#FactCheck - Viral Clip Not Harish Rana’s Farewell, Linked to Surat Woman’s Organ Donation

Introduction

Rajeev Chandrasekhar, Minister of State at the Ministry of Electronics and Information Technology, has emphasised the need for an open internet. He stated that no platform can deny content creators access to distribute and monetise content and that large technology companies have begun to play a significant role in the digital evolution. Chandrasekhar emphasised that the government does not want the internet or monetisation to be in the purview of just one or two companies and does not want 120 crore Indians on the internet in 2025 to be catered to by big islands on the internet.

The Voice for Open Internet 

India's Minister of State for IT, Rajeev Chandrasekhar, has stated that no technology company or social media platform can deny content creators access to distribute and monetise their content. Speaking at the Digital News Publishers Association Conference in Delhi, Chandrasekhar emphasized that the government does not want the internet or monetization of the internet to be in the hands of just one or two companies. He argued that the government does not like monopoly or duopoly and does not want 120 crore Indians on the Internet in 2025 to be catered to by big islands on the internet.

Chandrasekhar highlighted that large technology companies have begun to exert influence when it comes to the dissemination of content, which has become an area of concern for publishers and content creators. He stated that if any platform finds it necessary to block any content, they need to give reasons or grounds to the creators, stating that the content is violating norms.

As India tries to establish itself as an innovator in the technology sector, a recent corpus of Rs 1 lakh crore was announced by the government in the interim Budget of 2024-25. As big companies continue to tighten their stronghold on the sector, content moderation has become crucial. Under the IT Rules Act, 11 types of categories are unlawful under IT Act and criminal law. Platforms must ensure no user posts content that falls under these categories, take down any such content, and gateway users to either de-platforming or prosecuting. Chandrasekhar believes that the government has to protect the fundamental rights of people and emphasises legislative guardrails to ensure platforms are accountable for the correctness of the content.

Monetizing Content on the Platform

No platform can deny a content creator access to the platform to distribute and monetise it,' Chandrasekhar declared, boldly laying down a gauntlet that defies the prevailing norms. This tenet signals a nascent dawn where creators may envision reaping the rewards borne of their creative endeavours unfettered by platform restrictions.

An increasingly contentious issue that shadows this debate is the moderation of content within the digital realm. In this vast uncharted expanse, the powers that be within these monolithic platforms assume the mantle of vigilance—policing the digital avenues for transgressions against a conscribed code of conduct. Under the stipulations of India's IT Rules Act, for example, platforms are duty-bound to interdict user content that strays into territories encompassing a spectrum of 11 delineated unlawful categories. Violations span the gamut from the infringement of intellectual property rights to the propagation of misinformation—each category necessitating swift and decisive intervention. He raised the alarm against misinformation—a malignant growth fed by the fertile soils of innovation—a phenomenon wherein media reports chillingly suggest that up to half of the information circulating on the internet might be a mere fabrication, a misleading simulacrum of authenticity.

The government's stance, as expounded by Chandrasekhar, pivots on an axis of safeguarding citizens' fundamental rights, compelling digital platforms to shoulder the responsibility of arbiters of truth. 'We are a nation of over 90 crores today, a nation progressing with vigour, yet we find ourselves beset by those who wish us ill,' 

Upcoming Digital India Act

Awaiting upon the horizon, India's proposed Digital India Act (DIA), still in its embryonic stage of pre-consultation deliberation, seeks to sculpt these asymmetries into a more balanced form. Chandrasekhar hinted at the potential inclusion within the DIA of regulatory measures that would sculpt the interactions between platforms and the mosaic of content creators who inhabit them. Although specifics await the crucible of public discourse and the formalities of consultation, indications of a maturing framework are palpable.

Conclusion 

It is essential that the fable of digital transformation reverberates with the voices of individual creators, the very lifeblood propelling the vibrant heartbeat of the internet's culture. These are the voices that must echo at the centre stage of policy deliberations and legislative assembly halls; these are the visions that must guide us, and these are the rights that we must uphold. As we stand upon the precipice of a nascent digital age, the decisions we forge at this moment will cascade into the morrow and define the internet of our future. This internet must eternally stand as a bastion of freedom, of ceaseless innovation and as a realm of boundless opportunity for every soul that ventures into its infinite expanse with responsible use. 

