Cross-Language Media Review: Identifying Inaccuracies in GESI Conversations

Background

In today’s interconnected digital era, the rapid spread of inaccurate content poses significant challenges to societal well-being and informed discourse. This issue is especially critical in the context of Gender Equality and Social Inclusion (GESI), where inaccuracies can perpetuate biases and hinder progress. In multilingual environments like Sri Lanka, where Tamil and Sinhala are dominant languages, the need for cross-language media solutions to address these challenges is paramount. Ensuring that online narratives are accurate and constructive is essential for fostering an inclusive digital ecosystem.

Objective

The project aimed to:

• Gather and Annotate Data: Build a comprehensive dataset of online content in Tamil and Sinhala, focusing on GESI-related topics.
• Develop Tailored Language Models: Create advanced AI models capable of analyzing and flagging misleading content in these languages.
• Promote Informed Digital Discourse: Ensure that discussions surrounding GESI topics are accurate, inclusive, and constructive.

Approach

To tackle the problem, the team adopted a structured, multi-phase approach:

1. Data Collection: Comprehensive data was gathered from various online platforms, covering content in Tamil and Sinhala related to GESI topics.
2. Annotation: The data was meticulously labeled to classify its accuracy and relevance to GESI discussions.
3. Language Model Development: Advanced AI models were fine-tuned for Tamil and Sinhala to understand language nuances and detect inaccuracies effectively.
4. Evaluation and Optimization: Rigorous testing ensured the models’ accuracy and usability in real-world scenarios.

Tools and techniques used included natural language processing (NLP), deep learning frameworks, and human-in-the-loop data validation processes.

Results and Impact

The project yielded significant outcomes:

• Robust Language Models: Two advanced AI models were successfully developed for Tamil and Sinhala, enabling the detection of misleading content in GESI-related topics.
• Dataset Creation: A rich, annotated dataset became a valuable resource for future research in multilingual AI for social inclusion.
• Enhanced Digital Discourse: The project contributed to fostering a more accurate and inclusive online ecosystem, supporting discussions that drive social progress.

These results align with the broader goal of promoting equity and inclusivity in online spaces, particularly for underrepresented communities.

Future Implications

This project has laid the groundwork for future advancements in multilingual AI. Its findings can:

• Inform Policy Making: Provide data-driven insights to policymakers aiming to regulate digital content for inclusivity.
• Enable Broader Applications: Serve as a model for tackling misinformation in other languages and contexts.
• Encourage Further Research: Inspire continued exploration into the intersection of technology, social inclusion, and public discourse.

By pioneering AI solutions in underserved languages, this initiative supports a global movement toward equitable digital communication.