#FactCheck-Viral ‘Nehru Had Courage, Modi Bows Under Pressure’ Quote Is Falsely Attributed to Boris Johnson
Executive Summary
A purported statement attributed to former British Prime Minister Boris Johnson is going viral on social media. The post claims that Johnson said about Prime Minister Narendra Modi, “Modi bows under pressure.” It also attributes a comparison between Jawaharlal Nehru and Modi to Johnson, portraying Nehru as more courageous. A research done by the Research Wing of the CyberPeace found the viral claim to be false. During the research, the team examined Boris Johnson’s interview at the ET World Leaders Forum 2026, along with media reports related to the interview. The available transcript contains no remark by Johnson saying “Modi bows under pressure” or anything similar. On the contrary, he praised the changes and development taking place in India.
Claim:
A user on Instagram shared the viral claim, writing: “The purported statement by former British Prime Minister Boris Johnson has intensified the debate over India’s foreign policy and Prime Minister Narendra Modi’s leadership. The viral claim describes Nehru’s foreign policy as visionary while accusing the Modi government of bowing under pressure and taking shortcuts. The statement has triggered political reactions on social media.” The link to the post, its archive link, and a screenshot are provided below.
https://www.instagram.com/p/Dcdo_TGy-W0/
Fact Check
To verify the authenticity of the viral claim, the Desk examined the available transcript and reports of Boris Johnson’s interview at the ET World Leaders Forum 2026. The Desk found that Johnson also praised India’s current situation. He said that India is going through a “very good period under very strong and stable leadership.” He described India’s progress as “a really wonderful story” and expressed optimism about the country’s future.
During the interview, Johnson discussed several international issues, including India-UK relations, US-UK ties, NATO defence spending, the Iran war and the Ukraine war. The available interview transcript contains no statement from him saying that “Modi bows under pressure” or that Nehru was more courageous than Modi. Johnson also referred to Jawaharlal Nehru in the context of India-Russia relations. He recalled that the late Queen Elizabeth II had once shared an anecdote about a conversation she had with Nehru in the 1950s, during which Nehru reportedly told her that India would always stand by Russia and that some things never change.
Conclusion
Boris Johnson did not make any statement at the ET World Leaders Forum 2026 suggesting that “Modi bows under pressure” or that “Nehru had courage.” On the contrary, he praised India’s progress and spoke positively about Prime Minister Narendra Modi’s leadership. Johnson did mention Nehru during the conversation, but his reference was in the context of India-Russia relations and an old conversation between Nehru and the late Queen Elizabeth II. He did not compare Nehru with Modi, nor did he criticise Modi. Therefore, the claim circulating on social media attributing the statement “Nehru had courage, Modi bows under pressure” to Boris Johnson is false.
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Executive Summary
A video of actor Ranbir Kapoor is being shared on social media amid the ongoing Ganesh festivities across the country. The video is being shared with the claim that Ranbir Kapoor refused to accept prasad on the occasion of Ganesh Chaturthi. CyberPeace Research found the claim to be misleading. Our research revealed that the viral video is from 2023. At the time, Ranbir Kapoor had attended a Ganpati puja at the T-Series office. After the puja, when he was handed a box of prasad, he gave the box to another person and said that he was on a diet.
Claim:
A user on the social media platform Facebook shared the video with the caption, “Ranbir Kapoor refused to eat prasad, saying, ‘I am on a diet.’” The post link, archive link and screenshot are provided below.
https://www.facebook.com/reel/1563843065515757
Fact Check:
To verify the viral claim, we conducted a Google search using relevant keywords. During the search, we found a report published on the Dainik Hindustan website. According to the report, the Ambani family once again hosted a Ganpati celebration.Several Bollywood celebrities, including Salman Khan, Ranbir Kapoor, Katrina Kaif and Alia Bhatt, attended the event. Photographs from the celebration were also shared on social media. However, the clothes worn by Ranbir Kapoor in the photographs featured in the report are different from those seen in the viral video. The report link and screenshot are provided below.
To verify the viral claim, we conducted a reverse image search of keyframes from the video. During the search, we found the same video on the YouTube channel of Gujarati Midday, where it was published on September 25, 2023. The report link and screenshot are provided below.
https://www.youtube.com/watch?v=JoBwpC-jr5A
According to the video description, actor Ranbir Kapoor, known for his role in the film ‘Animal’, visited the T-Series office for Ganpati darshan. Our research established that the video is from 2023 and is being shared on social media with a misleading claim by presenting it as a recent incident. At the end of our research, we found the same video on the YouTube channel of Ei Samay. It was published on September 25, 2023. The video also contains the same scene seen in the viral clip.
https://www.youtube.com/watch?v=vmDsoRu8Aco
Conclusion
Our research found that the viral video is from 2023. At the time, Ranbir Kapoor attended a Ganpati puja at the T-Series office. After the puja, when he was handed a box of prasad, he gave the box to another person and said that he was on a diet.
Artificial intelligence is revolutionizing industries such as healthcare to finance to influence the decisions that touch the lives of millions daily. However, there is a hidden danger associated with this power: unfair results of AI systems, reinforcement of social inequalities, and distrust of technology. One of the main causes of this issue is training data bias, which appears when the examples on which an AI model is trained are not representative or skewed. To deal with it successfully, this needs a combination of statistical methods, algorithmic design that is mindful of fairness, and robust governance over the AI lifecycle. This article discusses the origin of bias, the ways to reduce it, and the unique position of fairness-conscious algorithms.
Why Bias in Training Data Matters
The bias in AI occurs when the models mirror and reproduce the trends of inequality in the training data. When a dataset has a biased representation of a demographic group or includes historical biases, the model will be trained to make decisions in ways that will harm the group. This is a fact that has a practical implication: prejudiced AI may cause discrimination during the recruitment of employees, lending, and evaluation of criminal risks, as well as various other spheres of social life, thus compromising justice and equity. These problems are not only technical in nature but also require moral principles and a system of governance (E&ICTA).
