#FactCheck-Viral Video Falsely Attributes Remarks on US Leaders to CDS Anil Chauhan

Introduction

Indian Cybercrime Coordination Centre (I4C) was established by the Ministry of Home Affairs  (MHA) to provide a framework for law enforcement agencies (LEAs) to deal with cybercrime in a coordinated and comprehensive manner. The Indian Ministry of Home Affairs approved a scheme for the establishment of the Indian Cyber Crime Coordination Centre (I4C) in October 2018.  I4C is actively working towards initiatives to combat the emerging threats in cyberspace and it has become a strong pillar of India’s cyber security and cybercrime prevention. The ‘National Cyber Crime Reporting Portal’ equipped with a 24x7 helpline number 1930,  is one of the key components of the I4C.

On 10 September 2024, I4Ccelebrated its foundation day for the first time at Vigyan Bhawan, New Delhi.  This celebration marked a major milestone in India’s efforts against cybercrimes and in enhancing its cybersecurity infrastructure. Union Home Minister and Minister of Cooperation, Shri Amit Shah, launched key initiatives aimed at strengthening the country’s cybersecurity landscape.

Launch of Key Initiatives to Strengthen Cybersecurity

1. Cyber ​​Fraud Mitigation Centre (CFMC): As a     product of Prime Minister Shri Narendra Modi’s vision, the Cyber Fraud     Mitigation Centre (CFMC), was incorporated to bring together banks,     financial institutions, telecom companies, Internet Service Providers, and     law enforcement agencies on a single platform to tackle online financial     crimes efficiently. This integrated approach is     expected to minimise the time required to streamline operations and to     track and neutralise cyber fraud.
2. Cyber ​​Commando: The Cyber Commandos Program is an initiative in which     a specialised wing of trained Cyber Commandos will be established in     states, Union Territories, and Central Police Organizations. These     commandos will work to secure the nation’s digital space and counter     rising cyber threats. They will form the first line of defence in     safeguarding India from the growing cyber threats.
3. Samanvay Platform: The Samanvay platform is a web-based Joint Cybercrime     Investigation Facility System that was introduced as a one-stop data     repository for cybercrime. It facilitates cybercrime mapping, data     analytics, and cooperation among law enforcement agencies across the     country. This will play a pivotal role in fostering collaborations in     combating cybercrimes. Mr. Shah recognised the Samanvay platform as a     crucial step in fostering data sharing and collaboration. He called for a     shift from the “need to know” principle to a “duty to share” mindset in     dealing with cyber threats. The Samanvay platform will serve as India’s     first shared data repository, significantly enhancing the country’s     cybercrime response.
4. Suspect Registry: The Suspect Registry Portal is a national-level     platform that has been designed to track cybercriminals. The portal     registry will be connected to the National Cybercrime Reporting Portal     (NCRP) which aims to help banks, financial intermediaries, and law enforcement     agencies strengthen fraud risk management. The     initiative is expected to improve the real-time tracking of cyber     suspects, preventing repeat offences and improving fraud detection     mechanisms.

Rising Digitalization: Prioritizing Cybersecurity

The number of internet users in India has grown from 25 crores in 2014 to 95 crores in 2024, accompanied by a 78-foldincrease in data consumption. This growth is echoed in the number of growing cybersecurity challenges in the digital era. With the rise of digital transactions through Jan Dhan accounts, Rupay debit cards, and UPI systems, Shri Shah underscored the growing threat of digital fraud. He emphasised the need to protect personal data, prevent online harassment, and counter misinformation, fake news, and child abuse in the digital space.

The three new criminal laws, the Bharatiya Nyaya Sanhita (BNS), Bharatiya Nagrik Suraksha Sanhita (BNSS), and Bharatiya Sakshya Adhiniyam (BSA), which aim to strengthen India’s legal framework for cybercrime prevention, were also referred to in the address bythe Home Minister. These laws incorporate tech-driven solutions that will ensure investigations are conducted scientifically and effectively.

