#FactCheck - Viral Claim of Highway in J&K Proven Misleading

Artificial Intelligence (AI) provides a varied range of services and continues to catch intrigue and experimentation. It has altered how we create and consume content. Specific prompts can now be used to create desired images enhancing experiences of storytelling and even education. However, as this content can influence public perception, its potential to cause misinformation must be noted as well. The realistic nature of the images can make it hard to discern as artificially generated by the untrained eye. As AI operates by analysing the data it was trained on previously to deliver, the lack of contextual knowledge and human biases (while framing prompts) also come into play. The stakes are higher whilst dabbling with subjects such as history, as there is a fine line between the creation of content with the intent of mere entertainment and the spread of misinformation owing to biases and lack of veracity left unchecked. AI-generated images enhance storytelling but can also spread misinformation, especially in historical contexts. For instance, an AI-generated image of London during the Black Death might include inaccurate details, misleading viewers about the past.

The Rise of AI-Generated Historical Images as Entertainment

Recently, generated images and videos of various historical instances along with the point of view of the people present have been floating all over the internet. Some of them include the streets of London during the Black Death in the 1300s in England, the eruption of Mount Vesuvius at Pompeii etc. Hogne and Dan, two creators who operate accounts named POV Lab and Time Traveller POV on TikTok state that they create such videos as they feel that seeing the past through a first-person perspective is an interesting way to bring history back to life while highlighting the cool parts, helping the audience learn something new. Mostly sensationalised for visual impact and storytelling, such content has been called out by historians for inconsistencies with respect to details particular of the time. Presently, artists admit to their creations being inaccurate, reasoning them to be more of an artistic interpretation than fact-checked documentaries. 

It is important to note that AI models may inaccurately depict objects (issues with lateral inversion), people(anatomical implausibilities), or scenes due to "present-ist" bias. As noted by Lauren Tilton, an associate professor of digital humanities at the University of Richmond, many AI models primarily rely on data from the last 15 years, making them prone to modern-day distortions especially when analysing and creating historical content. The idea is to spark interest rather than replace genuine historical facts while it is assumed that engagement with these images and videos is partly a product of the fascination with upcoming AI tools. Apart from this, there are also chatbots like Hello History and Charater.ai which enable simulations of interacting with historical figures that have piqued curiosity.

Although it makes for an interesting perspective, one cannot ignore that our inherent biases play a role in how we perceive the information presented. Dangerous consequences include feeding into conspiracy theories and the erasure of facts as information is geared particularly toward garnering attention and providing entertainment. Furthermore, exposure of such content to an impressionable audience with a lesser attention span increases the gravity of the matter. In such cases, information regarding the sources used for creation becomes an important factor.

Acknowledging the risks posed by AI-generated images and their susceptibility to create misinformation, the Government of Spain has taken a step in regulating the AI content created. It has passed a bill (for regulating AI-Generated content) that mandates the labelling of AI-generated images and failure to do so would warrant massive fines (up to $38 million or 7% of turnover on companies). The idea is to ensure that content creators label their content which would help to spot images that are artificially created from those that are not.

The Way Forward: Navigating AI and Misinformation

While AI-generated images make for exciting possibilities for storytelling and enabling intrigue, their potential to spread misinformation should not be overlooked. To address these challenges, certain measures should be encouraged.

1. Media Literacy and Awareness – In this day and age critical thinking and media literacy among consumers of content is imperative. Awareness, understanding, and access to tools that aid in detecting AI-generated content can prove to be helpful.
2. AI Transparency and Labeling – Implementing regulations similar to Spain’s bill on labelling content could be a guiding crutch for people who have yet to learn to tell apart AI-generated content from others.
3. Ethical AI Development – AI developers must prioritize ethical considerations in training using diverse and historically accurate datasets and sources which would minimise biases.

As AI continues to evolve, balancing innovation with responsibility is essential. By taking proactive measures in the early stages, we can harness AI's potential while safeguarding the integrity and trust of the sources while generating images.

References:

• https://www.npr.org/2023/06/07/1180768459/how-to-identify-ai-generated-deepfake-images
• https://www.nbcnews.com/tech/tech-news/ai-image-misinformation-surged-google-research-finds-rcna154333
• https://www.bbc.com/news/articles/cy87076pdw3o
• https://newskarnataka.com/technology/government-releases-guide-to-help-citizens-identify-ai-generated-images/21052024/
• https://www.technologyreview.com/2023/04/11/1071104/ai-helping-historians-analyze-past/
• https://www.psypost.org/ai-models-struggle-with-expert-level-global-history-knowledge/
• https://www.youtube.com/watch?v=M65IYIWlqes&t=2597s
• https://www.vice.com/en/article/people-are-creating-records-of-fake-historical-events-using-ai/?utm_source=chatgpt.com
• https://www.reuters.com/technology/artificial-intelligence/spain-impose-massive-fines-not-labelling-ai-generated-content-2025-03-11/?utm_source=chatgpt.com
• https://www.theguardian.com/film/2024/sep/13/documentary-ai-guidelines?utm_source=chatgpt.com