Bias is not uniform. It may be based on the data itself, the algorithm design, or even the lack of diversity among developers. The bias in data occurs when data does not represent the real world. Algorithm bias may arise when design decisions inadvertently put one group at an unfair advantage over another. Both the interpretation of the model and data collection may be affected by human bias. (MDPI)
Statistical Principles for Reducing Training Data Bias
Statistical principles are at the core of bias mitigation and they redefine the data-model interaction. These approaches are focused on data preparation, training process adjustment, and model output corrections in such a way that the notion of fairness becomes a quantifiable goal.
Balancing Data Through Re-Sampling and Re-Weighting
Among the aforementioned methods, a fair representation of all the relevant groups in the dataset is one way. This can be achieved by oversampling underrepresented groups and undersampling overrepresented groups. Oversampling gives greater weight to minority examples, whereas re-weighting gives greater weight to under-represented data points in training. The methods minimize the tendency of models to fit to salient patterns and improve coverage among vulnerable groups. (GeeksforGeeks)
Feature Engineering and Data Transformation
The other statistical technique is to convert data characteristics in such a way that sensitive characteristics have a lesser impact on the results. In one example, fair representation learning adjusts the data representation to discourage bias during the untraining of the model. The disparate impact remover adjust technique performs the adjustment of features of the model in such a way that the impact of sensitive features is reduced during learning. (GeeksforGeeks)
Measuring Fairness With Metrics
Statistical fairness measures are used to measure the effectiveness of a model in groups.
Fairness-Aware Algorithms Explained
Fair algorithms do not simply detect bias. They incorporate fairness goals in model construction and run in three phases including pre-processing, in-processing, and post-processing.
Pre-Processing Techniques
Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:
- Rebalancing training data through sampling and re-weighting training data to address sample imbalances.
- Data augmentation to generate examples of underrepresented groups.
- Feature transformation removes or downplays the impact of sensitive attributes prior to the commencement of training. (IJMRSET)
These methods can be used to guarantee that the model is trained on more balanced data and to reduce the chances of bias transfer between historical data.
In-Processing Techniques
The in-processing techniques alter the learning algorithm. These include:
- Fairness constraints that penalize the model for making biased predictions during training.
- Adversarial debiasing, where a second model is used to ensure that sensitive attributes are not predicted by the learned representations.
- Fair representation learning that modifies internal model representations in favor of
Post-Processing Techniques
Fairness may be enhanced after training by changing the model outputs. These strategies comprise:
- Threshold adjustments to various groups to meet conditions of fairness, like equalized odds.
- Calibration techniques such that the estimated probabilities are fair indicators of the actual probabilities in groups. (GeeksforGeeks)
Challenges
Mitigating bias is complex. The statistical bias minimization may at times come at the cost of the model accuracy, and there is a conflict between predictive performance and fairness. The definition of fairness itself is potentially a difficult task because various applications of fairness require various criteria, and various criteria can be conflicting. (MDPI)
Gaining varied and representative data is also a challenge that is experienced because of privacy issues, incomplete records, and a lack of resources. The auditing and reporting done on a continuous basis are needed so that mitigation processes are up to date, as models are continually updated. (E&ICTA)
Why Fairness-Aware Development Matters
The outcomes of the unfair treatment of some groups by AI systems are far-reaching. Discriminatory software in recruitment may support inequality in the workplace. Subjective credit rating may deprive deserving people of opportunities. Unbiased medical forecasts might result in the flawed allocation of medical resources. In both cases, prejudice contravenes the credibility and clouds the greater prospect of AI. (E&ICTA)
Algorithms that are fair and statistical mitigation plans provide a way to create not only powerful AI but also fair and trustworthy AI. They admit that the results of AI systems are social tools whose effects extend across society. Responsible development will necessitate sustained fairness quantification, model adjustment, and upholding human control.
Conclusion
AI bias is not a technical malfunction. It is a mirror of real-world disparities in data and exaggerated by models. Statistical rigor, wise algorithm design, and readiness to address the trade-offs between fairness and performance are required to reduce training data bias. Fairness-conscious algorithms (which can be implemented in pre-processing, in-processing, or post-processing) are useful in delivering more fair results. As AI is taking part in the most crucial decisions, it is necessary to consider fairness at the beginning to have a system that serves the population in a responsible and fair manner.
References
- Understanding Bias in Artificial Intelligence: Challenges, Impacts, and Mitigation Strategies: E&ICTA, IITK
- Bias and Fairness in Artificial Intelligence: Methods and Mitigation Strategies: JRPS Shodh Sagar
- Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies: MDPI
- Ensuring Fairness in Machine Learning Algorithms: GeeksforGeeks
Bias and Fairness in Machine Learning Models: A Critical Examination of Ethical Implications: IJMRSET - Bias in AI Models: Origins, Impact, and Mitigation Strategies: Preprints
- Bias in Artificial Intelligence and Mitigation Strategies: TCS
- Survey on Machine Learning Biases and Mitigation Techniques: MDPI
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.
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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.
- 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.
- 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.
- 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.
- 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
- https://mark-hurlstone.github.io/THKE.22.BJP.pdf
- https://futurefreespeech.org/wp-content/uploads/2024/01/Empowering-Audiences-Through-%E2%80%98Prebunking-Michael-Bang-Petersen-Background-Report_formatted.pdf
- https://newsreel.pte.hu/news/unprecedented_challenges_Debunking_disinformation
- https://misinforeview.hks.harvard.edu/article/global-vaccination-badnews/
