Latest Articles for Ethics In Technology

FairDP offers a solution for ensuring privacy and fairness in machine learning systems.

Examining how AI models can reflect social biases in database queries.

A look at monitoring fairness in algorithmic decision-making.

New methods improve attribution of AI-generated content, enhancing accountability and addressing ethical concerns.

Introducing a method that robustly identifies AI-written content without prior training.

A new method reduces bias in NLP models using dynamic clustering and active learning.

A new approach to align AI language models with societal norms through simulated interactions.

Examining the trust and ethics surrounding protestware in open source software.

Evaluating strategies to manage inappropriate outputs from image generation models.

Examining how selective classifiers preserve privacy and prediction accuracy.

Exploring the need for ethical and trustworthy AI development.

A new method enhances summary accuracy while maintaining informative content.

Studying how biases in training data influence model behavior and performance.

This article examines the structure of hate speech arguments in social media.

This article examines efficient methods for debiasing language models.

A study on how ChatGPT handles prompts and addresses bias in responses.

A new method targets gender bias in language models while minimizing data use.

Exploring methods to ensure reliability and clarity in AI decision-making.

Understanding models that combine various data types for better output generation.

A study on using triplet loss to create fairer machine learning models.

Discover how language models enhance customer service efficiency and reduce costs.

Content moderation is crucial for the responsible use of generative AI systems.

Examining how teams can improve fairness in AI through better collaboration.

A study reveals a method to create adversarial examples while maintaining their meaning.

A fresh approach to reduce bias in facial expression recognition systems.

Examining how manipulation affects the interpretation of deep neural networks.

This study evaluates GPT-4's ability to extract social health factors from records.

A method to reduce bias in AI training datasets for fairer outcomes.

A new approach enhances fairness and safety in ML systems.

Testing language models to identify harmful outputs before real-world application.

A study on linking fine-tuned models to their base versions.

Examining the need for regulations and the introduction of AI Architects for safety.

Exploring the balance between human input and machine learning capabilities.

Introducing GMMD, a framework to enhance fairness in graph neural networks.

New methods in AMR parsing enhance language comprehension and graph accuracy.

A comprehensive overview of the OBELICS dataset creation and its implications for machine learning.

Examining the intersection of AI image generation and copyright risks.

Examining methods to find and reduce dishonesty in AI behavior.

This article discusses methods to enhance fairness in machine learning by correcting label noise.

Discover the importance of fairness and the CIF framework in machine learning.