#FactCheck- Old Bangladesh Clip Misused as West Bengal Election Incident

Introduction

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India is seeing a major change due to the introduction of Artificial Intelligence (AI) across all sectors of government, business, and the digital economy with regard to areas such as governance, healthcare, finance, and the infrastructure. The large scale and rapid pace of AI implementation are expected to lead to efficiency gains, innovations in products and services, and to drive economic growth; however, the growth of AI also creates many serious concerns regarding ethics, legality, and societal ramifications. Issues such as algorithmic bias in the use of algorithms by AI, a lack of transparency in decision-making algorithms, data protection risks resulting from AI employments, and unclear frameworks for determining accountability for AI-related action; bring issues of how we will govern AI in a responsible manner to the forefront of public policy discourse.

India wants to become an AI superpower and leader in technology on the world stage. As such, India has a dual responsibility to fuel innovation without discounting democratic ideals, human rights, and public trust. UNESCO's AI Readiness Assessment Methodology (RAM) is a global tool for AI governance, created to provide concrete policy guidance on how to make ethical AI a reality. The India AI RAM Report is set to be formally released by UNESCO during the India AI Impact Summit 2026, taking place in New Delhi, as a major milestone in India's developing AI governance journey.

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What is UNESCO’s AI Readiness Assessment Methodology (RAM)?

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UNESCO has created a simple yet effective tool, called the AI Readiness Assessment Methodology (RAM), that can assist governments in determining how well they are prepared to develop, deploy and manage Artificial Intelligence ethically, responsibly and trustworthily. RAM provides a framework for diagnosing and self-assessing the state of a country’s ability to govern AI on the basis of evidence-based decision making rather than serving as a regulatory framework or ranking system.

The most important goal of RAM is to assess a country’s overall state of readiness to govern AI based on four dimensions: institutionally, legally, socially and technologically. In doing so, RAM examines how institutions function, their maturity level and the extent to which various policies align with one another; thereby giving governments an overview of strengths, weaknesses and priorities for reform.

Unlike other frameworks, RAM does not prescribe any one-size-fits-all solutions; instead, it uses a context sensitive approach when implementing the concepts of AI governance due to differing national realities, developmental priorities and social/economic conditions. Using the ethical principles established by UNESCO, RAM converts these principles into practical actions that can guide countries in their transition from abstract commitments to concrete strategies for governing AI.

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Key Dimensions Assessed Under RAM

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UNESCO's AI Readiness Assessment Methodology (RAM) is a tool used to assess a country's readiness to implement ethical Artificial Intelligence through five interconnected dimensions. These include: the legal and regulatory dimension (which looks at the laws, rules, and safeguards that are currently in place related to AI), the social and cultural dimension (which looks at whether the public is aware of AI, whether it trusts AI, whether AI is an inclusive experience for all people who use AI and whether AI has affected society in various ways), and the economic dimension (which looks at innovation, participation from industry, and readiness of the market for AI).

Also included in the framework/functionality of the RAM are: scientific and educational dimension (which examines a country’s capacity to conduct serious scientific research, including research activities that prepare persons to be employable in AI jobs); and technological and infrastructure dimension (which examines the availability of data, digital infrastructure, and computing capabilities for AI projects in a country).

All five of these dimensions consider the entirety of the scope of an AI readiness evaluation to ensure that AI Governance is more than just a technical issue; rather, it is a condition of a country’s capacity to generate laws, create policy and maintain social equality in relation to all forms of Artificial Intelligence.

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Methodology and Nationwide Consultative Process

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RAM takes both qualitative and quantitative characteristics together to create an overall understanding of how ready any nation is for AI capabilities. It is designed with flexibility so nations can define their assessments with respect to their own institutional capabilities and development agenda.

Normally, RAM is implemented by an independent expert who is assisted by a national team consisting of various stakeholders. With respect to the RAM process used in India, it was conducted as a national consultation where representatives from across all sectors of society (government, private sector, academia, civil society, and young people) participated in the assessment's creation. This consultation process made sure there were many different viewpoints present, which increased the legitimacy of the assessment results and how relevant they are in each country. The consultation process also yields policy recommendations based on real life governing situations or challenges that are specific to different sectors.

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Institutional Partnerships Behind India’s RAM

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The India RAM Initiative was developed by the UNESCO South Asia Regional Office (as a partner of IndiaAI Mission and the Indian Ministry of Electronics and Information Technology) and implemented by Ikigai Law with the help of The Patrick J. McGovern Foundation. This demonstrated the need for and importance of partnership in developing governance frameworks for Artificial Intelligence (AI). The result of the RAM process is a collaborative effort that includes evidence-based international norm-setting capabilities from around the world; government policies under the guidance of national political leadership; independent legal-technical implementation; input from civil society; all with the goal of empowering (increasing) India's ability to establish and implement both a consistent (i.e., coherent) and comprehensive (i.e., inclusive) AI Governance Framework.