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A report by MarketsandMarkets in 2024 showed that the global AI market size is estimated to grow from USD 214.6 billion in 2024 to USD 1,339.1 billion in 2030, at a CAGR of 35.7%. AI has become an enabler of productivity and innovation. A Forbes Advisor survey conducted in 2023 reported that 56% of businesses use AI to optimise their operations and drive efficiency. Further, 51% use AI for cybersecurity and fraud management, 47% employ AI-powered digital assistants to enhance productivity and 46% use AI to manage customer relationships.

AI has revolutionised business functions. According to a Forbes survey, 40% of businesses rely on AI for inventory management, 35% harness AI for content production and optimisation and 33% deploy AI-driven product recommendation systems for enhanced customer engagement.  This blog addresses the opportunities and challenges posed by integrating AI into operational efficiency.

Artificial Intelligence and its resultant Operational Efficiency

AI has exemplary optimisation or efficiency capabilities and is widely used to do repetitive tasks. These tasks include payroll processing, data entry, inventory management, patient registration, invoicing, claims processing, and others. AI use has been incorporated into such tasks as it can uncover complex patterns using NLP, machine learning, and deep learning beyond human capabilities. It has also shown promise in improving the decision-making process for businesses in time-critical, high-pressure situations. 

AI-driven efficiency is visible in industries such as the manufacturing industry for predictive maintenance, in the healthcare industry for streamlining diagnostics and in logistics for route optimisation. Some of the most common real-world examples of AI increasing operational efficiency are self-driving cars (Tesla), facial recognition (Apple Face ID), language translation (Google Translate), and medical diagnosis (IBM Watson Health)

Harnessing AI has advantages as it helps optimise the supply chain, extend product life cycles, and ultimately conserve resources and cut operational costs. 

Policy Implications for AI Deployment 

Some of the policy implications for development for AI deployment are as follows:

1. Develop clear and adaptable regulatory frameworks for the ongoing and future developments in AI. The frameworks need to ensure that innovation is not hindered while managing the potential risks. 
2. As AI systems rely on high-quality data that is accessible and interoperable to function effectively and without proper data governance, these systems may produce results that are biased, inaccurate and unreliable. Therefore, it is necessary to ensure data privacy as it is essential to maintain trust and prevent harm to individuals and organisations.
3. Policy developers need to focus on creating policies that upskill the workforce which complements AI development and therefore job displacement. 
4. To ensure cross-border applicability and efficiency of standardising AI policies, the policy-makers need to ensure that international cooperation is achieved when developing the policies.

Addressing Challenges and Risks 

Some of the main challenges that emerge with the development of AI are algorithmic bias, cybersecurity threats and the dependence on exclusive AI solutions or where the company retains exclusive control over the source codes. Some policy approaches that can be taken to mitigate these challenges are:

1. Having a robust accountability mechanism. 
2. Establishing identity and access management policies that have technical controls like authentication and authorisation mechanisms. 
3. Ensure that the learning data that AI systems use follows ethical considerations such as data privacy, fairness in decision-making, transparency, and the interpretability of AI models.

Conclusion 

AI can contribute and provide opportunities to drive operational efficiency in businesses. It can be an optimiser for productivity and costs and foster innovation for different industries. But this power of AI comes with its own considerations and therefore, it must be balanced with proactive policies that address the challenges that emerge such as the need for data governance, algorithmic bias and risks associated with cybersecurity. A solution to overcome these challenges is establishing an adaptable regulatory framework, fostering workforce upskilling and promoting international collaborations. As businesses integrate AI into core functions, it becomes necessary to leverage its potential while safeguarding fairness, transparency, and trust. AI is not just an efficiency tool, it has become a stimulant for organisations operating in a rapidly evolving digital world.

References

• https://indianexpress.com/article/technology/artificial-intelligence/ai-indian-businesses-long-term-gain-operational-efficiency-9717072/ 
• https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html
• https://www.forbes.com/councils/forbestechcouncil/2024/08/06/smart-automation-ais-impact-on-operational-efficiency/ 
• https://www.processexcellencenetwork.com/ai/articles/ai-operational-excellence 
• https://www.leewayhertz.com/ai-for-operational-efficiency/ 
• https://www.forbes.com/councils/forbestechcouncil/2024/11/04/bringing-ai-to-the-enterprise-challenges-and-considerations/  

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Introduction

The increasing online interaction and popularity of social media platforms for netizens have made a breeding ground for misinformation generation and spread. Misinformation propagation has become easier and faster on online social media platforms, unlike traditional news media sources like newspapers or TV. The big data analytics and Artificial Intelligence (AI) systems have made it possible to gather, combine, analyse and indefinitely store massive volumes of data. The constant surveillance of digital platforms can help detect and promptly respond to false and misinformation content. 

