Latest Articles for Fairness

Discussing privacy and fairness in machine learning through differential privacy and worst-group risk.

A study on students' views regarding fairness and ethics in MMLA.

An analysis of fairness discussions among software engineers on Stack Exchange.

A look into how fairness affects job and opportunity matching.

Addressing challenges in federated learning due to diverse devices and data.

A look at balancing fairness and accuracy in AI decision-making.

A framework to evaluate biases in recommendations generated by large language models.

Addressing fairness in machine learning for better statistical outcomes.

Exploring the principles and importance of ethical AI use.

A framework to evaluate how AI solutions affect health equity in healthcare.

A novel method for fair data collection in predictive modeling.

A look at how satisfiability, coverage, and fairness intersect in optimization.

Exploring the need for clear AI specifications to tackle bias and transparency.

Exploring the balance between model compression and trustworthiness in AI.

Exploring the key traits and challenges of developing reliable AI systems.

A new method integrates constraints into probabilistic circuits for better predictions.

Examining the role of communication in fairness decisions within AI systems.

Discover how distributed algorithms streamline resource allocation among agents.

A method to approximate fairness-accuracy trade-offs for machine learning models.

A new method enhances fairness in network traffic management using RTT data.

A multi-objective method for maximizing influence while considering various factors.

Explore sequential unanimity rules and their role in decision-making.

THEMIS improves fairness and efficiency in multi-tenant FPGA resource scheduling.

This article discusses robust accurate fairness in deep learning and its effects.

A study focusing on fair and explainable models for substance use disorder treatment predictions.

Examining methods to improve facial recognition across diverse demographics.

New algorithms ensure fairness and diversity in data selection.

A new method enhances fairness in AI decision-making without changing existing models.

Data pruning improves model efficiency while addressing potential bias issues.

Adaptive Fair Representation Learning offers fair and accurate recommendations tailored to individual user needs.

Researchers aim to generate balanced synthetic data to prevent bias in machine learning.

Introducing randomness can improve fairness in resource distribution decisions.

A new method aims to improve fairness in machine learning without sacrificing performance.

A guide to metrics for assessing LLM performance in various fields.

A new tool offers flexible definitions of fairness for machine learning analysis.

Exploring methods to achieve fairness in classification systems without sacrificing performance.

A method promoting fairness among agents competing for limited resources.

A novel method to ensure fairness in AI systems using historical data.

Exploring a new method for equitable resource allocation using karma principles.

A new framework enhances evaluation of federated learning algorithms across diverse applications.