#FactCheck: Viral AI Video Showing Finance Minister of India endorsing an investment platform offering high returns.

Introduction

Personalised advertisements deploy a mechanism that derives from the collection of the user’s data. Although it allows for a more tailored user experience, one cannot ignore the method through which this is achieved. Recently, as per a report by the Indian Express on 13th November 2024, Meta has come up with a less personalised ad option on Facebook and Instagram for its users in the European Union (EU). This was done due to the incompatibility of their previous ad offer with the EU’s Digital Markets Act (DMA). 

Relevant Legislation

In October 2023, Meta came up with a “Pay or Consent” option for their users in the EU. It gave the users two options: either to pay a monthly subscription fee to avail of the ad-free usage variant of Facebook and Instagram, or to give consent to see personalised ads based on the user’s data. This consent model was introduced in their attempts to comply with the EU’s DMA. However, this was found to be incompatible with the said mandate, according to the EU regulators, as they believed that the users should not only have the option to consent to ads but also have access to less personalised but equivalent alternatives. It is this decision that pushed Meta to come up with less personalised ad options for users in the EU. The less-personalised ad option claims to rely on limited data and show ads that are only based on the context of what is being viewed i.e. during a Facebook or Instagram session requiring a minimum set of data points such as location, age, gender, and the user’s engagement with the ads. However, choosing this option also allows for such ads to be less skippable. 

The EU’s Digital Markets Act came into force on November 1, 2022. The purpose was to make the digital marketing sector fairer and in doing so, identify what they consider to be “Gatekeepers” (core platform services such as messenger services, search engines, and app stores) and a list of do’s and don’ts for them. One of them, applicable to the case mentioned above, is the effective consent required by the user in case the gatekeeper decides to target advertisements enabled by tracking the users' activity outside the gatekeeper's core platform services. 

The Indian Context

Although no such issues have been raised in India yet, it is imperative to know that in the Indian context, the DPDP (Digital Personal Data Protection) Act 2023 governs personal data regulation. This includes rules for Data Fiduciaries (those who, alone or in partnership with others, determine the means and purpose of processing personal data), the Data Principal (those who give data), Consent Managers, and even rules regarding processing data of children. 

CyberPeace Recommendations:

At the level of the user, one can take steps to ensure limited collection of personal data by following the mentioned steps: 

• Review Privacy Settings- Reviewing Privacy settings for one’s online accounts and devices is a healthy practice to avoid giving unnecessary information to third-party applications.
• Private Browsing- Browsing through private mode or incognito is encouraged, as it prevents websites from tracking your activity and personal data.
• Using Ad-blockers- Certain websites have a user option to block ads when the user first visits their page. Availing of this prevents spam advertisements from the respective websites.
• Using VPN- Using Virtual Private Networks enables users to hide their IP address and their data to be encrypted, preventing third-party actors from tracking the users' online activities
• Other steps include clearing cookies and cache data and using the location-sharing feature with care.

Conclusion

Meta’s compliance with the EU’s DMA signals that social media platforms cannot circumnavigate their way around rules. Balancing the services provided while respecting user privacy is of the utmost importance. The EU has set precedence for a system that respects this and can be used as an example to help set guidelines for how other countries can continue to deal with similar issues and set standards accordingly.

References

• https://indianexpress.com/article/technology/tech-news-technology/meta-less-personalised-ads-eu-regulatory-demands-9667266/
• https://rainmaker.co.in/blog/view/the-price-of-personalization-how-targeted-advertising-breaches-data-privacy-and-challenges-the-gdprs-shield
• https://www.infosecurity-magazine.com/magazine-features/fines-data-protection-violations/
• https://www.forbes.com/councils/forbestechcouncil/2023/09/01/the-landscape-of-personalized-advertising-efficiency-versus-privacy/
• https://iapp.org/news/a/pay-or-consent-personalized-ads-the-rules-and-whats-next
• https://economictimes.indiatimes.com/news/how-to/how-to-safeguard-privacy-in-the-era-of-personalised-ads/articleshow/102748711.cms?from=mdr
• https://www.business-standard.com/technology/tech-news/facebook-instagram-users-in-europe-can-opt-for-less-personalised-ads-124111201558_1.html
• https://digital-markets-act.ec.europa.eu/about-dma_en

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Executive Summary

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A photo allegedly showing injuries to Seema Haider has been widely circulated on social media. Users are claiming that her husband Sachin Meena assaulted her. However, a fact-check by CyberPeace Research Wing  has found the claim to be false. The research reveals that the viral image is AI-generated and is being shared with a misleading narrative.

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Claim

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On X (formerly Twitter), a user shared the image on May 14, 2026, claiming that Sachin Meena assaulted Seema Haider.

• https://x.com/AarunKosli/status/2054873311612752053 
• https://archive.ph/N5IRl 

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Fact Check

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A keyword-based search on Google did not return any credible media reports supporting the viral claim.

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A closer visual examination of the image raised suspicions of AI generation. The image was first analysed using the AI detection tool Hive Moderation, which indicated a 99% probability that the image is AI-generated.

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Further analysis using another AI detection tool, Sightengine, also produced similar results, confirming a 99% likelihood that the image was generated using AI tools.

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Conclusion

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The viral image is AI-generated and misleading. The claim that Sachin Meena assaulted Seema Haider is false and has no factual basis.

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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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