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Significance of the India AI RAM Report and Its Launch

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The India AI RAM Report provides a complete initial assessment of India’s AI ecosystem and includes key insights into AI readiness, governance strengths/weaknesses, and potential opportunities across multiple sectors. It identifies priority areas to promote a responsible and trustworthy AI ecosystem in India.

The report will be officially released during the India AI Impact Summit (February 16, 2026 at Bharat Mandapam, New Delhi) where Mr. Abhishek Venkateswaran (National Project Officer-Social and Human Sciences at UNESCO South Asia) offered additional insight into the consultative process and the overall importance of this launch on India's future AI policy path.

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Policy Relevance and the Road Ahead

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The RAM Framework gives the government a structure and roadmap for developing and implementing AI Governance. In doing this, RAM reinforces the alignment of IndiaAI Mission, which includes safety and trust in AI as one of the pillars. However, the results from this Assessment will not automatically translate to reforming institutions, issuing guidelines specific to sectors, or developing a mechanism for continued evaluation. Implementation will require strong and sustained commitment from political leaders, as well as the commitment of institutions involved in the reforms made possible by RAM's implementation.

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Conclusion

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UNESCO has developed an AI Readiness Assessment Methodology (AI-RAM) that can greatly advance the way India approaches governance with respect to artificial intelligence (AI). By focusing on "readiness" (doing what needs to be done), "responsibility" (being or having good moral principles) and "inclusivity" (including everyone), the AI-RAM will enable India to become an active participant in discussions around ethical use of AI at a global level. India is now positioned to take on a leadership role in the world by adopting this methodology, which provides a platform for establishing global standards for AI development. The real benefit of the AI-RAM will come from policy measures that will ensure future AI development in India is 'human-centered', 'trustworthy' and 'aligns with democratic values'.

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References

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1. https://icaire.org/files/UNESCORam-en.pdf
2. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2134492&reg=3&lang=2 
3. https://www.facebook.com/unesconewdelhi/videos/unesco-is-set-to-launch-the-india-ai-readiness-assessment-methodology-ram-report/25955631820699516/
4. https://www.unesco.org/ethics-ai/en/ram
5. https://www.hindustantimes.com/india-news/unesco-meity-launch-exercise-to-assess-india-s-ai-readiness-101749188341803.html#
6. https://www.manoramayearbook.in/current-affairs/india/2025/06/09/unesco-ai-readiness-assessment-methodology-ram.html

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Executive Summary
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A video circulating on social media allegedly shows an Indian Army Air Defence JCO, identified as “Anurag Thakur,” criticising the Central government and announcing his resignation. In the clip, the individual is purportedly heard expressing disappointment with the government’s support for soldiers and claiming heavy casualties within his unit. However, an investigation by the CyberPeace Research Wing has found the claim to be false and the video manipulated.

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Claim:
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The viral post alleges that an Indian Army Air Defence JCO criticised the Centre and resigned, citing lack of support for soldiers and unacknowledged casualties.

• https://archive.ph/mGa5I 

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Fact Check:
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A keyword search using terms such as “Indian Army Air Defence JCO,” “Anurag Thakur,” and “resignation” did not yield any credible news reports supporting the claim. A reverse image search of the viral clip led to an original video posted by ANI on April 22, 2026. The ANI watermark is also visible in the viral footage, confirming that the clip has been sourced from this original video.

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In the authentic version, the same personnel appears in the same setting, but he is speaking in Hindi about operations against adversary drones during “Operation Sindoor” in May 2025. He explains how his unit detected and neutralised multiple drones, successfully safeguarding a critical installation. Importantly, the original video does not identify the individual as Anurag Thakur, nor does it contain any reference to resignation, casualties, or criticism of the government.

Further technical analysis using the AI detection tool Hive Moderation indicated that the audio in the viral clip has an 86 percent likelihood of being artificially generated.

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Conclusion:
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The viral video has been altered with fabricated audio to falsely claim that an Indian Army officer criticised the government and resigned. The claim is baseless and the clip is a clear case of a deepfake created to mislead viewers.

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Introduction

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Misinformation is no longer a challenge limited to major global platforms or widely spoken languages. In India and many other countries, false information is increasingly disseminated through local and vernacular languages, allowing it to reach communities more directly and intimately. While regional language content has played a crucial role in expanding access to information, it has also emerged as a powerful driver of misinformation by bad actors, and it often becomes harder to detect and counter. The challenge of local language misinformation is not merely digital in nature; it is deeply social, cultural, and shaped by specific local contexts.

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Why Local-Language Misinformation Is More Impactful

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A person’s mother tongue can be a highly effective medium for misinformation because it carries emotional resonance and a sense of authenticity. Information that aligns with an individual’s linguistic and cultural background is often trusted the most. When false narratives are framed using familiar expressions, local references, or community-specific concerns, they are more readily accepted and shared more widely.

Misinformation in a language like English, which is more heavily moderated, does not usually have the same impact as content in vernacular languages. In the latter case, such content tends to circulate within closed networks such as family WhatsApp groups, regional Facebook pages, local YouTube channels, and community forums. These spaces are often perceived as safe or trusted, which lowers scepticism and encourages the spread of unverified information.

