#FactCheck -Scripted Video of Pre-Wedding Roka at Metro Station Misleads Users

Introduction

With the increasing frequency and severity of cyber-attacks on critical sectors, the government of India has formulated the National Cyber Security Reference Framework (NCRF) 2023, aimed to address cybersecurity concerns in India. In today’s digital age, the security of critical sectors is paramount due to the ever-evolving landscape of cyber threats. Cybersecurity measures are crucial for protecting essential sectors such as banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises. This is an essential step towards safeguarding these critical sectors and preparing for the challenges they face in the face of cyber threats. Protecting critical sectors from cyber threats is an urgent priority that requires the development of robust cybersecurity practices and the implementation of effective measures to mitigate risks.

Overview of the National Cyber Security Policy 2013

The National Cyber Security Policy of 2013 was the first attempt to address cybersecurity concerns in India. However, it had several drawbacks that limited its effectiveness in mitigating cyber risks in the contemporary digital age. The policy’s outdated guidelines, insufficient prevention and response measures, and lack of legal implications hindered its ability to protect critical sectors adequately. Moreover, the policy should have kept up with the rapidly evolving cyber threat landscape and emerging technologies, leaving organisations vulnerable to new cyber-attacks. The 2013 policy failed to address the evolving nature of cyber threats, leaving organisations needing updated guidelines to combat new and sophisticated attacks.

As a result, an updated and more comprehensive policy, the National Cyber Security Reference Framework 2023, was necessary to address emerging challenges and provide strategic guidance for protecting critical sectors against cyber threats.

Highlights of NCRF 2023

• Strategic Guidance: NCRF 2023 has been developed to provide organisations with strategic guidance to address their cybersecurity concerns in a structured manner.
• Common but Differentiated Responsibility (CBDR): The policy is based on a CBDR approach, recognising that different organisations have varying levels of cybersecurity needs and responsibilities.
• Update of National Cyber Security Policy 2013: NCRF supersedes the National Cyber Security Policy 2013, which was due for an update to align with the evolving cyber threat landscape and emerging challenges.
• Different from CERT-In Directives: NCRF is distinct from the directives issued by the Indian Computer Emergency Response Team (CERT-In) published in April 2023. It provides a comprehensive framework rather than specific directives for reporting cyber incidents.
• Combination of robust strategies: National Cyber Security Reference Framework 2023 will provide strategic guidance, a revised structure, and a proactive approach to cybersecurity, enabling organisations to tackle the growing cyberattacks in India better and safeguard critical sectors.

Rising incidents of malware attacks on critical sectors

In recent years, there has been a significant increase in malware attacks targeting critical sectors. These sectors, including banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises, play a crucial role in the functioning of economies and the well-being of societies. The escalating incidents of malware attacks on these sectors have raised concerns about the security and resilience of critical infrastructure.

• Banking: The banking sector handles sensitive financial data and is a prime target for cybercriminals due to the potential for financial fraud and theft.
• Energy: The energy sector, including power grids and oil companies, is critical for the functioning of economies, and disruptions can have severe consequences for national security and public safety.
• Healthcare: The healthcare sector holds valuable patient data, and cyber-attacks can compromise patient privacy and disrupt healthcare services. Malware attacks on healthcare organisations can result in the theft of patient records, ransomware incidents that cripple healthcare operations, and compromise medical devices.
• Telecommunications: Telecommunications infrastructure is vital for reliable communication, and attacks targeting this sector can lead to communication disruptions and compromise the privacy of transmitted data. The interconnectedness of telecommunications networks globally presents opportunities for cybercriminals to launch large-scale attacks, such as Distributed Denial-of-Service (DDoS) attacks.
• Transportation: Malware attacks on transportation systems can lead to service disruptions, compromise control systems, and pose safety risks.
• Strategic Enterprises: Strategic enterprises, including defence, aerospace, intelligence agencies, and other sectors vital to national security, face sophisticated malware attacks with potentially severe consequences. Cyber adversaries target these enterprises to gain unauthorised access to classified information, compromise critical infrastructure, or sabotage national security operations.
• Government Enterprises: Government organisations hold a vast amount of sensitive data and provide essential services to citizens, making them targets for data breaches and attacks that can disrupt critical services.

