#Fact Check: Old Photo Misused to Claim Israeli Helicopter Downed in Lebanon!

The European Union (EU) has made trailblazing efforts regarding protection and privacy, coming up with the most comprehensive and detailed regulation called the GDPR (General Data Protection Regulation). As countries worldwide continue to grapple with setting their laws, the EU is already taking on issues with tech giants and focusing on the road ahead. Its contentious issues with Meta and the launch of Meta’s AI assistant in the EU are thus seen as a complex process, shaped by stringent data privacy regulations, ongoing debates over copyright, and ethical AI practices.​ This development is considered important as previously, the EU and Meta have had issues (including fines and and also received a pushback concerning its services), which broadly include data privacy regarding compliance with GDPR, antitrust law concerns- targeting ads, facebook marketplace activities and content moderation with respect to the spread of misinformation. 

Privacy and Data Protection Concerns

A significant part of operating Large Language Models (LLMs) is the need to train them with a repository of data/ plausible answers from which they can source. If it doesn’t find relevant information or the request is out of its scope, programmed to answer, it shall continue to follow orders, but with a reduction in the accuracy of its response. Meta's initial plans to train its AI models using publicly available content from adult users in the EU received a setback from privacy regulators. The Irish Data Protection Commission (DPC), acting as Meta's lead privacy regulator in Europe, raised the issue and requested a delay in the rollout to assess its compliance with GDPR. It has also raised similar concerns with Grok, the AI tool of X, to assess whether the EU users’  data was lawfully processed for training it. 

In response, Meta stalled the release of this feature for around a year and agreed to exclude private messages and data from users under the age of 18 and implemented an opt-out mechanism for users who do not wish their public data to be used for AI training. This approach aligns with GDPR requirements, which mandate a clear legal basis for processing personal data, such as obtaining explicit consent or demonstrating legitimate interest, along with the option of removal of consent at a later stage, as the user wishes. The version/service available at the moment is a text-based assistant which is not capable of things like image generation, but can provide services and assistance which include brainstorming, planning, and answering queries from web-based information. However, Meta has assured its users of expansion and exploration regarding the AI features in the near future as it continues to cooperate with the regulators.

Regulatory Environment and Strategic Decisions

The EU's regulatory landscape, characterised by the GDPR and the forthcoming AI Act, presents challenges for tech companies like Meta. Citing the "unpredictable nature" of EU regulations, Meta has decided not to release its multimodal Llama AI model—capable of processing text, images, audio, and video—in the EU. This decision underscores the tension between innovation and regulatory compliance, as companies navigate the complexities of deploying advanced AI technologies within strict legal frameworks. 

Implications and Future Outlook

Meta's experience highlights the broader challenges faced by AI developers operating in jurisdictions with robust data protection laws. The most critical issue that remains for now is to strike a balance  between  leveraging user data for AI advancement while respecting individual privacy rights.. As the EU continues to refine its regulatory approach to AI, companies need to adapt their strategies to ensure compliance while fostering innovation.​ Stringent measures and regular assessment also keep in check the accountability of big tech companies as they make for profit as well as for the public.

Reference:

• https://thehackernews.com/2025/04/meta-resumes-eu-ai-training-using.html
• https://www.thehindu.com/sci-tech/technology/meta-to-train-ai-models-on-european-users-public-data/article69451271.ece
• https://about.fb.com/news/2025/04/making-ai-work-harder-for-europeans/ 
• https://www.theregister.com/2025/04/15/meta_resume_ai_training_eu_user_posts/ 
• https://noyb.eu/en/twitters-ai-plans-hit-9-more-gdpr-complaints 

• https://www.businesstoday.in/technology/news/story/meta-ai-finally-comes-to-europe-after-a-year-long-delay-but-with-some-limitations-468809-2025-03-21
• https://www.bloomberg.com/news/articles/2025-02-13/meta-opens-facebook-marketplace-to-rivals-in-eu-antitrust-clash
• https://www.nytimes.com/2023/05/22/business/meta-facebook-eu-privacy-fine.html#:~:text=Many%20civil%20society%20groups%20and,million%20for%20a%20data%20leak.
• https://ec.europa.eu/commission/presscorner/detail/en/ip_24_5801
• https://www.thehindu.com/sci-tech/technology/european-union-accuses-facebook-owner-meta-of-breaking-digital-rules-with-paid-ad-free-option/article68358039.ece
• https://www.theregister.com/2025/04/14/ireland_investigation_into_x/ 
• https://www.theverge.com/2024/7/18/24201041/meta-multimodal-llama-ai-model-launch-eu-regulations?utm_source=chatgpt.com
• https://www.axios.com/2024/07/17/meta-future-multimodal-ai-models-eu?utm_source=chatgpt.com 

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Introduction

The fast-paced development of technology and the wider use of social media platforms have led to the rapid dissemination of misinformation with characteristics such as diffusion, fast propagation speed, wide influence, and deep impact through these platforms. Social Media Algorithms and their decisions are often perceived as a black box introduction that makes it impossible for users to understand and recognise how the decision-making process works. 

