#FactCheck:Fake Poster Falsely Claims ₹699 Prize Offer on PM Modi’s Birthday

Introduction

The ongoing armed conflict between Israel and Hamas/ Palestine is in the news all across the world. The latest conflict was triggered by unprecedented attacks against Israel by Hamas militants on October 7, killing thousands of people. Israel has launched a massive counter-offensive against the Islamic militant group. Amid the war, the bad information and propaganda spreading on various social media platforms, tech researchers have detected a network of 67 accounts that posted false content about the war and received millions of views. The ‘European Commission’ has sent a letter to Elon Musk, directing them to remove illegal content and disinformation; otherwise, penalties can be imposed. The European Commission has formally requested information from several social media giants on their handling of content related to the Israel-Hamas war. This widespread disinformation impacts and triggers the nature of war and also impacts the world and affects the goodwill of the citizens. The bad group, in this way, weaponise the information and fuels online hate activity, terrorism and extremism, flooding political polarisation with hateful content on social media. Online misinformation about the war is inciting extremism, violence, hate and different propaganda-based ideologies. The online information environment surrounding this conflict is being flooded with disinformation and misinformation, which amplifies the nature of war and too many fake narratives and videos are flooded on social media platforms.

Response of social media platforms 

As there is a proliferation of online misinformation and violent content surrounding the war, It imposes a question on social media companies in terms of content moderation and other policy shifts.  It is notable that Instagram, Facebook and X(Formerly Twitter) all have certain features in place giving users the ability to decide what content they want to view. They also allow for limiting the potentially sensitive content from being displayed in search results.

The experts say that It is of paramount importance to get a sort of control in this regard and define what is permissible online and what is not, Hence, what is required is expertise to determine the situation, and most importantly, It requires robust content moderation policies. 

During wartime, people who are aggrieved or provoked are often targeted by this internet disinformation that blends ideological beliefs and spreads conspiracy theories and hatred. This is not a new phenomenon, it is often observed that disinformation-spreading groups emerged and became active during such war and emergency times and spread disinformation and propaganda-based ideologies and influence the society at large by misrepresenting the facts and planted stories. Social media has made it easier to post user-generated content without properly moderating it. However, it is a shared responsibility of tech companies, users, government guidelines and policies to collectively define and follow certain mechanisms to fight against disinformation and misinformation.  

Digital Services Act (DSA)

The newly enacted EU law, i.e. Digital Services Act, pushes various larger online platforms to prevent posts containing illegal content and also puts limits on targeted advertising. DSA enables to challenge the of illegal online content and also poses requirements to prevent misinformation and disinformation and ensure more transparency over what the users see on the platforms. Rules under the DSA cover everything from content moderation & user privacy to transparency in operations. DSA is a landmark EU legislation moderating online platforms.  Large tech platforms are now subject to content-related regulation under this new EU law ‘The Digital Services Act’, which also requires them to prevent the spread of misinformation and disinformation and overall ensure a safer online environment.

Indian Scenario

The Indian government introduced the Intermediary Guidelines (Intermediary Guidelines and Digital Media Ethics Code) Rules, updated in 2023 which talks about the establishment of a "fact check unit" to identify false or misleading online content. Digital Personal Data Protection, 2023 has also been enacted which aims to protect personal data. The upcoming Digital India bill is also proposed to be tabled in the parliament, this act will replace the current Information & Technology Act, of 2000. The upcoming Digital India bill can be seen as future-ready legislation to strengthen India’s current cybersecurity posture. It will comprehensively deal with the aspects of ensuring privacy, data protection, and fighting growing cyber crimes in the evolving digital landscape and ensuring a safe digital environment. Certain other entities including civil societies are also actively engaged in fighting misinformation and spreading awareness for safe and responsible use of the Internet. 

Conclusion:

The widespread disinformation and misinformation content amid the Israel-Hamas war showcases how user-generated content on social media shows you the illusion of reality. There is widespread misinformation, misleading content or posts on social media platforms, and misuse of new advanced AI technologies that even make it easier for bad actors to create synthetic media content. It is also notable that social media has connected us like never before. Social media is a great platform with billions of active social media users around the globe, it offers various conveniences and opportunities to individuals and businesses. It is just certain aspects that require the attention of all of us to prevent the bad use of social media. The social media platforms and regulatory authorities need to be vigilant and active in clearly defining and improving the policies for content regulation and safe and responsible use of social media which can effectively combat and curtail the bad actors from misusing social media for their bad motives. As a user, it's the responsibility of users to exercise certain duties and promote responsible use of social media. With the increasing penetration of social media and the internet, misinformation is rampant all across the world and remains a global issue which needs to be addressed properly by implementing strict policies and adopting best practices to fight the misinformation. Users are encouraged to flag and report misinformative or misleading content on social media and should always verify it from authentic sources. Hence creating a safer Internet environment for everyone.

