Centre Designates I4C as an Agency to Notify Unlawful Activities in the Cyber World

Executive Summary:

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A viral video circulating on social media platforms, claimed to show the final moments of an Air India flight carrying passengers inside the cabin just before it crashed near Ahmedabad on June 12, 2025, is false. However, upon further research, the footage was found to originate from the Yeti Airlines Flight 691 crash that occurred in Pokhara, Nepal, on January 15, 2023. For all details, please follow the report.

Claim:

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Viral videos circulating on social media claiming to show the final moments inside Air India flight AI‑171 before it crashed near Ahmedabad on June 12, 2025. The footage appears to have been recorded by a passenger during the flight and is being shared as real-time visuals from the recent tragedy. Many users have believed the clip to be genuine and linked it directly to the Air India incident.

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Fact Check:

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To confirm the validity of the video going viral depicting the alleged final moments of Air India's AI-171 that crashed near Ahmedabad on 12 June 2025, we engaged in a comprehensive reverse image search and keyframe analysis then we got to know that the footage occurs back in January 2023, namely Yeti Airlines Flight 691 that crashed in Pokhara, Nepal. The visuals shared in the viral video match up, including cabin and passenger details, identically to the original livestream made by a passenger aboard the Nepal flight, confirming that the video is being reused out of context.

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Moreover, well-respected and reliable news organisations, including New York Post and NDTV, have shared reports confirming that the video originated from the 2023 Nepal plane crash and has no relation to the recent Air India incident. The Press Information Bureau (PIB) also released a clarification dismissing the video as disinformation. Reliable reports from the past, visual evidence, and reverse search verification all provide complete agreement in that the viral video is falsely attributed to the AI-171 tragedy.

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Conclusion:

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The viral footage does not show the AI-171 crash near Ahmedabad on 12 June 2025. It is an irrelevant, previously recorded livestream from the January 2023 Yeti Airlines crash in Pokhara, Nepal, falsely repurposed as breaking news. It’s essential to rely on verified and credible news agencies. Please refer to official investigation reports when discussing such sensitive events.

• Claim: A dramatic clip of passengers inside a crashing plane is being falsely linked to the recent Air India tragedy in Ahmedabad.
• Claimed On: Social Media
• ‍Fact Check: False and Misleading

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AI has grown manifold in the past decade and so has its reliance. A MarketsandMarkets study estimates the AI market to reach $1,339 billion by 2030. Further, Statista reports that ChatGPT amassed more than a million users within the first five days of its release, showcasing its rapid integration into our lives. This development and integration have their risks. Consider this response from Google’s AI chatbot, Gemini to a student’s homework inquiry: “You are not special, you are not important, and you are not needed…Please die.” In other instances, AI has suggested eating rocks for minerals or adding glue to pizza sauce. Such nonsensical outputs are not just absurd; they’re dangerous. They underscore the urgent need to address the risks of unrestrained AI reliance. 

AI’s Rise and Its Limitations

The swiftness of AI’s rise, fueled by OpenAI's GPT series, has revolutionised fields like natural language processing, computer vision, and robotics. Generative AI Models like GPT-3, GPT-4 and GPT-4o with their advanced language understanding, enable learning from data, recognising patterns, predicting outcomes and finally improving through trial and error. However, despite their efficiency, these AI models are not infallible. Some seemingly harmless outputs can spread toxic misinformation or cause harm in critical areas like healthcare or legal advice. These instances underscore the dangers of blindly trusting AI-generated content and highlight the importance and the need to understand its limitations.

Defining the Problem: What Constitutes “Nonsensical Answers”?

Harmless errors due to AI nonsensical responses can be in the form of a wrong answer for a trivia question, whereas, critical failures could be as damaging as wrong legal advice. 

AI algorithms sometimes produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. This response is known as a Nonsensical Answer and the situation is known as an “AI Hallucination”. It can be factual inaccuracies, irrelevant information or even contextually inappropriate responses. 

A significant source of hallucination in machine learning algorithms is the bias in input that it receives. If the inputs for the AI model are full of biased datasets or unrepresentative data, it may lead to the model hallucinating and producing results that reflect these biases. These models are also vulnerable to adversarial attacks, wherein bad actors manipulate the output of an AI model by tweaking the input data ina subtle manner.

The Need for Policy Intervention

Nonsensical AI responses risk eroding user trust and causing harm, highlighting the need for accountability despite AI’s opaque and probabilistic nature. Different jurisdictions address these challenges in varied ways. The EU’s AI Act enforces stringent reliability standards with a risk-based and transparent approach. The U.S. emphasises creating ethical guidelines and industry-driven standards. India’s DPDP Act indirectly tackles AI safety through data protection, focusing on the principles of accountability and consent. While the EU prioritises compliance, the U.S. and India balance innovation with safeguards. This reflects on the diverse approaches that nations have to AI regulation.

Where Do We Draw the Line?