Social media algorithms may unintentionally promote false narratives that garner more interactions, further reinforcing the misinformation cycle and making it harder to control its spread within vast, interconnected networks. Algorithms judge the content based on the metrics, which is user engagement. It is the prerequisite for algorithms to serve you the best. Hence, algorithms or search engines enlist relevant items you are more likely to enjoy. This process, initially, was created to cut the clutter and provide you with the best information. However, sometimes it results in unknowingly widespread misinformation due to the viral nature of information and user interactions. 

Analysing the Algorithmic Architecture of Misinformation

Social media algorithms, designed to maximize user engagement, can inadvertently promote misinformation due to their tendency to trigger strong emotions, creating echo chambers and filter bubbles. These algorithms prioritize content based on user behaviour, leading to the promotion of emotionally charged misinformation. Additionally, the algorithms prioritize content that has the potential to go viral, which can lead to the spread of false or misleading content faster than corrections or factual content.

Additionally, popular content is amplified by platforms, which spreads it faster by presenting it to more users. Limited fact-checking efforts are particularly difficult since, by the time they are reported or corrected, erroneous claims may have gained widespread acceptance due to delayed responses. Social media algorithms find it difficult to distinguish between real people and organized networks of troll farms or bots that propagate false information. This creates a vicious loop where users are constantly exposed to inaccurate or misleading material, which strengthens their convictions and disseminates erroneous information through networks.

Though algorithms, primarily, aim to enhance user engagement by curating content that aligns with the user's previous behaviour and preferences. Sometimes this process leads to "echo chambers," where individuals are exposed mainly to information that reaffirms their beliefs which existed prior, effectively silencing dissenting voices and opposing viewpoints. This curated experience reduces exposure to diverse opinions and amplifies biased and polarising content, making it arduous for users to discern credible information from misinformation. Algorithms feed into a feedback loop that continuously gathers data from users' activities across digital platforms, including websites, social media, and apps. This data is analysed to optimise user experiences, making platforms more attractive. While this process drives innovation and improves user satisfaction from a business standpoint, it also poses a danger in the context of misinformation. The repetitive reinforcement of user preferences leads to the entrenchment of false beliefs, as users are less likely to encounter fact-checks or corrective information.

Moreover, social networks and their sheer size and complexity today exacerbate the issue. With billions of users participating in online spaces, misinformation spreads rapidly, and attempting to contain it—such as by inspecting messages or URLs for false information—can be computationally challenging and inefficient. The extensive amount of content that is shared daily means that misinformation can be propagated far quicker than it can get fact-checked or debunked.

Understanding how algorithms influence user behaviour is important to tackling misinformation. The personalisation of content, feedback loops, the complexity of network structures, and the role of superspreaders all work together to create a challenging environment where misinformation thrives. Hence, highlighting the importance of countering misinformation through robust measures. 

The Role of Regulations in Curbing Algorithmic Misinformation

The EU's Digital Services Act (DSA) applicable in the EU is one of the regulations that aims to increase the responsibilities of tech companies and ensure that their algorithms do not promote harmful content. These regulatory frameworks play an important role they can be used to establish mechanisms for users to appeal against the algorithmic decisions and ensure that these systems do not disproportionately suppress legitimate voices. Independent oversight and periodic audits can ensure that algorithms are not biased or used maliciously. Self-regulation and Platform regulation are the first steps that can be taken to regulate misinformation. By fostering a more transparent and accountable ecosystem, regulations help mitigate the negative effects of algorithmic misinformation, thereby protecting the integrity of information that is shared online. In the Indian context, the Intermediary Guidelines, 2023, Rule 3(1)(b)(v) explicitly prohibits the dissemination of misinformation on digital platforms. The ‘Intermediaries’ are obliged to ensure reasonable efforts to prevent users from hosting, displaying, uploading, modifying, publishing, transmitting, storing, updating, or sharing any information related to the 11 listed user harms or prohibited content. This rule aims to ensure platforms identify and swiftly remove misinformation, and false or misleading content.