References 

• https://www.financialexpress.com/business/brandwagon-no-platform-can-deny-a-content-creator-access-to-distribute-and-monetise-content-says-mos-it-rajeev-chandrasekhar-3386388/
• https://indianexpress.com/article/india/meta-content-monetisation-social-media-it-rules-rajeev-chandrasekhar-9147334/
• https://www.medianama.com/2024/02/223-rajeev-chandrasekhar-content-creators-publishers/

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Executive Summary 

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On January 22, an Indian Army vehicle met with an accident in Jammu and Kashmir’s Doda district, resulting in the death of 10 soldiers, while several others were injured. In connection with this tragic incident, a photograph is now going viral on social media. The viral image shows an Army vehicle that appears to have fallen into a deep gorge, with several soldiers visible around the site. Users sharing the image are claiming that it depicts the actual scene of the Doda accident.

However, an research  by the CyberPeacehas found that the viral image is not genuine. The photograph has been generated using Artificial Intelligence (AI) and does not represent the real accident. Hence, the viral post is misleading.

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Claim

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An Instagram user shared the viral image on January 22, 2026, writing:“Deeply saddened by the tragic accident in Doda, Jammu & Kashmir today, in which 10 brave soldiers lost their lives. My heartfelt tribute to the martyrs who laid down their lives in the line of duty.Sincere condolences to the bereaved families, and prayers for the speedy recovery of the injured soldiers.The nation will forever remember your sacrifice.”

The link and screenshot of the post can be seen below.

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• https://www.instagram.com/p/DT0UBIRk_3k/ 
• https://archive.ph/submit/?url=https%3A%2F%2Fwww.instagram.com%2Fp%2FDT0UBIRk_3k%2F+ 

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Fact Check:  

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To verify the claim, we first closely examined the viral image. Several visual inconsistencies were observed. The structure of the soldier visible inside the damaged vehicle appears distorted, and the hands and limbs of people involved in the rescue operation look unnatural. These anomalies raised suspicion that the image might be AI-generated. Based on this, we ran the image through the AI detection tool Hive Moderation, which indicated that the image is over 99.9% likely to be AI-generated.

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Another AI image detection tool, Sightengine, also flagged the image as 99% AI-generated.

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During further research , we found a report published by Navbharat Times on January 22, 2026, which confirmed that an Indian Army vehicle had indeed fallen into a deep gorge in Doda district. According to officials, 10 soldiers were killed and 7 others were injured, and rescue operations were immediately launched.

However, it is important to note that the image circulating on social media is not an actual photograph from the incident.

• https://navbharattimes.indiatimes.com/state/jammu-and-kashmir/jammu/indian-army-vehicle-falls-into-gorge-in-doda-know-soldiers-death-injured-update/articleshow/127127547.cms

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Conclusion

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CyberPeace research  confirms that the viral image linked to the Doda Army vehicle accident has been created using Artificial Intelligence. It is not a real photograph from the incident, and therefore, the viral post is misleading.

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The Expanding Governance Challenge of Artificial Intelligence
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Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk. 

Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.

The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process. 

This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
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The Lifecycle of an AI System
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AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.

The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.

Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.

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Where AI Risks Emerge
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AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks:  bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence

Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms. 
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AI System Lifecycle: Key Risks at Each Stage
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Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
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Policy Tools for Mitigating AI Risks
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Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment. 

For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.

Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
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The Policy Gap in AI Governance
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The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.

An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment. 

Analysis
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1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.

2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment. 

3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems. 

4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
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Recommendations
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1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.

2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.

3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.

4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
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Conclusion - The Need for Global AI Governance
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Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.

Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
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References
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• OECD AI lifecycle
• OECD AI system lifecycle description
• OECD AI governance lifecycle framework
• EU AI Act overview
• EU AI Act risk categories
• UNESCO Recommendation on the Ethics of AI
• AI governance lifecycle analysis
• OECD AI policy tools database

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