Conclusion  

The sectors of banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises face unique vulnerabilities and challenges in the face of cyber-attacks. By recognising the significance of safeguarding these sectors, we can emphasise the need for proactive cybersecurity measures and collaborative efforts between public and private entities. Strengthening regulatory frameworks, sharing threat intelligence, and adopting best practices are essential to ensure our critical infrastructure’s resilience and security. Through these concerted efforts, we can create a safer digital environment for these sectors, protecting vital services and preserving the integrity of our economy and society. The rising incidents of malware attacks on critical sectors emphasise the urgent need for updated cybersecurity policy, enhanced cybersecurity measures, a collaboration between public and private entities, and the development of proactive defence strategies. National Cyber Security Reference Framework 2023 will help in addressing the evolving cyber threat landscape, protect critical sectors, fill the gaps in sector-specific best practices, promote collaboration, establish a regulatory framework, and address the challenges posed by emerging technologies. By providing strategic guidance, this framework will enhance organisations’ cybersecurity posture and ensure the protection of critical infrastructure in an increasingly digitised world.

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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As Generative AI continues to make strides by creating content through user prompts, the increasing sophistication of language models widens the scope of the services they can deliver. However, they have their own limitations. Recently, alerts by Apple Intelligence on the iPhone’s latest version have come under fire for misrepresenting news by news agencies.

The new feature was introduced with the aim of presenting an effective way to group and summarise app notifications in a single alert on a user’s lock screen. This was to enable an easier scan for important details amongst a large number of notifications, doing away with overwhelming updates for the user. This, however, resulted in the misrepresentation of news channels and reporting of fake news such as the arrest of Israeli Prime Minister Benjamin Netanyahu, Luke Litter winning the PDC World Darts Championship even before the competition, tennis Player Rafael Nadal coming out as gay, among other news alerts. Following false alerts, BBC had complained about its journalism being misrepresented. In response, Apple’s proposed solution was to clarify to the user that when the text summary is displayed in the notifications, it is clearly stated to be a product of notification Apple Intelligence and not of the news agency. It also claimed the complexity of having to compress content into short summaries which resulted in fallacious alerts. Further comments revealed that the AI alert feature was in beta and is continuously being worked on depending on the user’s feedback. Owing to the backlash, Apple has suspended this service and announced that an improved version of the feature is set to be released in the near future, however, no dates have been set.

CyberPeace Insights

The rush to release new features often exacerbates the problem, especially when AI-generated alerts are responsible for summarising news reports. This can significantly damage the credibility and trust that brands have worked hard to build. The premature release of features that affect the dissemination, content, and public comprehension of information carries substantial risks, particularly in the current environment where misinformation is widespread. Timely action and software updates, which typically require weeks to implement, are crucial in mitigating these risks. The desire to be ahead in the game and bring out competitive features must not resolve the responsibility of providing services that are secure and reliable. This aforementioned incident highlights the inherent nature of generative AI, which operates by analysing the data it was trained on to deliver the best possible responses based on user prompts. However, these responses are not always accurate or reliable. When faced with prompts beyond its scope, AI systems often produce untrustworthy information, underlining the need for careful oversight and verification. A question to deliberate on is whether we require such services at all, which in practice, do save our time, but do so at the risk of the spread of false tidbits.

References

• https://www.theguardian.com/technology/2025/jan/07/apple-update-ai-inaccurate-news-alerts-bbc-apple-intelligence-iphone
• https://www.firstpost.com/tech/apple-intelligence-hallucinates-falsely-credits-bbc-for-fake-news-broadcaster-lodges-complaint-13845214.html
• https://www.cnbc.com/2025/01/08/apple-ai-fake-news-alerts-highlight-the-techs-misinformation-problem.html
• https://news.sky.com/story/apple-ai-feature-must-be-revoked-over-notifications-misleading-users-say-journalists-13288716
• https://www.hindustantimes.com/world-news/apple-to-pay-95-million-in-user-privacy-violation-lawsuit-on-siri-101735835058198.html
• https://www.hindustantimes.com/business/apple-denies-claims-of-siri-violating-user-privacy-after-95-million-class-action-suit-settlement-101736445941497.html#:~:text=Apple%20denies%20claims%20of%20Siri,action%20suit%20settlement%20%2D%20Hindustan%20Times
• https://www.google.com/search?q=apple+AI+alerts+misinformation&oq=apple+AI+alerts+misinformation+&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigATIHCAMQIRigATIHCAQQIRigAdIBCTEyMzUxajBqN6gCALACAA&sourceid=chrome&ie=UTF-8
• https://www.fastcompany.com/91261727/apple-intelligence-news-summaries-mistakes
• https://timesofindia.indiatimes.com/technology/tech-news/siris-secret-listening-costs-apple-95m/articleshow/116906209.cms
• https://www.theguardian.com/technology/2025/jan/17/apple-suspends-ai-generated-news-alert-service-after-bbc-complaint 

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