#FactCheck -AI-Manipulated Video Falsely Claims ₹50 Crore Deal Involving Bhupen Bora

Executive Summary 

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A video of the Leader of the Opposition in the Lok Sabha and Congress MP Rahul Gandhi is being widely shared on social media. In the clip, Gandhi is seen saying that he does not know what “G Gram G” is. Several users are sharing the video with the claim that Rahul Gandhi insulted Lord Ram. However, CyberPeace research found that the claim is misleading. Rahul Gandhi was not referring to Lord Ram in the video. Instead, he was speaking about a newly introduced law titled Viksit Bharat–G RAM G (VB–G RAM G), which has been brought in to replace the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA). The viral clip has been shared with a false narrative.

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Claim

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On January 22, 2026, an Instagram user apnisarkar2024 shared the video claiming, “Rahul Gandhi once again insulted Shri Ram.” (Link, archive link, and screenshot available above)

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• https://www.instagram.com/reel/DTzeiy0k3l5
• https://perma.cc/J3A3-NGBM?type=standard

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Research

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As part of the Research, we first closely examined the viral video. In the clip, Rahul Gandhi is heard saying: “I don’t know what Gram G is. I don’t even know the name of this new law… what is G Gram G…” At no point in the video does Rahul Gandhi mention Lord Ram or make any comment related to religion. To verify the context, we extracted keyframes from the viral clip and conducted a Google Lens search. This led us to a longer version of the same speech uploaded on the official YouTube channel of the Indian National Congress on January 22, 2026. The viral segment appears after the 39:50-minute mark.

The video is from the National MGNREGA Convention held in New Delhi, where Rahul Gandhi criticised the central government over the replacement of MGNREGA with the new VB–G RAM G law. During his speech, he expressed his opposition to the new legislation and stated that he was unfamiliar with its details. Throughout the address, he did not mention or refer to Lord Ram in any manner.

• https://youtu.be/9cFTuhjsKx4

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Conclusion

Rahul Gandhi’s remarks in the viral video were related to the newly introduced VB–G RAM G law and were part of his criticism of the central government’s policy decisions. He did not insult Lord Ram. The video is being shared on social media with a misleading and false claim.

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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.

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Why Bias in Training Data Matters

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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)

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Statistical Principles for Reducing Training Data Bias 

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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.

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Balancing Data Through Re-Sampling and Re-Weighting

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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)

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Feature Engineering and Data Transformation

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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)

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Measuring Fairness With Metrics

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Statistical fairness measures are used to measure the effectiveness of a model in groups. 

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Fairness-Aware Algorithms Explained

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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.

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Pre-Processing Techniques

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Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:

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• 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)

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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.

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In-Processing Techniques

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The in-processing techniques alter the learning algorithm. These include:

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• 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 

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Post-Processing Techniques

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Fairness may be enhanced after training by changing the model outputs. These strategies comprise:

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• 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)

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Challenges 

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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)

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Why Fairness-Aware Development Matters

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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.

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Conclusion 

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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.

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References

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• 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

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Introduction

Summer vacations have always been one of the most anticipated times in a child’s life. In earlier times, it was something entirely different. The season was filled with outdoor games, muddy hands, mango-stained mouths, and stories shared with cousins under the stars. Children lived in the moment, playing in parks, riding bicycles, and inventing new adventures without a screen in sight. Today, those same summer days are shaped by glowing devices, virtual games, and hours spent online. While technology brings learning and entertainment, it also invites risks that parents cannot ignore. The Cyber Mom Toolkit is here to help you navigate this shift, offering simple and thoughtful ways to keep your children safe, balanced, and joyful during these screen filled holidays.
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The Hidden Cyber Risks of Summer Break

With increased leisure time and less supervision, children are likely to venture into unknown reaches of the internet. I4C reports indicate that child-related cases, such as cyberbullying, sextortion, and viewing offensive content, surge during school vacations. Gaming applications, social networking applications, and YouTube can serve as entry points for cyber predators and spammers. That's why it is important that parents, particularly mothers know what digital spaces their children live in and how to intervene appropriately.