References:

• https://abcnews.go.com/US/experts-fear-hate-extremism-social-media-israel-hamas-war/story?id=104221215
• https://edition.cnn.com/2023/10/14/tech/social-media-misinformation-israel-hamas/index.html
• https://www.nytimes.com/2023/10/13/business/israel-hamas-misinformation-social-media-x.html
• https://www.africanews.com/2023/10/24/fact-check-misinformation-about-the-israel-hamas-war-is-flooding-social-media-here-are-the//
• https://www.theverge.com/23845672/eu-digital-services-act-explained

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The Expanding Governance Challenge of Artificial Intelligence
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Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk. 

Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.

The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process. 

This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
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The Lifecycle of an AI System
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AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.

The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.

Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.

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Where AI Risks Emerge
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AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks:  bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence

Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms. 
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AI System Lifecycle: Key Risks at Each Stage
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Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
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Policy Tools for Mitigating AI Risks
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Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment. 

For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.

Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
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The Policy Gap in AI Governance
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The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.

An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment. 

Analysis
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1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.

2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment. 

3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems. 

4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
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Recommendations
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1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.

2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.

3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.

4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
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Conclusion - The Need for Global AI Governance
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Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.

Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
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References
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• OECD AI lifecycle
• OECD AI system lifecycle description
• OECD AI governance lifecycle framework
• EU AI Act overview
• EU AI Act risk categories
• UNESCO Recommendation on the Ethics of AI
• AI governance lifecycle analysis
• OECD AI policy tools database

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Introduction:

CDR is a term that refers to Call detail records, The Telecom Industries holds the call details data of the users. As it amounts to a large amount of data, the telecom companies retain the data for a period of 6 months. CDR plays a significant role in investigations and cases in the courts. It can be used as pivotal evidence in court proceedings to prove or disprove certain facts & circumstances. Power of Interception of Call detail records is allowed for reasonable grounds and only by the authorized authority as per the laws. 

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Admissibility of CDR’s in Courts:

Call Details Records (CDRs) can be used as effective pieces of evidence to assist the court in ascertaining the facts of the particular case and inquiring about the commission of an offence, and according to the judicial pronouncements, it is made clear that CDRs can be used supporting or secondary evidence in the court. However, it cannot be the sole basis of the conviction. Section 92 of the Criminal Procedure Code 1973 provides procedure and empowers certain authorities to apply for court or competent authority intervention to seek the CDR. 

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Legal provisions to obtain CDR:

The CDR can be obtained under the statutory provisions of law contained in section 92 Criminal Procedure Code, 1973. Or under section 5(2) of Indian Telegraph Act 1885, read with rule 419(A) Indian Telegraph Amendment rule 2007. The guidelines were also issued in 2016 by Ministry of Ministry of Home Affairs for seeking Call details records (CDRs)

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How long is CDR stored with telecom Companies (Data Retention)

Call Data is retained by telecom companies for a period of 6 months. As the data amounts to high storage, almost several Petabytes per year, telecom companies store the call details data for a period of 6 months and archive the rest of it to tapes. 

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New Delhi 25Cr jewellery heist 

Recently, an incident took place where a 25-crore jewellery theft was carried out in a jewellery shop in Delhi, It was planned and executed by a man from Chhattisgarh. After committing the crime, the criminal went back to Chhattisgarh. It was a case of a 25Cr heist, and the police started their search & investigation. Police used technology and analysed the mobile numbers which were active at the crime scene. Delhi police used advanced software to analyse data. The police were able to trace the mobile number of thieves or suspects active at the crime scene. They discovered suspected contacts who were active within the range of the crime scene, and it helped in the arrest of the main suspects.  From around 5,000 mobile numbers active around the crime scene, police have used advanced software that analyses huge data, and then police found a number registered outside of Delhi.  The surveillance on the number has revealed that the suspected criminal has moved to the MP from Delhi, then moved further to Bhilai Chattisgarh. Police have successfully arrested the suspected criminal. This incident highlights how technology or call data can assist law enforcement agencies in investigating and finding the real culprits.

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Conclusion: 

CDR refers to call detail records retained by telecom companies for a period of 6 months, it can be obtained through lawful procedure and by competent authorities only. CDR can be helpful in cases before the court or law enforcement agencies, to assist the court and law enforcement agencies in ascertaining the facts of the case or to prove or disprove certain things. It is important to reiterated that unauthorized seeking of CDR is not allowed; the intervention of the court or competent authority is required to seek the CDR from the telecom companies. CDRs cannot be unauthorizedly obtained, and there has to be a directive from the court or competent authority to do so.

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References:

• https://indianlegalsystem.org/cdr-the-wonder-word/#:~:text=CDR%20is%20admissible%20as%20secondary,the%20Indian%20Evidence%20Act%2C%201872.
• https://timesofindia.indiatimes.com/city/delhi/needle-in-a-haystack-how-cops-scanned-5k-mobile-numbers-to-crack-rs-25cr-heist/articleshow/104055687.cms?from=mdr
• https://www.ndtv.com/delhi-news/just-one-man-planned-executed-rs-25-crore-delhi-heist-another-thief-did-him-in-4436494

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