Cyberpeace Outlook

Understanding how algorithms prioritise content will enable users to critically evaluate the information they encounter and recognise potential biases. Such cognitive defenses can empower individuals to question the sources of the information and report misleading content effectively. In the future of algorithms in information moderation, platforms should evolve toward more transparent, user-driven systems where algorithms are optimised not just for engagement but for accuracy and fairness. Incorporating advanced AI moderation tools, coupled with human oversight can improve the detection and reduction of harmful and misleading content. Collaboration between regulatory bodies, tech companies, and users will help shape the algorithms landscape to promote a healthier, more informed digital environment.

References:

• https://www.advancedsciencenews.com/misformation-spreads-like-a-nuclear-reaction-on-the-internet/
• https://www.niemanlab.org/2024/09/want-to-fight-misinformation-teach-people-how-algorithms-work/ 
• Press Release: Press Information Bureau (pib.gov.in) 

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Introduction 

India envisions reaching its goal of becoming Viksit Bharat by 2047. With a net-zero emissions target by 2070, it has already reduced GDP emission intensity by 36% (from 2005 to 2020) and is working towards a 45% reduction goal by 2030. This will help the country achieve economic growth while minimizing environmental impact, ensuring sustainable development for the future. The 2025 Union Budget prioritises energy security, clean energy expansion, and green tech manufacturing. Furthermore, India’s promotion of sustainability policies in startups, MSMEs, and clean tech shows its commitment to COP28 and SDGs. India’s key policy developments for sustainability and energy efficiency include the Energy Conservation Act (2022), PAT scheme, S&L scheme, and the Energy Conservation Building Code, driving decarbonization, energy efficiency, and a sustainable future.

India’s Policy and Regulatory Landscape 

The Indian law of Energy Conservation (Amendment) Act which was enacted in 2022 aims at enhancing energy efficiency while ensuring economic growth. It works on the aim of reducing emission intensity by 2030. The Act tackles regulatory, financial, and awareness barriers to promote energy-saving technologies. Next, the Perform, Achieve, and Trade (PAT) Scheme improves cost-effective energy efficiency in energy-intensive industries through tradable energy-saving certificates. Adding on, the PLI Scheme boosts green manufacturing by attracting investments, both domestically and internationally. The Bureau of Energy Efficiency (BEE) enforces Minimum Energy Performance Standards (MEPS) and star ratings for appliances, guiding consumers toward energy-efficient choices. These initiatives collectively drive carbon reduction and sustainable energy use in India.

Growth of Energy-Efficient Technologies 

India has been making massive strides in its integration of renewable energy, such as solar and wind energies, mainly due to improvements in storage technologies. Another key development is the real-time optimization of energy usage through smart grids and AI-driven energy management. The EV and green mobility boom has been charged through by the rapid expansion of charging infrastructure and the policy interventions to support the shift. The building of green building codes and IoT-driven energy management has led to building efficiency, and finally, the efforts for industrial energy optimisation have been met through AI/ML-driven demand-side management in heavy industries. 

Market Trends, Investment, and Industry Adoption 

The World Energy Investment Report 2024 (IEA) projects global energy investment to surpass $3 trillion, with $2 trillion allocated to clean energy. India’s clean energy investment reached $68 billion in 2023, a 40%+ rise from 2016-2020, with nearly 50% directed toward low-emission power, including solar PV. Investment is set to double by 2030 but needs a 20% further rise to meet climate goals.

India’s ESG push is driven by Net Zero 2070, SEBI’s BRSR mandates, and UN SDGs, with rising scrutiny on corporate governance. ESG-aligned investments are expanding, reinforcing sustainability. Meanwhile, energy efficiency in manufacturing minimizes waste and environmental impact, while digital transformation in energy management boosts renewable integration, grid reliability, and cost efficiency, ensuring a sustainable energy transition.

The Way Forward 

There are multiple implementation bottlenecks present for the active policies which include infrastructure paucity, financing issues and even the on-ground implementational challenges of the active policies. To combat these issues India needs to adopt measures for promoting public-private partnerships to scale energy-efficient solutions. Incentives for industries to adopt green technologies should be strengthened (tax exemptions and subsidies for specific periods), with increased R&D support and regulatory sandboxes to encourage adoption. Finally, the role of industries, policymakers and consumers needs to be in tandem to accelerate the efforts made towards a sustainable and green future for India. Emerging technologies play an important in bridging gaps and aim towards the adoption of global best practices for India.

References

• https://instituteofsustainabilitystudies.com/insights/lexicon/green-technologies-innovations-opportunities-challenges/
• https://powermin.gov.in/sites/default/files/The_Energy_Conservation_Amendment_Act_2022_0.pdf‍
• https://www.ibef.org/blogs/esg-investing-in-india-navigating-environmental-social-and-governance-factors-for-sustainable-growth