#FactCheck: Viral Fake Post Claims Central Government Offers Unemployment Allowance Under ‘PM Berojgari Bhatta Yojna’
Executive Summary:
A viral thumbnail and numerous social posts state that the government of India is giving unemployed youth ₹4,500 a month under a program labeled "PM Berojgari Bhatta Yojana." This claim has been shared on multiple online platforms.. It has given many job-seeking individuals hope, however, when we independently researched the claim, there was no verified source of the scheme or government notification.
Claim:
The viral post states: "The Central Government is conducting a scheme called PM Berojgari Bhatta Yojana in which any unemployed youth would be given ₹ 4,500 each month. Eligible candidates can apply online and get benefits." Several videos and posts show suspicious and unverified website links for registration, trying to get the general public to share their personal information.
Fact check:
In the course of our verification, we conducted a research of all government portals that are official, in this case, the Ministry of Labour and Employment, PMO India, MyScheme, MyGov, and Integrated Government Online Directory, which lists all legitimate Schemes, Programmes, Missions, and Applications run by the Government of India does not posted any scheme related to the PM Berojgari Bhatta Yojana.
Numerous YouTube channels seem to be monetizing false narratives at the expense of sentiment, leading users to misleading websites. The purpose of these scams is typically to either harvest data or market pay-per-click ads that suspend disbelief in outrageous claims.
Our research findings were backed up later by the PIB Fact Check which shared a clarification on social media. stated that: “No such scheme called ‘PM Berojgari Bhatta Yojana’ is in existence. The claim that has gone viral is fake”.
To provide some perspective, in 2021-22, the Rajasthan government launched a state-level program under the Mukhyamantri Udyog Sambal Yojana (MUSY) that provided ₹4,500/month to unemployed women and transgender persons, and ₹4000/month to unemployed males. This was not a Central Government program, and the current viral claim falsely contextualizes past, local initiatives as nationwide policy.
Conclusion:
The claim of a ₹4,500 monthly unemployment benefit under the PM Berojgari Bhatta Yojana is incorrect. The Central Government or any government department has not launched such a scheme. Our claim aligns with PIB Fact Check, which classifies this as a case of misinformation. We encourage everyone to be vigilant and avoid reacting to viral fake news. Verify claims through official sources before sharing or taking action. Let's work together to curb misinformation and protect citizens from false hopes and data fraud.
- Claim: A central policy offers jobless individuals ₹4,500 monthly financial relief
- Claimed On: Social Media
- Fact Check: False and Misleading
Related Blogs
Executive Summary
A video showing a military convoy moving along a road is being widely circulated on social media with the claim that the entry of CRPF forces into West Bengal has changed the situation on the ground, suggesting strict action is underway during the ongoing elections. However, research by CyberPeace found the claim to be misleading. The video is not recent and has been available online since February 2025.
Claim
The 12-second viral clip shows multiple heavy vehicles moving in a convoy on a road. It has been shared on X (formerly Twitter) with a caption claiming that CRPF’s entry into West Bengal has led to a shift from dialogue to strong action, along with communal assertions.
Fact Check
During the verification process, we found that the same video had been posted by several X users around February 17, 2025. In those earlier posts, the video was described as being from Manipur, not West Bengal.
Further analysis revealed that the video contains background audio in the Manipuri language. To confirm this, we contacted a Manipuri journalist, who stated that the audio includes announcements asking people to stay indoors and avoid gathering on the streets. Notably, this audio is missing in the currently viral version of the clip.Although we could not independently verify the exact date and precise location of the footage, visual elements such as road dividers and streetlight patterns closely resemble those found in Imphal, the capital city of Manipur.
Additionally, reports confirm that central armed police forces have indeed been deployed in West Bengal for election duties in multiple phases. However, there is no evidence linking this specific video to those deployments.
Conclusion
The viral claim is misleading. The video does not show CRPF deployment in West Bengal during the ongoing elections. Instead, it appears to be an older clip from Manipur, likely recorded in early 2025, and has been shared with a false and communal narrative. There is no credible evidence to support the claim made alongside the video. Users are advised to verify content before sharing, especially during sensitive events like elections.
The Expanding Governance Challenge of Artificial Intelligence
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).
The Lifecycle of an AI System
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.
Where AI Risks Emerge
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.
AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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.
AI System Lifecycle: Key Risks at Each Stage
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.
Policy Tools for Mitigating AI Risks
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.
Governance Overlay: Policy Interventions Across the AI Lifecycle
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.
The Policy Gap in AI Governance
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
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.
Recommendations
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.
Conclusion - The Need for Global AI Governance
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.
References
- 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
Executive Summary
A video showing a massive fire and explosion is going viral on social media. The clip shows a large plume of smoke followed by a sudden blast. It is being shared with the claim that it depicts Iran attacking a nuclear reactor in Israel amid the ongoing Iran-Israel conflict. However, research by CyberPeace found that the claim is misleading. The viral video is actually from 2017 and shows a massive explosion at an ammunition depot in Ukraine.
Claim:
On social media platform X (formerly Twitter), a user shared the video on March 21, 2026, with the caption:“Israel’s nuclear reactor was targeted with Fateh and Khyber missiles. Well done Iran! The whole world is with you.”
Fact Check:
To verify the viral claim, we extracted keyframes from the video and conducted a reverse image search. During this process, we found the same video uploaded on March 23, 2017, on a YouTube channel named “null.” According to the upload, the video shows a massive explosion at an ammunition depot in Balakliya, Ukraine. Using these clues, we performed a keyword search and found a report published on March 24, 2017, by Global News.
According to the report, a major fire and explosion broke out at a large military ammunition depot in Balakliya, located in Ukraine’s Kharkiv region. The incident resulted in one death, while nearly 20,000 people from surrounding areas were evacuated to safer locations.
Conclusion:
The claim that the video shows Iran attacking a nuclear reactor in Israel is misleading. The viral footage is actually from 2017 and depicts an explosion at an ammunition depot in Ukraine.
