl, Noah, et al.
Published: (2025)

Harnessing IoT and Generative AI for Weather-Adaptive Learning in Climate Resilience Education
by: Khan, Imran S. A., et al.
Published: (2025)

Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems
by: Duan, Zhangqi, et al.
Published: (2025)

Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
by: de Conto, Eduardo, et al.
Published: (2024)

Machine Learning Systems: A Survey from a Data-Oriented Perspective
by: Cabrera, Christian, et al.
Published: (2023)

Fuzzy Intelligent System for Student Software Project Evaluation
by: Ogorodova, Anna, et al.
Published: (2024)

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
by: Abdelkader, Hala, et al.
Published: (2024)

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

Machine Learning Robustness: A Primer
by: Braiek, Houssem Ben, et al.
Published: (2024)

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

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

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

SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
by: Chi, Yizhou, et al.
Published: (2024)

Machine Learning with Requirements: a Manifesto
by: Giunchiglia, Eleonora, et al.
Published: (2023)

Linguacodus: A Synergistic Framework for Transformative Code Generation in Machine Learning Pipelines
by: Trofimova, Ekaterina, et al.
Published: (2024)