Image de couverture de livre

Philosophy-informed Machine Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Naser, MZ
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Computers and Society Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics
par: Nguyen, Tin Trung, et autres
Publié: (2025)

On the Societal Impact of Machine Learning
par: Baumann, Joachim
Publié: (2025)

Creativity and Machine Learning: A Survey
par: Franceschelli, Giorgio, et autres
Publié: (2021)

Tuning Derivatives for Causal Fairness in Machine Learning
par: Edström, Filip, et autres
Publié: (2026)

Fair Machine Learning in Healthcare: A Review
par: Feng, Qizhang, et autres
Publié: (2022)

On The Fairness Impacts of Hardware Selection in Machine Learning
par: Nelaturu, Sree Harsha, et autres
Publié: (2023)

Military Applications of Machine Learning: A Bibliometric Perspective
par: Galán, José Javier, et autres
Publié: (2024)

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

BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
par: Cohen-Inger, Nurit, et autres
Publié: (2025)

Evaluation of Machine Learning Models in Student Academic Performance Prediction
par: Sandeepa, A. G. R., et autres
Publié: (2025)

Safeguarding Autonomy: a Focus on Machine Learning Decision Systems
par: Subías-Beltrán, Paula, et autres
Publié: (2025)

ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
par: Krone, Hendrik, et autres
Publié: (2024)

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

Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
par: Toups, Connor, et autres
Publié: (2023)

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

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
par: Liu, Mingxuan, et autres
Publié: (2024)

Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
par: Roberts-Licklider, Karen, et autres
Publié: (2024)

Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning
par: Weerts, Hilde, et autres
Publié: (2022)

Detection of Problem Gambling with Less Features Using Machine Learning Methods
par: Jiao, Yang, et autres
Publié: (2024)

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

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

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

Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap
par: Rosenfeld, Nir, et autres
Publié: (2025)

Quantifying Itch and its Impact on Sleep Using Machine Learning and Radio Signals
par: Ouroutzoglou, Michail, et autres
Publié: (2025)

Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
par: Asiedu, Mercy Nyamewaa, et autres
Publié: (2023)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
par: Löfström, Tuwe, et autres
Publié: (2024)

Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation
par: Alcalde-Llergo, José Manuel, et autres
Publié: (2026)

Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
par: Ovi, Md Sultanul Islam, et autres
Publié: (2025)

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
par: Gohar, Usman, et autres
Publié: (2024)

A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
par: Fayaz, Sheikh Junaid, et autres
Publié: (2026)

Ethnography and Machine Learning: Synergies and New Directions
par: Li, Zhuofan, et autres
Publié: (2024)

Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches
par: Lima, Vinicius, et autres
Publié: (2024)

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
par: Tertulino, Rodrigo, et autres
Publié: (2025)

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
par: Li, Siqi, et autres
Publié: (2024)

From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap
par: Kou, Tianqi
Publié: (2024)

Enhancing Project Performance Forecasting using Machine Learning Techniques
par: Sadeghi, Soheila
Publié: (2024)

Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
par: Adibi, Amin, et autres
Publié: (2025)

Open Problems in Machine Unlearning for AI Safety
par: Barez, Fazl, et autres
Publié: (2025)

A Look into How Machine Learning is Reshaping Engineering Models: the Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the Inevitable Rashomon Paradox
par: Naser, MZ
Publié: (2025)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
par: Schaeffer, Rylan, et autres
Publié: (2025)