Artificial Intelligence has transformed how content is created, manipulated, and distributed at scale. News, video, photos, and opinions are now shared across different platforms within seconds. This increased speed and the accessibility of communication thanks to Artificial Intelligence has created an unprecedented challenge for communicators, platforms, and policymakers alike.
Artificial Intelligence is the New Source of Misinformation
Artificial Intelligence is capable of creating images, videos, and written materials that can replicate almost any aspect of human life and be indistinguishable from the original. New technologies like deepfakes are able to create images of people’s faces and sound of people’s voices with such amazing startling accuracy that can be used to make up fabricated narratives, or events that can be quickly spread out into the world. Because misinformation can travel faster than fact-checking activities, one of the greatest challenges in the digital world today has been how to respond to misleading data.
Using AI to Fight AI
Many believe the answer to fighting AI-generated misinformation will come from applying AI to fight against AI. One emerging framework is Composite AI, an end-to-end AI-based framework that includes a set of layers. The outer layer contains the Structured Intent Layer, which uses traditional natural language processing and natural language understanding. The second layer is the Agentic RAG Layer, powered by small language models such as BharatGPT, and the third layer is the General LLM Layer, which includes such systems as ChatGPT, Gemini, and others.
The use of multiple levels of AI as opposed to relying on a single AI will provide validation of the accuracy of information through the comparison of multiple sources, thus, improving contextual accuracy by cross-validating outputs across multiple model layers. The use of a model developed with pre-training and fine-tuning based on BharatGPT, Llama, Gemma, and Nemotron will provide the technology necessary to verify intent and validate factual information with the goal of not only generating a response but also understanding the intent behind the question.
Human-focused AI
In addition, another important trend is the emergence of Human-Centric Conversational Agentic AI Platforms. Participant engagement with human/technology interaction is changing due to multi-lingual, multi-model, and multi-channel technologies – including Telephony AI – being made available through these platforms.
These AI platforms not only improve participant experience and reduce costs associated with operating, but also provide trust and access to information across different languages and audiences. This is especially significant in enabling Voice First and Accessible AI experiences, ultimately contributing to the ease of living for underserved and multilingual populations.
Building Secure and Compliant AI Systems
Another area that is evolving is the development of Full-Stack Sovereign AI. As well as providing protection and security, scalability, and compliance and trust in the adoption of AI solutions, the use of domain-specific and sovereign foundational models (e.g., BharatGPT) developed by CoRover can provide the means for organisations to build more secure and reliable ecosystems.
Transparency Through Clean Labeling and AI Content Disclaimers
People are already struggling to tell what is real and what is not online. Clean labeling is one of the most straightforward ways to fix that. Content produced, modified, or verified by AI must carry clear and visible markers such as “AI-Generated” or “AI-Assisted” across text, image, audio, and video formats.
Regulatory frameworks are already moving in this direction. The EU AI Act and U.S. FTC guidelines both require disclosure of AI involvement in consumer-facing content. Beyond legal compliance, AI systems themselves should clearly document their data sources, limitations, and intended use. Users engaging with chatbots, voicebots, or videobots also deserve to know when they are interacting with AI rather than a human being.
Won’t Technology Ever Win?
While technology may not succeed in completely eradicating misinformation, the combination of Purpose Led Models, Conversational Agentic AI Platforms, AI Assistant Technology (including ChatBots, VoiceBots, and VideoBots), and Composite AI architectures are likely to incrementally improve accuracy and trustworthiness.
Even though AI can provide additional defence against false or misleading content, digital literacy and responsible use will continue to play a large role in providing the means for defeating such misinformation. Ultimately, the true measure of success is simple: AI should solve the problems of society and organisations at scale, and fighting misinformation is one of the most critical problems of our time.