During the recent Israel-Hamas conflict, there was a lot of misinformation spread on big platforms like X (formerly Twitter) and Telegram. Images and videos were falsely shared attributing to the ongoing conflict, and had spread widespread confusion and tension. While advanced technologies such as AI and big data analytics can help flag harmful content quickly, they must be carefully balanced against privacy concerns to ensure that surveillance practices do not infringe upon individual privacy rights. Ultimately, the challenge lies in creating a system that upholds both public security and personal privacy, fostering trust without compromising on either front.

The Need for Real-Time Misinformation Surveillance

According to a recent survey from the Pew Research Center, 54% of U.S. adults at least sometimes get news on social media. The top spots are taken by Facebook and YouTube respectively with Instagram trailing in as third and TikTok and X as fourth and fifth. Social media platforms provide users with instant connectivity allowing them to share information quickly with other users without requiring the permission of a gatekeeper such as an editor as in the case of traditional media channels. 

Keeping in mind the data dumps that generated misinformation due to the elections that took place in 2024 (more than 100 countries), the public health crisis of COVID-19, the conflicts in the West Bank and Gaza Strip and the sheer volume of information, both true and false, has been immense. Identifying accurate information amid real-time misinformation is challenging. The dilemma emerges as the traditional content moderation techniques may not be sufficient in curbing it. Traditional content moderation alone may be insufficient, hence the call for a dedicated, real-time misinformation surveillance system backed by AI and with certain human sight and also balancing the privacy of user's data, can be proven to be a good mechanism to counter misinformation on much larger platforms. The concerns regarding data privacy need to be prioritized before deploying such technologies on platforms with larger user bases. 

Ethical Concerns Surrounding Surveillance in Misinformation Control

Real-time misinformation surveillance could pose significant ethical risks and privacy risks. Monitoring communication patterns and metadata, or even inspecting private messages, can infringe upon user privacy and restrict their freedom of expression. Furthermore, defining misinformation remains a challenge; overly restrictive surveillance can unintentionally stifle legitimate dissent and alternate perspectives. Beyond these concerns, real-time surveillance mechanisms could be exploited for political, economic, or social objectives unrelated to misinformation control. Establishing clear ethical standards and limitations is essential to ensure that surveillance supports public safety without compromising individual rights. 

In light of these ethical challenges, developing a responsible framework for real-time surveillance is essential.

Balancing Ethics and Efficacy in Real-Time Surveillance: Key Policy Implications

Despite these ethical challenges, a reliable misinformation surveillance system is essential. Key considerations for creating ethical, real-time surveillance may include:

• Misinformation-detection algorithms should be designed with transparency and accountability in mind. Third-party audits and explainable AI can help ensure fairness, avoid biases, and foster trust in monitoring systems.
• Establishing clear, consistent definitions of misinformation is crucial for fair enforcement. These guidelines should carefully differentiate harmful misinformation from protected free speech to respect users’ rights.
• Only collecting necessary data and adopting a consent-based approach which protects user privacy and enhances transparency and trust. It further protects them from stifling dissent and profiling for targeted ads.
• An independent oversight body that can monitor surveillance activities while ensuring accountability and preventing misuse or overreach can be created. These measures, such as the ability to appeal to wrongful content flagging, can increase user confidence in the system.

Conclusion: Striking a Balance

Real-time misinformation surveillance has shown its usefulness in counteracting the rapid spread of false information online. But, it brings complex ethical challenges that cannot be overlooked such as balancing the need for public safety with the preservation of privacy and free expression is essential to maintaining a democratic digital landscape. The references from the EU’s Digital Services Act and Singapore’s POFMA underscore that, while regulation can enhance accountability and transparency, it also risks overreach if not carefully structured. Moving forward, a framework for misinformation monitoring must prioritise transparency, accountability, and user rights, ensuring that algorithms are fair, oversight is independent, and user data is protected. By embedding these safeguards, we can create a system that addresses the threat of misinformation and upholds the foundational values of an open, responsible, and ethical online ecosystem. Balancing ethics and privacy and policy-driven AI Solutions for Real-Time Misinformation Monitoring are the need of the hour.

References

• https://www.pewresearch.org/journalism/fact-sheet/social-media-and-news-fact-sheet/ 
• https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:C:2018:233:FULL 

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