# AI as a Tool for Fair Journalism: Case Studies from Malta

arXiv: https://arxiv.org/abs/2407.15316 · HTML: https://mlsanity.com/paper/2407.15316/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [VLP: A Survey on Vision-Language Pre-training](https://mlsanity.com/paper/2202.09061/) (2022)
- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)
- [A Survey on Optical Character Recognition System](https://mlsanity.com/paper/1710.05703/) (2017)

## Built on by (most cited first, 4 of 4)

- ["It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry](https://mlsanity.com/paper/2409.12000/) (2024)
- [A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces](https://mlsanity.com/paper/2503.17212/) (2025)
- [Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation](https://mlsanity.com/paper/2503.21848/) (2025)
- [A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video](https://mlsanity.com/paper/2602.09154/) (2026)

## Abstract (arXiv)

In today`s media landscape, the role of Artificial Intelligence (AI) in shaping societal perspectives and journalistic integrity is becoming increasingly apparent. This paper presents two case studies centred on Malta`s media market featuring technical novelty. Despite its relatively small scale, Malta offers invaluable insights applicable to both similar and broader media contexts. These two projects focus on media monitoring and present tools designed to analyse potential biases in news articles and television news segments. The first project uses Computer Vision and Natural Language Processing techniques to analyse the coherence between images in news articles and their corresponding captions, headlines, and article bodies. The second project employs computer vision techniques to track individuals` on-screen time or visual exposure in news videos, providing queryable data. These initiatives aim to contribute to society by providing both journalists and the public with the means to identify biases. Furthermore, we make these tools accessible to journalists to improve the trustworthiness of media outlets by offering robust tools for detecting and reducing bias.