#FactCheck - Fake Quote Attributed To Manoj Tiwari On Rupee-Dollar Value Goes Viral Online

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/

Executive Summary:

Recently, a viral social media post alleged that the Delhi Metro Rail Corporation Ltd. (DMRC) had increased ticket prices following the BJP’s victory in the Delhi Legislative Assembly elections. After thorough research and verification, we have found this claim to be misleading and entirely baseless. Authorities have asserted that no fare hike has been declared.

Claim:

Viral social media posts have claimed that the Delhi Metro Rail Corporation Ltd. (DMRC) increased metro fares following the BJP's victory in the Delhi Legislative Assembly elections.

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Fact Check:

After thorough research, we conclude that the claims regarding a fare hike by the Delhi Metro Rail Corporation Ltd. (DMRC) following the BJP’s victory in the Delhi Legislative Assembly elections are misleading. Our review of DMRC’s official website and social media handles found no mention of any fare increase.Furthermore, the official X (formerly Twitter) handle of DMRC has also clarified that no such price hike has been announced. We urge the public to rely on verified sources for accurate information and refrain from spreading misinformation.

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Conclusion:

Upon examining the alleged fare hike, it is evident that the increase pertains to Bengaluru, not Delhi. To verify this, we reviewed the official website of Bangalore Metro Rail Corporation Limited (BMRCL) and cross-checked the information with appropriate evidence, including relevant images. Our findings confirm that no fare hike has been announced by the Delhi Metro Rail Corporation Ltd. (DMRC).

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• Claim: Delhi Metro price Hike after BJP’s victory in election 
• Claimed On: X (Formerly Known As Twitter)
• Fact Check: False and Misleading

Introduction

The increasing online interaction and popularity of social media platforms for netizens have made a breeding ground for misinformation generation and spread. Misinformation propagation has become easier and faster on online social media platforms, unlike traditional news media sources like newspapers or TV. The big data analytics and Artificial Intelligence (AI) systems have made it possible to gather, combine, analyse and indefinitely store massive volumes of data. The constant surveillance of digital platforms can help detect and promptly respond to false and misinformation content. 

During the recent Israel-Hamas conflict, there was a lot of misinformation spread on big platforms like X (formerly Twitter) and Telegram. Images and videos were falsely shared attributing to the ongoing conflict, and had spread widespread confusion and tension. While advanced technologies such as AI and big data analytics can help flag harmful content quickly, they must be carefully balanced against privacy concerns to ensure that surveillance practices do not infringe upon individual privacy rights. Ultimately, the challenge lies in creating a system that upholds both public security and personal privacy, fostering trust without compromising on either front.

The Need for Real-Time Misinformation Surveillance

According to a recent survey from the Pew Research Center, 54% of U.S. adults at least sometimes get news on social media. The top spots are taken by Facebook and YouTube respectively with Instagram trailing in as third and TikTok and X as fourth and fifth. Social media platforms provide users with instant connectivity allowing them to share information quickly with other users without requiring the permission of a gatekeeper such as an editor as in the case of traditional media channels. 

Keeping in mind the data dumps that generated misinformation due to the elections that took place in 2024 (more than 100 countries), the public health crisis of COVID-19, the conflicts in the West Bank and Gaza Strip and the sheer volume of information, both true and false, has been immense. Identifying accurate information amid real-time misinformation is challenging. The dilemma emerges as the traditional content moderation techniques may not be sufficient in curbing it. Traditional content moderation alone may be insufficient, hence the call for a dedicated, real-time misinformation surveillance system backed by AI and with certain human sight and also balancing the privacy of user's data, can be proven to be a good mechanism to counter misinformation on much larger platforms. The concerns regarding data privacy need to be prioritized before deploying such technologies on platforms with larger user bases. 

Ethical Concerns Surrounding Surveillance in Misinformation Control

Real-time misinformation surveillance could pose significant ethical risks and privacy risks. Monitoring communication patterns and metadata, or even inspecting private messages, can infringe upon user privacy and restrict their freedom of expression. Furthermore, defining misinformation remains a challenge; overly restrictive surveillance can unintentionally stifle legitimate dissent and alternate perspectives. Beyond these concerns, real-time surveillance mechanisms could be exploited for political, economic, or social objectives unrelated to misinformation control. Establishing clear ethical standards and limitations is essential to ensure that surveillance supports public safety without compromising individual rights. 

In light of these ethical challenges, developing a responsible framework for real-time surveillance is essential.

Balancing Ethics and Efficacy in Real-Time Surveillance: Key Policy Implications

Despite these ethical challenges, a reliable misinformation surveillance system is essential. Key considerations for creating ethical, real-time surveillance may include:

• Misinformation-detection algorithms should be designed with transparency and accountability in mind. Third-party audits and explainable AI can help ensure fairness, avoid biases, and foster trust in monitoring systems.
• Establishing clear, consistent definitions of misinformation is crucial for fair enforcement. These guidelines should carefully differentiate harmful misinformation from protected free speech to respect users’ rights.
• Only collecting necessary data and adopting a consent-based approach which protects user privacy and enhances transparency and trust. It further protects them from stifling dissent and profiling for targeted ads.
• An independent oversight body that can monitor surveillance activities while ensuring accountability and preventing misuse or overreach can be created. These measures, such as the ability to appeal to wrongful content flagging, can increase user confidence in the system.

Conclusion: Striking a Balance

Real-time misinformation surveillance has shown its usefulness in counteracting the rapid spread of false information online. But, it brings complex ethical challenges that cannot be overlooked such as balancing the need for public safety with the preservation of privacy and free expression is essential to maintaining a democratic digital landscape. The references from the EU’s Digital Services Act and Singapore’s POFMA underscore that, while regulation can enhance accountability and transparency, it also risks overreach if not carefully structured. Moving forward, a framework for misinformation monitoring must prioritise transparency, accountability, and user rights, ensuring that algorithms are fair, oversight is independent, and user data is protected. By embedding these safeguards, we can create a system that addresses the threat of misinformation and upholds the foundational values of an open, responsible, and ethical online ecosystem. Balancing ethics and privacy and policy-driven AI Solutions for Real-Time Misinformation Monitoring are the need of the hour.

References

• https://www.pewresearch.org/journalism/fact-sheet/social-media-and-news-fact-sheet/ 
• https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:C:2018:233:FULL 

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