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Your Action Plan for Being a Cyber Smart Mom

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Moms Need to Get Digitally Engaged 

You do not need to be a tech expert to become a cyber smart mom. With just a few simple digital skills, you can start protecting your child online with confidence and ease.

1. Know the Platforms Your Children Use

Spend some time investigating apps such as Instagram, Snapchat, Discord, YouTube, or computer games like Roblox and Minecraft. Familiarise yourself with the type of content, chat options, and privacy loopholes they may have.

2. Install Parental Controls

Make use of native features on devices (Android, iOS, Windows) to limit screen time, block mature content, and track downloads. Applications such as Google Family Link and Apple Screen Time enable parents to control apps and web browsing. 

3. Develop a Family Cyber Agreement

• Establish common rules such as:
• No devices in bedrooms past 9 p.m.
• Add only safe connections on social media.
• Don't open suspicious messages or click on mysterious links.
• Always tell your mom if something makes you feel uncomfortable online.

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‍Talk Openly and Often

Kids tend to hide things online because they don't want to get punished or embarrassed. Trust is built better than monitoring. Here's how:

• Have non-judgmental chats about what they do online.
• Use news reports or real-life cases as conversation starters: "Did you hear about that YouTuber's hacked account?
• Encourage them to question things if they're confused or frightened.
• Honour their online life as a legitimate aspect of their lives.

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Look for the Signs of Online Trouble

Stay alert to subtle changes in your child’s behavior, as they can be early signs of trouble in their online world.

• Sudden secrecy or aggression when questioned about online activity.
• Overuse of screens, particularly in the evening.
• Deterioration in school work or interest in leisure activities.
• Mood swings, anxiety, or withdrawn behaviour.

If you notice these, speak to your child calmly. You can also report serious matters such as cyberbullying or blackmail on the Cybercrime Helpline 1930 or visit https://cybercrime.gov.in

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Support Healthy Digital Behaviours

Teach your kids to be good netizens by leading them to:

• Reflect Before Posting: No address, school name, or family information should ever appear in public posts.
• Set Strong Passwords: Passwords must be long, complicated, and not disclosed to friends, even best friends.
• Enable Privacy Settings: Keep social media accounts privately. Disable location sharing. Restrict comments and messages from others.
• Vigilance: Encourage them to spot fake news, scams, and manipulative ads. Critical thinking is the ultimate defence.

Stay alert to subtle changes in your child’s behavior, as they can be early signs of trouble in their online world.

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Where to Learn More and Get Support as a Cyber Mom

Cyber moms looking to deepen their understanding of online safety can explore a range of helpful resources offered by CyberPeace. Our blog features easy-to-understand articles on current cyber threats, safety tips, and parenting guidance for the digital age. You can also follow our social media pages for regular updates, quick tips, and awareness campaigns designed especially for families. If you ever feel concerned or need help, the CyberPeace Helpline is available to offer support and guidance. (+91 9570000066 or write to us at helpline@cyberpeace.net). For those who want to get more involved, joining the CyberPeace Corps allows you to become part of a larger community working to promote digital safety and cyber awareness across the country.

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Empowering Mothers Empowers Society

We at CyberPeace feel that every mother, irrespective of her background and technological expertise, has the potential to be a Cyber Mom. The intention is not to control the child but to mentor towards safer decisions, identify issues early, and prepare them for a lifetime of online responsibility. Mothers are empowered when they know. And children are safe when they are protected.

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Conclusion

The web isn't disappearing, and neither are its dangers. But when mothers are digital role models, they can make summer screen time a season of wise decisions. This summer, become a Cyber Mom: someone who learns, leads, and listens. Whether it's installing a parental control app, discussing openly about cyberbullying, or just asking your child, "What did you discover online today? " that engagement can make a difference. This summer break, help your child become digitally equipped with the skills and knowledge they need to navigate the online world safely and confidently.

Cyber safety starts at home, and there's no better point of departure than being alongside your child, rather than behind them.

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References

• https://cybercrime.gov.in
• https://support.apple.com/en-in/HT208982
• https://beinternetawesome.withgoogle.com
• https://www.cyberpeace.org
• https://ncpcr.gov.in

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