Mr. Shah emphasised popularising the 1930Cyber Crime Helpline. Additionally, he noted that I4C has issued over 600advisories, blocked numerous websites and social media pages operated by cybercriminals, and established a National Cyber Forensic Laboratory in Delhi. Over 1,100 officers have already received cyber forensics training under theI4C umbrella.

In response to the regional cybercrime challenges, the formation of Joint Cyber Coordination Teams in cybercrime hotspot areas like Mewat, Jamtara, Ahmedabad, Hyderabad, Chandigarh, Visakhapatnam and Guwahati was highlighted as a coordinated response to local cybercrime hotspot issues.

Conclusion

With the launch of initiatives like the Cyber Fraud Mitigation Centre, the Samanvay platform, and the Cyber Commandos Program, I4C is positioned to play a crucial role in combating cybercrime. The I4C is moving forward with a clear vision for a secure digital future and safeguarding India's digital ecosystem.

References:

●     https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2053438

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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

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India officially became part of the US-led Pax Silica project on February 20, 2026, at the India AI Impact Summit in New Delhi. This was a significant milestone in India’s involvement in global technology and supply chain cooperation. India joined a coalition of advanced economies by signing the Pax Silica Declaration in a move aimed at strengthening coordination over technology supply chains on which artificial intelligence, semiconductors, critical minerals and advanced manufacturing rely. The entry of India into the global technology landscape is indicative of India’s growing role in the global technology order and reflects broader shifts in how countries are responding to the geopolitics of silicon and AI infrastructure.

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What Is Pax Silica and Why It Matters

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The United States Department of State introduced Pax Silica as a strategic program launched in December 2025. It seeks to establish safe, resilient and innovation-driven supply chains for emerging technologies that are the foundations of the AI era. This encompasses activities ranging from mining and refining of rare earths, gallium and germanium to semiconductor manufacturing, the creation of advanced computing hardware and energy infrastructure. The project describes cooperation as a method of reducing what are termed as coercive dependencies on any one supplier or economy, thereby supporting sustained access to building blocks of state-of-the-art technology.

Pax Silica derives its name from the Latin terms for 'peace' and the substrate material of 'silicon', meaning that the coalition aims at achieving stability and prosperity by working together in supply chains of technology. Early signatories were the United States, Japan, South Korea, Australia, the United Kingdom, Israel, Singapore, the Netherlands, Greece, Qatar and the United Arab Emirates. India was the twelfth member to sign the declaration.

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India’s Strategic Interests in Pax Silica

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The move to join Pax Silica is both a diplomatic and economic decision. The incorporation of India into a network led ostensibly by the Western bloc and containing developed economy players in the technological supply chain creates the messaging that it wants to be more deeply integrated into the global high-tech ecosystems. 

India currently relies on importing a large proportion of the chips for its electronics production sector, while its domestic manufacturing capacity remains limited. Pax Silica membership could provide Indian firms with advanced manufacturing equipment, process expertise and joint ventures with their partners, who have already developed the fabrication capabilities.

The signing of the declaration was done by the current Union Minister of Electronics and Information Technology (MeitY) , the Union Minister, who noted that India is expanding its technological capabilities and future ambitions. He observed that the Indian engineers already play a role in designing advanced semiconductor chips and that the increase in semiconductor capacity will demand a professional workforce. He also emphasised that the availability of international tools and alliances would help accelerate India’s growth in this sector.

Another strategic area is the critical minerals. India is estimated to have significant rare earth reserves, but the resources remain largely underdeveloped. The diversification strategy of Pax Silica in terms of supply and processing routes provides India with an opportunity to have joint ventures and infrastructure projects that could help unlock domestic mineral potential within the country.

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Supply Chains, AI, and Geopolitical Context

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Pax Silica has emerged within a broader geopolitical and supply chain context rather than as a purely economic initiative. The last few years have placed a strain on global technology supply chains with disruptions caused by pandemics, trade tensions, export controls, and the concentrated control of some components of the value chain. China currently dominates in the refinement of rare earths as well as in a variety of legacy semiconductor manufacturing. The concentration has raised concerns about resilience and strategic autonomy among the technology-producing democracies.

