#FactCheck: Viral video of Unrest in Kenya is being falsely linked with J&K

Overview:

In today’s digital landscape, safeguarding personal data and communications is more crucial than ever. WhatsApp, as one of the world’s leading messaging platforms, consistently enhances its security features to protect user interactions, offering a seamless and private messaging experience

App Lock: Secure Access with Biometric Authentication

To fortify security at the device level, WhatsApp offers an app lock feature, enabling users to protect their app with biometric authentication such as fingerprint or Face ID. This feature ensures that only authorized users can access the app, adding an additional layer of protection to private conversations.

How to Enable App Lock:

1. Open WhatsApp and navigate to Settings.
2. Select Privacy.
3. Scroll down and tap App Lock.
4. Activate Fingerprint Lock or Face ID and follow the on-screen instructions.

Chat Lock: Restrict Access to Private Conversations

WhatsApp allows users to lock specific chats, moving them to a secured folder that requires biometric authentication or a passcode for access. This feature is ideal for safeguarding sensitive conversations from unauthorized viewing.

How to Lock a Chat:

1. Open WhatsApp and select the chat to be locked.
2. Tap on the three dots (Android) or More Options (iPhone).
3. Select Lock Chat
4. Enable the lock using Fingerprint or Face ID.

Privacy Checkup: Strengthening Security Preferences

The privacy checkup tool assists users in reviewing and customizing essential security settings. It provides guidance on adjusting visibility preferences, call security, and blocked contacts, ensuring a personalized and secure communication experience.

How to Run Privacy Checkup:

1. Open WhatsApp and navigate to Settings.
2. Tap Privacy.
3. Select Privacy Checkup and follow the prompts to adjust settings.

Automatic Blocking of Unknown Accounts and Messages

To combat spam and potential security threats, WhatsApp automatically restricts unknown accounts that send excessive messages. Users can also manually block or report suspicious contacts to further enhance security.

How to Manage Blocking of Unknown Accounts:

1. Open WhatsApp and go to Settings.
2. Select Privacy.
3. Tap to Advanced 
4. Enable Block unknown account messages

IP Address Protection in Calls

To prevent tracking and enhance privacy, WhatsApp provides an option to hide IP addresses during calls. When enabled, calls are routed through WhatsApp’s servers, preventing location exposure via direct connections.

How to Enable IP Address Protection in Calls:

1. Open WhatsApp and go to Settings.
2. Select Privacy, then tap Advanced.
3. Enable Protect IP Address in Calls. 

Disappearing Messages: Auto-Deleting Conversations

Disappearing messages help maintain confidentiality by automatically deleting sent messages after a predefined period—24 hours, 7 days, or 90 days. This feature is particularly beneficial for reducing digital footprints.

How to Enable Disappearing Messages:

1. Open the chat and tap the Chat Name.
2. Select Disappearing Messages.
3. Choose the preferred duration before messages disappear.

View Once: One-Time Access to Media Files

The ‘View Once’ feature ensures that shared photos and videos can only be viewed a single time before being automatically deleted, reducing the risk of unauthorized storage or redistribution.

How to Send View Once Media:

1. Open a chat and tap the attachment icon.
2. Choose Camera or Gallery to select media.
3. Tap the ‘1’ icon before sending the media file.

Group Privacy Controls: Manage Who Can Add You

WhatsApp provides users with the ability to control group invitations, preventing unwanted additions by unknown individuals. Users can restrict group invitations to ‘Everyone,’ ‘My Contacts,’ or ‘My Contacts Except…’ for enhanced privacy.

How to Adjust Group Privacy Settings:

1. Open WhatsApp and go to Settings.
2. Select Privacy and tap Groups.
3. Choose from the available options: Everyone, My Contacts, or My Contacts Except

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Conclusion

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WhatsApp continuously enhances its security features to protect user privacy and ensure safe communication. With tools like App Lock, Chat Lock, Privacy Checkup, IP Address Protection, and Disappearing Messages, users can safeguard their data and interactions. Features like View Once and Group Privacy Controls further enhance confidentiality. By enabling these settings, users can maintain a secure and private messaging experience, effectively reducing risks associated with unauthorized access, tracking, and digital footprints. Stay updated and leverage these features for enhanced security.

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Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/

Introduction

A Reuters investigation has uncovered an elephant in the room regarding Meta Platforms' internal measures to address online fraud and illicit advertising. The confidential documents that Reuters reviewed disclosed that Meta was planning to generate approximately 10% of its 2024 revenue, i.e., USD 16 billion, from ads related to scams and prohibited goods. The findings point out a disturbing paradox: on the one hand, Meta is a vocal advocate for digital safety and platform integrity, while on the other hand, the internal logs of the company indicate the existence of a very large area allowing the shunning of fraudulent advertisement activities that exploit users throughout the world. 

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The Scale of the Problem

Internal Meta projections show that its platforms, Facebook, Instagram, and WhatsApp, are displaying a staggering 15 billion scam ads per day combined. The advertisements include deceitful e-commerce promotions, fake investment schemes, counterfeit medical products, and unlicensed gambling platforms.

Meta has developed sophisticated detection tools, but even then, the system does not catch the advertisers until they are 95% certain to be fraudsters. By having at least that threshold for removing an ad, the company is unlikely to lose much money. As a result, instead of turning the fraud adjacent advertisers down, it charges them higher ad rates, which is the strategy they call “penalty bids” internally. 