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The Role of Digital Platforms and Algorithms

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Although social media platforms have opened up access to the content of regional languages, the moderation mechanisms have not kept up. The automated control systems for content are frequently trained mainly on the dominant languages, thus missing the detection of vernacular speech, slang, dialects, and code-mixing. 

This results in a disparity in the enforcement of laws where misinformation in local languages:

• Doesn’t go through automated fact-checking tools
• Is subject to human moderation takes place at a slower pace
• Is less prone to being reported or flagged
• Gains unrestrained access for a longer time period than first imagined

The problem is further magnified by algorithmic amplification. Content that triggers very strong emotional reactions fear, anger, pride, or outrage, has a higher chance of being promoted, irrespective of its truthfulness. In regional situations, such content may very quickly sway public opinion even in very closely knit communities.

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Forms of Vernacular Misinformation

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Local-language misinformation appears in various forms:

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• Health misinformation, with such examples as panic remedies, vaccine myths, and misleading medical prescriptions
• Political misinformation, which is mostly identified with regional identity, local grievances, or community narratives
• Rumours regarding disasters that are very hard to control and spread hatred during floods, earthquakes, or other public emergencies
• Economic and financial frauds that are perpetrated via the local dialect authorities or trusted institutions
• Cultural and religious untruths, which are based on exploiting the core of the beliefs

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The regional aspect of such misinformation makes it very difficult to be corrected because the fact-checks in other languages may not get to that audience.

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Community-Level Consequences

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The effect of misinformation in local languages is not only about the misdirection of individuals. It can also:

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• Negatively affect the process of public institutions gaining trust
• Support social polarisation and communal strife
• Get in the way of public health measures
• Help shape the decision-making process in elections at the grassroots level
• Take advantage of the digitally illiterate poor people

In a lot of scenarios, the damage done is not instant but rather accumulative, thus changing perceptions and supporting false worldviews more.

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Why Countering Vernacular Misinformation Is Difficult

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Multiple structural layers make it difficult to respond effectively:

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1. Variety of Languages: Just in India, there are many languages and dialects, which are very hard to monitor universally.
2. Culturally Aware Systems: The local languages sometimes bear meanings that are deeply rooted in the culture, such as by using sarcasm or referring to history, and automated systems are unable to interpret it correctly.
3. Reporting Not Common: Users might not spot misinformation or may not want to be a part of the struggle by showing the content shared by reliable members of the community.
4. Insufficient Fact-Checking Capacity: Resources are often unavailable for fact-checking organisations to perform their duties worldwide in different languages effectively.

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Building a Community-Centric Response

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Overcoming misinformation in local languages needs a community-driven resilience approach instead of a platform-centric one. Some of the key actions are: 

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• Boosting Digital Literacy: Users will be able to question, verify, and put the content on hold before sharing it, thanks to the regional language awareness campaigns that will be conducted.
• Facilitating Local Fact-Checkers: Local journalists, educators, and NGOs are the main players in providing the context for verification.
• Accountability of Platforms: It is necessary for technology companies to support global moderation in several languages, the hiring of local experts, and the implementation of transparent enforcement mechanisms.
• Contemplating Policy and Governance: Regulatory frameworks should facilitate proactive risk assessment while controlling the right to free expression.
• Establishment of Trusted Local Intermediaries: Community leaders, health workers, teachers, and local organisations can engage in preventing misinformation among the networks that they are trusted in.

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The Way Forward

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Misinformation in local languages is not a minor concern; it is an issue that directly affects the future of digital trust. As the number of users accessing the internet through local language interfaces continues to grow, the volume and influence of regional content will also increase. If measures do not include all language groups, misinformation will remain least corrected and most influential at the community level, where it is also the hardest to identify and address.

Such a problem exists only if the power of language is not recognised. Therefore, one can say that it is necessary to protect the quality of information in local languages, not only for digital safety but for other factors as well, such as social cohesion, democratic participation, and public well-being.

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Conclusion

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Vernacular content has the potential to be very powerful in the ways it can inform, include and empower; meanwhile, if it goes unmonitored, it has the same potential to mislead, divide, and harm. Mis-disinformation in local languages calls for the cooperation of platforms, regulators, NGOs, and the communities involved. To win over the digital ecosystem, it has to speak all languages, not only for communication but also for protection.

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References

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• https://www.mdpi.com/2304-6775/10/2/15
• https://afpr.in/regional-languages-shaping-indias-online-discourse/
• https://medium.com/@pratikgsalvi03/how-indias-misinformation-surge-and-media-credibility-crisis-are-undermining-democracy-public-dc8ad7be8e12
• https://projectshakti.in/
• https://journals.sagepub.com/doi/10.1177/02683962211037693
• https://rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-11/505-518.pdf
• https://www.irjmets.com/upload_newfiles/irjmets71200016652/paper_file/irjmets71200016652.pdf

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