#FactCheck - Debunking Viral Photo: Tears of Photographer Not Linked to Ram Mandir Opening

Introduction

Search engines have become indispensable in our daily lives, allowing us to find information instantly by entering keywords or phrases. Using the prompt "search Google or type a URL" reflects just how seamless this journey to knowledge has become. With millions of searches conducted every second, and Google handling over 6.3 million searches per minute as of 2023 (Statista), one critical question arises: do search engines prioritise results based on user preferences and past behaviours, or are they truly unbiased?

Understanding AI Bias in Search Algorithms

AI bias is also known as machine learning bias or algorithm bias. It refers to the occurrence of biased results due to human biases that deviate from the original training data or AI algorithm which leads to distortion of outputs and creation of potentially harmful outcomes. The sources of this bias are algorithmic bias, data bias and interpretation bias which emerge from user history, geographical data, and even broader societal biases in training data. 

Common biases include excluding certain groups of people from opportunities because of AI bias. In healthcare, underrepresenting data of women or minority groups can skew predictive AI algorithms. While AI helps streamline the automation of resume scanning during a search to help identify ideal candidates, the information requested and answers screened out can result in biased outcomes due to a biased dataset or any other bias in the input data.

Case in Point: Google’s "Helpful" Results and Its Impact 

Google optimises results by analysing user interactions to determine satisfaction with specific types of content. This data-driven approach forms ‘filter bubbles’ by repeatedly displaying content that aligns with a user’s preferences, regardless of factual accuracy. While this can create a more personalised experience, it risks confining users to a limited view, excluding diverse perspectives or alternative viewpoints.

The personal and societal impacts of such biases are significant. At an individual level, filter bubbles can influence decision-making, perceptions, and even mental health. On a societal level, these biases can reinforce stereotypes, polarise opinions, and shape collective narratives. There is also a growing concern that these biases may promote misinformation or limit users’ exposure to diverse perspectives, all stemming from the inherent bias in search algorithms.

Policy Challenges and Regulatory Measures

Regulating emerging technologies like AI, especially in search engine algorithms, presents significant challenges due to their intricate, proprietary nature. Traditional regulatory frameworks struggle to keep up with them as existing laws were not designed to address the nuances of algorithm-driven platforms. Regulatory bodies are pushing for transparency and accountability in AI-powered search algorithms to counter biases and ensure fairness globally. For example, the EU’s Artificial Intelligence Act aims to establish a regulatory framework that will categorise AI systems based on risk and enforces strict standards for transparency, accountability, and fairness, especially for high-risk AI applications, which may include search engines. India has proposed the Digital India Act in 2023 which will define and regulate High-risk AI. 

Efforts include ethical guidelines emphasising fairness, accountability, and transparency in information prioritisation. However, a complex regulatory landscape could hinder market entrants, highlighting the need for adaptable, balanced frameworks that protect user interests without stifling innovation.

CyberPeace Insights

In a world where search engines are gateways to knowledge, ensuring unbiased, accurate, and diverse information access is crucial. True objectivity remains elusive as AI-driven algorithms tend to personalise results based on user preferences and past behaviour, often creating a biased view of the web. Filter bubbles, which reinforce individual perspectives, can obscure factual accuracy and limit exposure to diverse viewpoints. Addressing this bias requires efforts from both users and companies. Users should diversify sources and verify information, while companies should enhance transparency and regularly audit algorithms for biases. Together, these actions can promote a more equitable, accurate, and unbiased search experience for all users. 

References 

• https://www.bbc.com/future/article/20241101-how-online-photos-and-videos-alter-the-way-you-think 
• https://www.bbc.com/future/article/20241031-how-google-tells-you-what-you-want-to-hear
• https://www.ibm.com/topics/ai-bias#:~:text=In%20healthcare%2C%20underrepresenting%20data%20of,can%20skew%20predictive%20AI%20algorithms

‍

Introduction

Quantum mechanics is not a new field. It finds its roots in the works of physicists such as Niels Bohr in the 1920s, and has informed the development of technologies like nuclear power in the past. But with developments in science and engineering, we are at the cusp of harnessing quantum mechanics for a new wave of real-world uses in sensing and metrology, computing, networking, security, and more. While at different stages of development, quantum technologies have the potential to revolutionise global security, economic systems, and digital infrastructure. The science is dazzling, but it is equally urgent to start preparing for its broader impact on society, especially regarding privacy and digital security. This article explores quantum computing, its threat to information integrity, and global interdependencies as they exist today, and discusses policy areas that should be addressed.

