#FactCheck - Viral Video of Argentina Football Team Dancing to Bhojpuri Song is Misleading

Introduction

The spread of misinformation online has become a significant concern, with far-reaching social, political, economic and personal implications. The degree of vulnerability to misinformation differs from person to person, dependent on psychological elements such as personality traits, familial background and digital literacy combined with contextual factors like information source, repetition, emotional content and topic. How to reduce misinformation susceptibility in real-world environments where misinformation is regularly consumed on social media remains an open question. Inoculation theory has been proposed as a way to reduce susceptibility to misinformation by informing people about how they might be misinformed. Psychological inoculation campaigns on social media are effective at improving misinformation resilience at scale.

Prebunking has gained prominence as a means to preemptively build resilience against anticipated exposure to misinformation. This approach, grounded in Inoculation Theory, allows people to analyse and avoid manipulation without prior knowledge of specific misleading content by helping them build generalised resilience. We may draw a parallel here with broad spectrum antibiotics that can be used to fight infections and protect the body against symptoms before one is able to identify the particular pathogen at play.

Inoculation Theory and Prebunking

Inoculation theory is a promising approach to combat misinformation in the digital age. It involves exposing individuals to weakened forms of misinformation before encountering the actual false information. This helps develop resistance and critical thinking skills to identify and counter deceptive content. 

Inoculation theory has been established as a robust framework for countering unwanted persuasion and can be applied within the modern context of online misinformation:

1. Preemptive Inoculation:  Preemptive inoculation entails exposing people to weaker kinds of misinformation before they encounter genuine erroneous information. Individuals can build resistance and critical thinking abilities by being exposed to typical misinformation methods and strategies.
2. Technique/logic based Inoculation: Individuals can educate themselves about typical manipulative strategies used in online misinformation, which could be emotionally manipulative language, conspiratorial reasoning, trolling and logical fallacies. Learning to recognise these tactics as indicators of misinformation is an important first step to being able to recognise and reject the same. Through logical reasoning, individuals can recognize such tactics for what they are: attempts to distort the facts or spread misleading information. Individuals who are equipped with the capacity to discern weak arguments and misleading methods may properly evaluate the reliability and validity of information they encounter on the Internet.
3. Educational Campaigns: Educational initiatives that increase awareness about misinformation, its consequences, and the tactics used to manipulate information can be useful inoculation tools. These programmes equip individuals with the knowledge and resources they need to distinguish between reputable and fraudulent sources, allowing them to navigate the online information landscape more successfully.
4. Interactive Games and Simulations: Online games and simulations, such as ‘Bad News,’ have been created as interactive aids to protect people from misinformation methods. These games immerse users in a virtual world where they may learn about the creation and spread of misinformation, increasing their awareness and critical thinking abilities.
5. Joint Efforts: Combining inoculation tactics with other anti-misinformation initiatives, such as accuracy primes, building resilience on social media platforms, and media literacy programmes, can improve the overall efficacy of our attempts to combat misinformation. Expert organisations and people can build a stronger defence against the spread of misleading information by using many actions at the same time.

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CyberPeace Policy Recommendations for Tech/Social Media Platforms

Implementation of the Inoculation Theory on social media platforms can be seen as an effective strategy point for building resilience among users and combating misinformation. Tech/social media platforms can develop interactive and engaging content in the form of educational prebunking videos, short animations, infographics, tip sheets, and misinformation simulations. These techniques can be deployed through online games, collaborations with influencers and trusted sources that help design and deploy targeted campaigns whilst also educating netizens about the usefulness of Inoculation Theory so that they can practice critical thinking. 

The approach will inspire self-monitoring amongst netizens so that people consume information mindfully. It is a powerful tool in the battle against misinformation because it not only seeks to prevent harm before it occurs, but also actively empowers the target audience. In other words, Inoculation Theory helps build people up, and takes them on a journey of transformation from ‘potential victim’ to ‘warrior’ in the battle against misinformation. Through awareness-building, this approach makes people more aware of their own vulnerabilities and attempts to exploit them so that they can be on the lookout while they read, watch, share and believe the content they receive online.

Widespread adoption of Inoculation Theory may well inspire systemic and technological change that goes beyond individual empowerment: these interventions on social media platforms can be utilized to advance digital tools and algorithms so that such interventions and their impact are amplified. Additionally, social media platforms can explore personalized inoculation strategies, and customized inoculation approaches for different audiences so as to be able to better serve more people. One such elegant solution for social media platforms can be to develop a dedicated prebunking strategy that identifies and targets specific themes and topics that could be potential vectors for misinformation and disinformation. This will come in handy, especially during sensitive and special times such as the ongoing elections where tools and strategies for ‘Election Prebunks’ could be transformational.

Conclusion

Applying Inoculation Theory in the modern context of misinformation can be an effective method of establishing resilience against misinformation, help in developing critical thinking and empower individuals to discern fact from fiction in the digital information landscape. The need of the hour is to prioritize extensive awareness campaigns that encourage critical thinking, educate people about manipulation tactics, and pre-emptively counter false narratives associated with information. Inoculation strategies can help people to build mental amour or mental defenses against malicious content and malintent that they may encounter in the future by learning about it in advance. As they say, forewarned is forearmed. 

References

• https://www.science.org/doi/10.1126/sciadv.abo6254
• https://stratcomcoe.org/publications/download/Inoculation-theory-and-Misinformation-FINAL-digital-ISBN-ebbe8.pdf

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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/

Executive Summary:

A manipulated image showing someone making an offensive gesture towards Prime Minister Narendra Modi is circulating on social media. However, the original photo does not display any such behavior towards the Prime Minister. The CyberPeace Research Team conducted an analysis and found that the genuine image was published in a Hindustan Times article in May 2019, where no rude gesture was visible. A comparison of the viral and authentic images clearly shows the manipulation. Moreover, The Hitavada also published the same image in 2019. Further investigation revealed that ABPLive also had the image.

Claims:

A picture showing an individual making a derogatory gesture towards Prime Minister Narendra Modi is being widely shared across social media platforms.

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Fact Check:

Upon receiving the news, we immediately ran a reverse search of the image and found an article by Hindustan Times, where a similar photo was posted but there was no sign of such obscene gestures shown towards PM Modi.

ABP Live and The Hitavada also have the same image published on their website in May 2019. 

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Comparing both the viral photo and the photo found on official news websites, we found that almost everything resembles each other except the derogatory sign claimed in the viral image.

With this, we have found that someone took the original image, published in May 2019, and edited it with a disrespectful hand gesture, and which has recently gone viral across social media and has no connection with reality.

Conclusion:

In conclusion, a manipulated picture circulating online showing someone making a rude gesture towards Prime Minister Narendra Modi has been debunked by the Cyberpeace Research team. The viral image is just an edited version of the original image published in 2019. This demonstrates the need for all social media users to check/ verify the information and facts before sharing, to prevent the spread of fake content. Hence the viral image is fake and Misleading.

• Claim: A picture shows someone making a rude gesture towards Prime Minister Narendra Modi
• Claimed on: X, Instagram
• Fact Check: Fake & Misleading

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