The critical question is whether AI policies should demand perfection or accept a reasonable margin for error. Striving for flawless AI responses may be impractical, but a well-defined framework can balance innovation and accountability. Adopting these simple measures can lead to the creation of an ecosystem where AI develops responsibly while minimising the societal risks it can pose. Key measures to achieve this include:

• Ensure that users are informed about AI and its capabilities and limitations. Transparent communication is the key to this.  
• Implement regular audits and rigorous quality checks to maintain high standards. This will in turn prevent any form of lapses.
• Establishing robust liability mechanisms to address any harms caused by AI-generated material which is in the form of misinformation. This fosters trust and accountability.

CyberPeace Key Takeaways: Balancing Innovation with Responsibility

The rapid growth in AI development offers immense opportunities but this must be done responsibly. Overregulation of AI can stifle innovation, on the other hand, being lax could lead to unintended societal harm or disruptions. 

Maintaining a balanced approach to development is essential. Collaboration between stakeholders such as governments, academia, and the private sector is important. They can ensure the establishment of guidelines, promote transparency, and create liability mechanisms. Regular audits and promoting user education can build trust in AI systems. Furthermore, policymakers need to prioritise user safety and trust without hindering creativity while making regulatory policies. 

We can create a future that is AI-development-driven and benefits us all by fostering ethical AI development and enabling innovation. Striking this balance will ensure AI remains a tool for progress, underpinned by safety, reliability, and human values.

References

• https://timesofindia.indiatimes.com/technology/tech-news/googles-ai-chatbot-tells-student-you-are-not-needed-please-die/articleshow/115343886.cms
• https://www.forbes.com/advisor/business/ai-statistics/#2 
• https://www.reuters.com/legal/legalindustry/artificial-intelligence-trade-secrets-2023-12-11/ ‍
• https://www.indiatoday.in/technology/news/story/chatgpt-has-gone-mad-today-openai-says-it-is-investigating-reports-of-unexpected-responses-2505070-2024-02-21

Introduction

In an era when misinformation spreads like wildfire across the digital landscape, the need for effective strategies to counteract these challenges has grown exponentially in a very short period. Prebunking and Debunking are two approaches for countering the growing spread of misinformation online. Prebunking empowers individuals by teaching them to discern between true and false information and acts as a protective layer that comes into play even before people encounter malicious content. Debunking is the correction of false or misleading claims after exposure, aiming to undo or reverse the effects of a particular piece of misinformation. Debunking includes methods such as fact-checking, algorithmic correction on a platform, social correction by an individual or group of online peers, or fact-checking reports by expert organisations or journalists. An integrated approach which involves both strategies can be effective in countering the rapid spread of misinformation online.

Brief Analysis of Prebunking 

Prebunking is a proactive practice that seeks to rebut erroneous information before it spreads. The goal is to train people to critically analyse information and develop ‘cognitive immunity’ so that they are less likely to be misled when they do encounter misinformation. 

The Prebunking approach, grounded in Inoculation theory, teaches people to recognise, analyse and avoid manipulation and misleading content so that they build resilience against the same. Inoculation theory, a social psychology framework, suggests that pre-emptively conferring psychological resistance against malicious persuasion attempts can reduce susceptibility to misinformation across cultures. As the term suggests, the MO is to help the mind in the present develop resistance to influence that it may encounter in the future. Just as medical vaccines or inoculations help the body build resistance to future infections by administering weakened doses of the harm agent, inoculation theory seeks to teach people fact from fiction through exposure to examples of weak, dichotomous arguments, manipulation tactics like emotionally charged language, case studies that draw parallels between truths and distortions, and so on. In showing people the difference, inoculation theory teaches them to be on the lookout for misinformation and manipulation even, or especially, when they least expect it. 

The core difference between Prebunking and Debunking is that while the former is preventative and seeks to provide a broad-spectrum cover against misinformation, the latter is reactive and focuses on specific instances of misinformation. While Debunking is closely tied to fact-checking, Prebunking is tied to a wider range of specific interventions, some of which increase motivation to be vigilant against misinformation and others increase the ability to engage in vigilance with success.

There is much to be said in favour of the Prebunking approach because these interventions build the capacity to identify misinformation and recognise red flags However, their success in practice may vary. It might be difficult to scale up Prebunking efforts and ensure their reach to a larger audience. Sustainability is critical in ensuring that Prebunking measures maintain their impact over time. Continuous reinforcement and reminders may be required to ensure that individuals retain the skills and information they gained from the Prebunking training activities. Misinformation tactics and strategies are always evolving, so it is critical that Prebunking interventions are also flexible and agile and respond promptly to developing challenges. This may be easier said than done, but with new misinformation and cyber threats developing frequently, it is a challenge that has to be addressed for Prebunking to be a successful long-term solution. 

Encouraging people to be actively cautious while interacting with information, acquire critical thinking abilities, and reject the effect of misinformation requires a significant behavioural change over a relatively short period of time. Overcoming ingrained habits and prejudices, and countering a natural reluctance to change is no mean feat. Developing a widespread culture of information literacy requires years of social conditioning and unlearning and may pose a significant challenge to the effectiveness of Prebunking interventions. 