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Internal Acknowledgements & Business Dependence

Internal documents that date between 2021 and 2025 reveal that the financial, safety, and lobbying divisions of Meta were cognizant of the enormity of revenues generated from scams. One of the 2025 strategic papers even describes this revenue source as "violating revenue," which implies that it includes ads that are against Meta's policies regarding scams, gambling, sexual services, and misleading healthcare products.

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The company's top executives consider the cost-benefit scenario of stricter enforcement. According to a 2024 internal projection, Meta's half-yearly earnings from high-risk scam ads were estimated at USD 3.5 billion, whereas regulatory fines for such violations would not exceed USD 1 billion, thus making it a tolerable trade-off from a commercial viewpoint. At the same time, the company intends to scale down scam ad revenue gradually, thus from 10.1% in 2024 to 7.3% by 2025, and 6% by 2026; however, the documents also reveal a planned slowdown in enforcement to avoid "abrupt reductions" that could affect business forecasts.

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Algorithmic Amplification of Scams

One of the most alarming situations is the fact that Meta's own advertising algorithms amplify scam content. It has been reported that users who click on fraudulent ads are more likely to see other similar ads, as the platform's personalisation engine assumes user "interest."

This scenario creates a self-reinforcing feedback loop where the user engagement with scam content dictates the amount of such content being displayed. Thus, a digital environment is created which encourages deceptive engagement and consequently, user trust is eroded and systemic risk is amplified.

An internal presentation in May 2025 was said to put a number on how deeply the platform's ad ecosystem was intertwined with the global fraud economy, estimating that one-third of the scams that succeeded in the U.S. were due to advertising on Meta's platforms.

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Regulatory & Legal Implications

The disclosures arrived at the same time as the US and UK governments started to closely check the company's activities more than ever before.

• The U.S. Securities and Exchange Commission (SEC) is said to be looking into whether Meta has had any part in the promotion of fraudulent financial ads.
• The UK’s Financial Conduct Authority (FCA) found that Meta’s platforms were the main sources of scams related to online payments and claimed that the amount of money lost was more than all the other social platforms combined in 2023.

Meta’s spokesperson, Andy Stone, at first denied the accusations, stating that the figures mentioned in the leak were “rough and overly-inclusive”; nevertheless, he conceded that the company’s consistent efforts toward enforcement had negatively impacted revenue and would continue to do so.

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Operational Challenges & Policy Gaps

The internal documents also reveal the weaknesses in Meta's day-to-day operations when it comes to the implementation of its own policies.

• Because of the large number of employees laid off in 2023, the whole department that dealt with advertiser-brand impersonation was said to have been dissolved.
• Scam ads were categorised as a "low severity" issue, which was more of a "bad user experience" than a critical security risk.
• At the end of 2023, users were submitting around 100,000 legitimate scam reports per week, of which Meta dismissed or rejected 96%.

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Human Impact: When Fraud Becomes Personal

The financial and ethical issues have tangible human consequences. The Reuters investigation documented multiple cases of individuals defrauded through hijacked Meta accounts.

One striking example involves a Canadian Air Force recruiter, whose hacked Facebook account was used to promote fake cryptocurrency schemes. Despite over a hundred user reports, Meta failed to act for weeks, during which several victims, including military colleagues, lost tens of thousands of dollars.

The case underscores not just platform negligence, but also the difficulty of law enforcement collaboration. Canadian authorities confirmed that funds traced to Nigerian accounts could not be recovered due to jurisdictional barriers, a recurring issue in transnational cyber fraud.

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Ethical and Cybersecurity Implications

The research has questioned extremely important things at least from the perspective of cyber policy:

1. Platform Accountability: Meta, by its practice, is giving more importance to the monetary aspect rather than the truth, and in this way, it is going against the principles of responsible digital governance.
2. Transparency in Ad Ecosystems: The lack of transparency in digital advertising systems makes it very easy for dishonest actors to use automated processes with very little supervision.
3. Algorithmic Responsibility: The use of algorithms that impact the visibility of misleading content and targeting can be considered the direct involvement of the algorithms in the fraud.
4. Regulatory Harmonisation: The presence of different and disconnected enforcement frameworks across jurisdictions is a drawback to the efforts in dealing with cross-border cybercrime.
5. Public Trust: Users’ trust in the digital world is mainly dependent on the safety level they see and the accountability of the companies.

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Conclusion

Meta’s records show a very unpleasant mix of profit, laxity, and failure in the policy area concerning scam-related ads. The platform’s readiness to accept and even profit from fraudulent players, though admitting the damage they cause, calls for an immediate global rethinking of advertising ethics, regulatory enforcement, and algorithmic transparency.

With the expansion of its AI-driven operations and advertising networks, protecting the users of Meta must evolve from being just a public relations goal to being a core business necessity, thus requiring verifiable accountability measures, independent audits, and regulatory oversight. It is an undeniable fact that there are billions of users who count on Meta’s platforms for their right to digital safety, which is why this right must be respected and enforced rather than becoming optional.

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References

• https://www.reuters.com/investigations/meta-is-earning-fortune-deluge-fraudulent-ads-documents-show-2025-11-06/?utm_source=chatgpt.com
• https://www.indiatoday.in/technology/news/story/leaked-docs-claim-meta-made-16-billion-from-scam-ads-even-after-deleting-134-million-of-them-2815183-2025-11-07

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