Latest Articles for Ethics In Technology

Research presents a framework to reduce bias in AI-generated text.

Workshops enhance understanding of Responsible AI for industry professionals.

Research reveals how friendly prompts can mislead AI systems.

A robust dataset for training advanced chat-based AI systems.

A framework to incorporate minority voices in annotation processes.

Learn how Text Style Transfer changes text style while preserving meaning.

A new method enhances AI model alignment without retraining.

PUFFLE offers a solution for privacy, utility, and fairness challenges in machine learning.

New model efficiently creates realistic 3D human head representations.

This article discusses how LLM reasoning enhances recommendation systems and introduces Rec-SAVER.

A method to reduce bias in language models by making them forget harmful information.

Combining machine learning with automated reasoning for clearer AI explanations.

Understanding techniques to bypass safety in language models.

Exploring the use of watermarks to tackle copyright issues in language models.

A new synthetic dataset enables accurate head detection and 3D modeling.

A detailed study on how models memorize text and its implications.

An analysis of how surveys impact AI research, values, and public engagement.

This project aims to identify and reduce bias in language models across European languages.

A deep dive into the importance of interpreting NLP models.

Examining the methods for preparing data in model training.

A new approach to assess the reliability of methods explaining AI decision-making.

Examining fairness issues in anomaly detection algorithms for facial images.

Exploring machine unlearning and its role in enhancing generative AI safety and privacy.

Exploring human biases and their impact on AI fairness.

New methods detect and respond to memorization in AI-generated content.

Exploring principles for ethical relationships between people and their data.

New methods tackle issues of copying in image generation models.

Examining biases and fairness in large language models.

Exploring the role and challenges of LLMs in knowledge engineering.

Study reveals gaps in representation for marginalized users of Stable Diffusion.

A new model for realistic face swapping using advanced techniques.

An overview of risks and methods related to language model safety.

A look at bias and fairness in computer vision technology.

Larger language models show increased vulnerability to harmful data behaviors.

Innovative methods to enhance fairness in large language models.

Examining risks of many-shot jailbreaking in Italian language models.

Shuffling attacks reveal vulnerabilities in AI fairness assessments using methods like SHAP.

This article examines how different contexts affect fairness testing results in AI.

Discover the latest developments in text-to-image models and their impact.

Introducing BMFT: a method to enhance fairness in machine learning without original training data.