What Is Quantum Computing?

Classical computers use binary bits (0 or 1) to represent and process information. This binary system forms the base of modern computing. But quantum computers use qubits (quantum bits) as a basic unit, which can exist in multiple states ( 0, 1, both, or with other qubits) simultaneously due to quantum principles like superposition and entanglement. This creates an infinite range of possibilities in information processing and allows quantum machines to perform complex computations at speeds impossible for traditional computers. While still in their early stages, large-scale quantum computers could eventually:

1. Break modern encryption systems
2. Model complex molecules for drug discovery
3. Optimise global logistics and financial systems
4. Accelerate AI and machine learning

While this could eventually present significant opportunities in fields such as health innovation, material sciences, climate modelling, and cybersecurity, challenges will continue to arise even before the technology is ready for commercial application. Policymakers must start anticipating their impact.

Threats

Policy solutions surrounding quantum technologies will depend on the pace of development of the elements of the quantum ecosystem. However, the most urgent concerns regarding quantum computing applications are the risk to encryption and the impact on market competition.

1. Cybersecurity Threat: Digital infrastructure today (e.g., cloud services, networks, servers, etc.) across sectors such as government, banking and finance, healthcare, energy, etc., depends on encryption for secure data handling and communications. Threat actors can utilise quantum computers to break this encryption. Widely used asymmetric encryption keys, such as RSA or ECC, are particularly susceptible to being broken. Threat actors could "harvest now, decrypt later”- steal encrypted data now and decrypt it later when quantum capabilities mature. Although AES-256, a symmetric encryption standard, is currently considered resistant to quantum decryption, it only protects data after a secure connection is established through a process that today relies on RSA or ECC. This is why governments and companies are racing to adopt Post- Quantum Cryptography (PQC) and quantum key distribution (QKD) to protect security and privacy in digital infrastructure.

2. Market Monopoly: Quantum computing demands significant investments in infrastructure, talent, and research, which only a handful of countries and companies currently possess. As a result, firms that develop early quantum advantage may gain unprecedented competitive leverage through offerings such as quantum-as-a-service, disrupting encryption-dependent industries, or accelerating innovation in pharmaceuticals, finance, and logistics. This could reinforce the existing power asymmetries in the global digital economy. Given these challenges, proactive and forward-looking policy frameworks are critical.

What Should Quantum Computing Policy Cover?

Commercial quantum computing will transform many industries. Policy will have to be flexible and be developed in iterations to account for fast-paced developments in the field. It will also require enduring international collaboration to effectively address a broad range of concerns, including ethics, security, privacy, competition, and workforce implications.

1. Cybersecurity and Encryption: Quantum policy should prioritise the development and standardisation of quantum-resistant encryption methods. This includes ongoing research into Post-Quantum Cryptography (PQC) algorithms and their integration into digital infrastructure. Global policy will need to align national efforts with international standards to create unified quantum-safe encryption protocols.

2. Market Competition and Access: Given the high barriers to entry, regulatory frameworks should promote fair competition, enabling smaller players like startups and developing economies to participate meaningfully in the quantum economy. Frameworks to ensure equitable access, interoperability, and fair competition will become imperative as the quantum ecosystem matures so that society can reap its benefits as a whole.

4. Ethical Considerations: Policymakers will have to consider the impact on privacy and security, and push for the responsible use of quantum capabilities. This includes ensuring that quantum advances do not contribute to cybercrime, disproportionate surveillance, or human rights violations.

5. International Standard-Setting: Setting benchmarks, shared terminologies, and measurement standards will ensure interoperability and security across diverse stakeholders and facilitate global collaboration in quantum research and infrastructure.

6. Military and Defence Implications: Militarisation of quantum technologies is a growing concern, and national security affairs related to quantum espionage are being urgently explored. Nations will have to develop regulations to protect sensitive data and intellectual property from quantum-enabled attacks.