This initiative is based on the premise that a diversified and trusted supply chain will make the economic security of countries participating in Pax Silica more secure in case of a trade embargo or as a tool of political leverage. The voluntary and non-binding framework by the coalition only provides a guide to cooperation instead of a binding commitment, though it highlights an acknowledgement of risk and opportunity in global technology markets.

Such concerns as strategic autonomy and the extent of India’s involvement in the initiative have been expressed by those who criticise it, particularly because the coalition is perceived to be partially designed to respond to Chinese dominance in the most important technological sectors. Some analysts have also suggested that India will have to balance its participation in Pax Silica by taking special care of its own interests and alliances outside this coalition.

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Economic and Industrial Implications for India

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Joining Pax Silica offers India potential benefits on multiple fronts.

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Strengthening Innovation and Manufacturing Ecosystems

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India's membership will allow cooperation in semiconductor production, development of advanced computing infrastructure and implementation of AI. The government and industry players could attract investments through partnerships, technology transfer and joint R&D. India’s emerging design and fabrication projects could use a greater international integration in this venture.

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Talent and Skills Development

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A recurring theme among Indian policymakers is the issue of a skilled workforce. As the world semiconductor and AI sector is expected to need millions of specialists in the next 10 years, India’s large talent pool presents an opportunity to produce local talent that is capable of catering to local demands as well as international supply needs. Initiatives linked to Pax Silica have the potential to establish training pathways and institutional bridges that facilitate workforce preparedness.

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Diversification of Supply Partnerships

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In the case of India, the diversification of suppliers and partners goes beyond the availability of materials and technologies. It also implies reducing exposure to supply shocks and enhancing resilience in important industries such as consumer electronics, automotive manufacturing, defence systems and digital infrastructure, all of which rely on semiconductors and advanced computing hardware.

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Broader Industrial Readiness and Domestic Challenges

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India’s participation in Pax Silica highlights the domestic conditions required to support advanced technology manufacturing. A conducive environment will depend on reliable infrastructure, regulatory stability, specialised industrial clusters and sustained policy coordination across government and industry. Semiconductor and AI hardware production are resource-intensive, requiring significant energy, water and chemical management, making environmental safeguards and sustainable industrial planning essential to prevent long-term ecological strain.

At the same time, India faces gaps in its human resource development ecosystem. While engineering talent is abundant, specialised training in semiconductor fabrication, materials science and advanced manufacturing remains limited. Additionally, the relative lack of applied research and development initiatives aimed at reducing technological and financial risks may constrain large-scale industrial expansion, underscoring the need for stronger industry–academia collaboration and targeted innovation support.

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Conclusion: A Strategic Step into the AI Era

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India’s formal entry into the Pax Silica initiative at the 2026 India AI Impact Summit reflects a thoughtful recalibration of its global technology engagement. By aligning with a coalition aimed at securing the supply chains that make modern digital economies possible, India has signalled its intent to be more than just a consumer of technology. It seeks to help shape the infrastructure, partnerships and norms that will define the next generation of AI, semiconductors and critical technologies.

While questions around strategic autonomy and long-term dependencies remain important considerations, Pax Silica offers India access to networks, capabilities and collaborative frameworks that can accelerate its semiconductor ambitions and broaden its role in the global tech order. The move underscores how technology cooperation today increasingly interacts with geopolitics, economic strategy and national aspirations for growth and innovation.

Sources

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• https://timesofindia.indiatimes.com/technology/tech-news/what-is-pax-silica-and-why-does-india-joining-the-ai-supply-chain-alliance-matter/articleshow/128594775.cms
• https://paxsilica.org/f/pax-silica-securing-the-foundations-of-the-ai-era
• https://www.businesstoday.in/india/story/ai-impact-summit-2026-india-set-to-join-us-led-pax-silica-today-517167-2026-02-20
• https://www.business-standard.com/india-news/pax-silica-india-joins-us-supply-chain-initiative-ai-impact-summit-2026-126022000339_1.html

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