Latest Articles for Technology Ethics

Research reveals bias in AI tools used for hiring based on race and gender.

Investigating how LLMs interact with human thoughts and feelings.

This article discusses methods to make language model outputs fairer.

A method to prevent misuse of text-to-image models while maintaining their lawful applications.

Research shows vulnerabilities in federated learning's approach to text privacy.

Improving models for understanding everyday human-object interactions through innovative approaches.

A look at the importance of safety in AI systems and user interactions.

A novel method improves understanding of language model outputs.

Exploring safety, reliability, and ethical issues in language models.

Examining AI's struggle with honesty and its impact on user trust.

This article examines how AI can impact marginalized groups and ways to improve outcomes.

Explore the privacy challenges posed by inference attacks in machine learning models.

Exploring federated learning and unlearning for user privacy and model integrity.

Exploring the complexities of aligning AI systems with diverse human interests.

This article examines how diffusion models improve image generation and manipulation tasks.

Examining how fairness in machine learning can evolve across decisions and time.

This article discusses the challenges of machine unlearning and a new approach to balance privacy and accuracy.

This article explores techniques and challenges in detecting deepfake media.

Study reveals biases in AI hiring recommendations based on candidate names.

User traits influence the responses of language models and their safety.

Examining how cultural bias affects AI image understanding.

A method to maintain privacy while sharing urban traffic statistics.

This article explores strategies for protecting individual privacy in machine learning.

Learn how differential privacy protects individual data while allowing useful analysis.

A fresh approach to AI combines language models with symbolic programs for better interpretability.

Learn best practices for developing AI models responsibly and effectively.

A look at AI risk categories and the need for unified policies.

New methods reveal serious privacy threats from location data sharing.

This article examines if large language models possess beliefs and intentions.

Discussing long-term fairness in technology and its social impact.

AAggFF introduces adaptive strategies for equitable model performance in Federated Learning.

Tools like OxonFair help ensure fairness in AI decision-making.

Research shows how easily safety features can be removed from Llama 3 models.

Research shows how prompt adjustments can enhance AI responses to diverse cultures.

This article examines risks linked to LLMs and proposes ways to enhance safety.

A new framework aims to detect and fix errors in LVLM outputs.

This study examines privacy issues and protection methods for AI classifiers.

This study evaluates how well AI models understand different cultures.

A new defense method to enhance safety in text-to-image diffusion models.

NFARD offers innovative methods to protect deep learning model copyrights.