Cover Image

From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Kou, Tianqi
Format:	Preprint
Published:	2024
Subjects:	Computers and Society Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Safeguarding Autonomy: a Focus on Machine Learning Decision Systems
by: Subías-Beltrán, Paula, et al.
Published: (2025)

Evaluation of Machine Learning Models in Student Academic Performance Prediction
by: Sandeepa, A. G. R., et al.
Published: (2025)

Can You Trust an LLM with Your Life-Changing Decision? An Investigation into AI High-Stakes Responses
by: Cahyono, Joshua Adrian, et al.
Published: (2025)

Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
by: Keenan, Bernard, et al.
Published: (2023)

Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap
by: Rosenfeld, Nir, et al.
Published: (2025)

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems
by: Periáñez, África, et al.
Published: (2024)

From Protoscience to Epistemic Monoculture: How Benchmarking Set the Stage for the Deep Learning Revolution
by: Koch, Bernard J., et al.
Published: (2024)

Bridging the Data Provenance Gap Across Text, Speech and Video
by: Longpre, Shayne, et al.
Published: (2024)

Enhancing Project Performance Forecasting using Machine Learning Techniques
by: Sadeghi, Soheila
Published: (2024)

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
by: Tertulino, Rodrigo, et al.
Published: (2025)

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
by: Zheng, Mingqian, et al.
Published: (2023)

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
by: Löfström, Tuwe, et al.
Published: (2024)

Philosophy-informed Machine Learning
by: Naser, MZ
Published: (2025)

On the Societal Impact of Machine Learning
by: Baumann, Joachim
Published: (2025)

Explaining How Quantization Disparately Skews a Model
by: Bellam, Abhimanyu, et al.
Published: (2025)

Creativity and Machine Learning: A Survey
by: Franceschelli, Giorgio, et al.
Published: (2021)

Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
by: Sokol, Kacper, et al.
Published: (2025)

Tuning Derivatives for Causal Fairness in Machine Learning
by: Edström, Filip, et al.
Published: (2026)

Fair Machine Learning in Healthcare: A Review
by: Feng, Qizhang, et al.
Published: (2022)

On The Fairness Impacts of Hardware Selection in Machine Learning
by: Nelaturu, Sree Harsha, et al.
Published: (2023)

Putnam's Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective
by: Soudin, Sheldon Z.
Published: (2025)

Military Applications of Machine Learning: A Bibliometric Perspective
by: Galán, José Javier, et al.
Published: (2024)

Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
by: Zhao, Chen, et al.
Published: (2024)

From Model Training to Model Raising
by: Aydin, Roland, et al.
Published: (2025)

ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
by: Krone, Hendrik, et al.
Published: (2024)

The Metaphysics We Train: A Heideggerian Reading of Machine Learning
by: Shakeri, Heman
Published: (2025)

BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
by: Cohen-Inger, Nurit, et al.
Published: (2025)

Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
by: Liu, Mutong, et al.
Published: (2026)

Can machines think efficiently?
by: Winchell, Adam
Published: (2025)

Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
by: Ovi, Md Sultanul Islam, et al.
Published: (2025)

VirnyFlow: A Design Space for Responsible Model Development
by: Herasymuk, Denys, et al.
Published: (2025)

Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale
by: Cooper, A. Feder
Published: (2024)

Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
by: Roberts-Licklider, Karen, et al.
Published: (2024)

Detection of Problem Gambling with Less Features Using Machine Learning Methods
by: Jiao, Yang, et al.
Published: (2024)

What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity
by: Kong, Youjin
Published: (2025)

Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
by: Toups, Connor, et al.
Published: (2023)

Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning
by: Weerts, Hilde, et al.
Published: (2022)

Synthetic Data Augmentation for Enhancing Harmful Algal Bloom Detection with Machine Learning
by: Huang, Tianyi
Published: (2025)

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning
by: Rabanser, Stephan
Published: (2025)