7. Workforce Development and Education: Policies should encourage quantum computing education at various levels to ensure a steady pipeline of talent and foster cross-disciplinary programs that blend quantum computing with fields like machine learning, AI, and engineering.

8. Environmental and Societal Impact: Quantum computing hardware requires specialised conditions such as extreme cooling. Policy will have to address the environmental footprint of the infrastructure and energy consumption of large-scale quantum systems. Broader societal impacts of quantum computing, including potential job displacement, accessibility issues, and the equitable distribution of quantum computing benefits, will have to be explored.

Conclusion

Like nuclear power and AI, the new wave of quantum technologies is expected to be an exciting paradigm shift for society. While they can bring numerous benefits to commercial operations and address societal challenges, they also pose significant risks to global information security. Quantum policy will require regulatory, strategic, and ethical frameworks to govern the rise of these technologies, especially as they intersect with national security, global competition, and privacy. Policymakers must act in collaboration to mitigate unethical use of these technologies and the entrenchment of digital divides across countries. The OECD’s Anticipatory Governance of Emerging Technologies provides a framework of essential values like respect for human rights, privacy, and sustainable development, which can be used to set a baseline, so that quantum computing and related technologies benefit society as a whole.

References

• https://www.weforum.org/stories/2024/07/explainer-what-is-quantum-technology/
• https://www.paconsulting.com/insights/what-is-quantum-technology
• https://delinea.com/blog/quantum-safe-encryption#:~:text=This%20can%20result%20in%20AES,%2D128%20to%20AES%2D256.
• https://www.oecd.org/en/publications/a-quantum-technologies-policy-primer_fd1153c3-en.html

‍

Executive Summary:

‍
Recently, we came upon some AI-generated deep fake videos that have gone viral on social media, purporting to show Indian political figures Prime Minister Narendra Modi, Home Minister Amit Shah, and External Affairs Minister Dr. S. Jaishankar apologizing in public for initiating "Operation Sindoor." The videos are fake and use artificial intelligence tools to mimic the leaders' voices and appearances, as concluded by our research. The purpose of this report is to provide a clear understanding of the facts and to reveal the truth behind these viral videos.

Claim:

‍

Multiple videos circulating on social media claim to show Prime Minister Narendra Modi, Central Home Minister Amit Shah, and External Affairs Minister Dr. S. Jaishankar publicly apologised for launching "Operation Sindoor." The videos, which are being circulated to suggest a political and diplomatic failure, feature the leaders speaking passionately and expressing regret over the operation.

‍

‍

‍

‍

‍

Fact Check:

‍

‍
Our research revealed that the widely shared videos were deepfakes made with artificial intelligence tools. Following the 22 April 2025 Pahalgam terror attack, after “Operation Sindoor”, which was held by the Indian Armed Forces, this video emerged, intending to spread false propaganda and misinformation. 

Finding important frames and visual clues from the videos that seemed suspicious, such as strange lip movements, misaligned audio, and facial distortions, was the first step in the fact-checking process. By putting audio samples and video frames in Hive AI Content Moderation, a program for detecting AI-generated content. After examining audio, facial, and visual cues, Hive's deepfake detection system verified that all three of the videos were artificial intelligence (AI) produced.

Below are three Hive Moderator result screenshots that clearly flag the videos as synthetic content, confirming that none of them are authentic or released by any official government source.

‍

‍

Conclusion:

‍
The artificial intelligence-generated videos that claim Prime Minister Narendra Modi, Home Minister Amit Shah, and External Affairs Minister Dr. S. Jaishankar apologized for the start of "Operation Sindoor" are completely untrue. A purposeful disinformation campaign to mislead the public and incite political unrest includes these deepfake videos. No such apology has been made by the Indian government, and the operation in question does not exist in any official or verified capacity. The public must exercise caution, avoid disseminating videos that have not been verified, and rely on reliable fact-checking websites. Such disinformation can seriously affect national discourse and security in addition to eroding public trust.

• Claim: India's top executives apologize publicly for Operation Sindoor blunder.
• Claimed On: Social Media
• ‍Fact Check: AI Misleads

‍

‍