Latest Articles for Technology Ethics

The quest for explainable AI focuses on transparency and trust in decision-making.

Examining how AI language models reflect biases against marginalized communities.

A novel technique checks for training data exposure in diffusion models.

This research presents a method to identify real versus generated graphs.

A new method employs neural architecture search to improve face forgery detection.

Exploring the importance of public involvement in AI and its challenges.

A look at gender bias in AI and its impact on society.

Mobile apps often lack clarity in data collection, impacting user trust.

This article explores the impact of gender bias in sentiment analysis with BERT.

Exploring the nature of beliefs in large language models.

Explainability in AI is crucial for trust in critical fields like healthcare.

International cooperation is essential for managing AI risks and benefits.

Small biases in AI can lead to major unfair outcomes.

This article explores the link between feature attributions and counterfactual explanations in AI.

Examining the risks and explainability challenges of adversarial attacks on AI models.

Exploring privacy risks and strategies for managing data leakage in language models.

This article examines methods to test language models for bias.

Diversity in AI design is vital to prevent bias and promote fairness.

This article discusses creating fair hashmaps for equitable data management.

Introducing a secure method to identify machine-generated text.

Examining how biases in AI affect job suggestions for different groups.

A look into how empathetic ChatGPT truly is.

Examining the challenges and opportunities of differential privacy in data analysis.

Examining risks of re-identification in anonymized court rulings using language models.

Exploring a new method to protect privacy in causal research while maintaining accuracy.

A new dataset provides insights into bias in language technology.

Using perplexity to identify risky inputs in language models.

Research analyzes ChatGPT's handling of biases in controversial discussions.

Examining the risks associated with leading computer vision models and their effectiveness.

A structured approach to creating effective datasets for hate speech analysis.

A study reveals gender bias in AI across different cultures.

This study investigates biases in language models using prompt-based learning.

Fairness as a Service tackles bias in machine learning systems securely.

Exploring the need for clear explanations in AI decision-making, especially quantum models.

A new framework aims to clarify AI decision-making for humans.

A study on how users interpret AI explanations and their limitations.

Exploring how attackers exploit large language models for knowledge extraction.

A study on identifying human vs. machine-generated texts and their sources.

Knowledge sanitization helps protect sensitive information in language models.

Examining the effectiveness of watermarking against adaptive attacks on deepfake images.