Brief Analysis of Debunking 

Debunking is a technique for identifying and informing people that certain news items or information are incorrect or misleading. It seeks to lessen the impact of misinformation that has already spread. The most popular kind of Debunking occurs through collaboration between fact-checking organisations and social media businesses. Journalists or other fact-checkers discover inaccurate or misleading material, and social media platforms flag or label it. Debunking is an important strategy for curtailing the spread of misinformation and promoting accuracy in the digital information ecosystem. 

Debunking interventions are crucial in combating misinformation. However, there are certain challenges associated with the same. Debunking misinformation entails critically verifying facts and promoting corrected information. However, this is difficult owing to the rising complexity of modern tools used to generate narratives that combine truth and untruth, views and facts. These advanced approaches, which include emotional spectrum elements, deepfakes, audiovisual material, and pervasive trolling, necessitate a sophisticated reaction at all levels: technological, organisational, and cultural.

Furthermore, It is impossible to debunk all misinformation at any given time, which effectively means that it is impossible to protect everyone at all times, which means that at least some innocent netizens will fall victim to manipulation despite our best efforts.  Debunking is inherently reactive in nature, addressing misinformation after it has grown extensively. This reactionary method may be less successful than proactive strategies such as Prebunking from the perspective of total harm done. Misinformation producers operate swiftly and unexpectedly, making it difficult for fact-checkers to keep up with the rapid dissemination of erroneous or misleading information. Debunking may need continuous exposure to fact-check to prevent erroneous beliefs from forming, implying that a single Debunking may not be enough to rectify misinformation.  Debunking requires time and resources, and it is not possible to disprove every piece of misinformation that circulates at any particular moment. This constraint may cause certain misinformation to go unchecked, perhaps leading to unexpected effects. The misinformation on social media can be quickly spread and may become viral faster than Debunking pieces or articles. This leads to a situation in which misinformation spreads like a virus, while the antidote to debunked facts struggles to catch up.

Prebunking vs Debunking: Comparative Analysis

Prebunking interventions seek to educate people to recognise and reject misinformation before they are exposed to actual manipulation. Prebunking offers tactics for critical examination, lessening the individuals' susceptibility to misinformation in a variety of contexts. On the other hand, Debunking interventions involve correcting specific false claims after they have been circulated. While Debunking can address individual instances of misinformation, its impact on reducing overall reliance on misinformation may be limited by the reactive nature of the approach.

CyberPeace Policy Recommendations for Tech/Social Media Platforms

With the rising threat of online misinformation, tech/social media platforms can adopt an integrated strategy that includes both Prebunking and Debunking initiatives to be deployed and supported on all platforms to empower users to recognise the manipulative messaging through Prebunking and be aware of the accuracy of misinformation through Debunking interventions. 

1. Gamified Inoculation: Tech/social media companies can encourage gamified inoculation campaigns, which is a competence-oriented approach to Prebunking misinformation. This can be effective in helping people immunise the receiver against subsequent exposures. It can empower people to build competencies to detect misinformation through gamified interventions. 
2. Promotion of Prebunking and Debunking Campaigns through Algorithm Mechanisms: Tech/social media platforms may promote and guarantee that algorithms prioritise the distribution of Prebunking materials to users, boosting educational content that strengthens resistance to misinformation. Platform operators should incorporate algorithms that prioritise the visibility of Debunking content in order to combat the spread of erroneous information and deliver proper corrections; this can eventually address and aid in Prebunking and Debunking methods to reach a bigger or targeted audience.
3. User Empowerment to Counter Misinformation: Tech/social media platforms can design user-friendly interfaces that allow people to access Prebunking materials, quizzes, and instructional information to help them improve their critical thinking abilities. Furthermore, they can incorporate simple reporting tools for flagging misinformation, as well as links to fact-checking resources and corrections.
4. Partnership with Fact-Checking/Expert Organizations: Tech/social media platforms can facilitate Prebunking and Debunking initiatives/campaigns by collaborating with fact-checking/expert organisations and promoting such initiatives at a larger scale and ultimately fighting misinformation with joint hands initiatives.

Conclusion

The threat of online misinformation is only growing with every passing day and so, deploying effective countermeasures is essential.  Prebunking and Debunking are the two such interventions. To sum up: Prebunking interventions try to increase resilience to misinformation, proactively lowering susceptibility to erroneous or misleading information and addressing broader patterns of misinformation consumption, while Debunking is effective in correcting a particular piece of misinformation and having a targeted impact on belief in individual false claims. An integrated approach involving both the methods and joint initiatives by tech/social media platforms and expert organizations can ultimately help in fighting the rising tide of online misinformation and establishing a resilient online information landscape. 

References

1. https://mark-hurlstone.github.io/THKE.22.BJP.pdf
2. https://futurefreespeech.org/wp-content/uploads/2024/01/Empowering-Audiences-Through-%E2%80%98Prebunking-Michael-Bang-Petersen-Background-Report_formatted.pdf
3. https://newsreel.pte.hu/news/unprecedented_challenges_Debunking_disinformation
4. https://misinforeview.hks.harvard.edu/article/global-vaccination-badnews/

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