#FactCheck- Viral ‘Prison Torture’ Video Not from Israel, Taken from Iraqi TV Show

Introduction

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India is making strides in developing its own quantum communication capabilities, despite being a latecomer compared to nations like China and the US. In the digital age, quantum communication is gradually becoming one of the most important technologies for national security. It promises to transform secure data exchange across government, financial, and military systems by enabling unhackable communication channels through quantum concepts like entanglement and superposition.  Scientists from the Defence Research and Development Organisation (DRDO) and IIT Delhi recently demonstrated quantum communication over a distance of over one kilometre in free space. One significant step at a time, India's quantum roadmap is beginning to take shape thanks to strategic partnerships between top research institutes and defence organisations.

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Recent Developments

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• In February 2022, by DRDO and IIT Delhi, a 100 km Quantum Key Distribution (QKD) link was established between Prayagraj and Vindhyachal using pre-existing commercial-grade optical fibre, with secure key rates of up to 10 kHz. This proved that using India's current telecom infrastructure to implement quantum-secure communication is feasible.
• Scientists at DRDO finished testing a 6-qubit superconducting quantum processor in August 2024, showing complete system integration by submitting quantum circuits through a cloud interface, running them on quantum hardware, and updating the results. 
• A free-space QKD demonstration over over 1 km was conducted in June 2025, with a secure key rate of approximately 240 bits/s and a Quantum Bit Error Rate (QBER) of less than 7%. A crucial step towards satellite-based and defence-grade secure networks, this successful outdoor trial demonstrates that quantum-secure communication is now feasible in actual atmospheric conditions.

• India is looking to space as well. Since 2017, the Raman Research Institute (RRI) and ISRO have been collaborating on satellite-based QKD, with funding totalling more than ₹15 crore. In 2025, a specialised QKD-enabled satellite called SAQTI (Secured Applications using Quantum and optical Technologies by ISRO) is anticipated to go into orbit. The initiative's foundation has already been established by ground-based quantum encryption trials up to 300 meters.
• In India, private companies such as QNu Labs are assisting in the commercialisation of quantum communication. QNu, which was founded at IIT Madras, has created the plug-and-play QKD module Armos, the quantum random number generator (QRNG)Tropos, and the integrated platform QShield, which combines QKD, QRNG, and post-quantum cryptography (PQC). 

Where India Stands Globally

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India is still in its infancy when compared to China's 2,000 km Beijing–Shanghai QKD network and its satellite-based communication accomplishments. Leading nations like the US, UK, and Singapore are also ahead of the curve, concentrating on operationalising QKD trials for government systems and incorporating post-quantum cryptography (PQC) into national infrastructure.

However, considering the nation's limited prior exposure to quantum technologies, India's progress is noteworthy for its rapid pace and indigenous innovation.

Policy Challenges and Priorities

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• Strong policy support is required to match India's efforts in quantum communication. The standardisation of PQC algorithms and their incorporation into digital public infrastructure have to be major priorities.
• Scaling innovation from lab to deployment through public-private partnership
• Accelerating satellite QKD to establish a secure communications ecosystem owned by India.
• International standards compliance and worldwide interoperability for secure quantum protocols.

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Conclusion

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India has made timely strides in quantum communication, spearheaded by DRDO, IITs, and ISRO. Establishing unbreakable communication systems will be essential to national security as digital infrastructure becomes more and more integrated into governance and economic life. India can establish itself as a significant player in the developing quantum-secure world with consistent investment, well-coordinated policy, and international collaboration.

References

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• https://www.thehindu.com/sci-tech/science/quantum-communication-iit-delhi-drdo-entanglement-qkd-explained/article69705017.ece 
• https://drdo.gov.in/drdo/quantum-technologies 
• https://www.indiatoday.in/science/story/the-end-of-hacking-how-isro-and-drdo-are-building-an-unhackable-quantum-future-2743715-2025-06-22 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2136702 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=1800648 
• https://thequantuminsider.com/2024/08/29/indias-drdo-scientists-complete-testing-of-6-qubit-superconducting-quantum-processor/ 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2077600 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2121617 
• https://www.rri.res.in/news/quic-lab-achieves-next-step-towards-realising-secure-satellite-based-quantum-communication#:~:text=QuIC%20lab%20achieves%20the%20next,transactions%2Dsafe%2D2561836.html 
• https://www.gsma.com/newsroom/post-quantum-government-initiatives-by-country-and-region/ 
• https://tech.hindustantimes.com/tech/news/rri-demonstrates-secure-satellite-based-quantum-communication-in-collaboration-with-isro-71680375748247.html 

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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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Executive Summary: 

A viral post on X (formerly Twitter) has been spreading misleading captions about a video that falsely claims to depict severe wildfires in Los Angeles similar to the real wildfire happening in Los Angeles. Using AI Content Detection tools we confirmed that the footage shown is entirely AI-generated and not authentic. In this report, we’ll break down the claims, fact-check the information, and provide a clear summary of the misinformation that has emerged with this viral clip.

Claim:

A video shared across social media platforms and messaging apps alleges to show wildfires ravaging Los Angeles, suggesting an ongoing natural disaster.

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Fact Check:

After taking a close look at the video, we noticed some discrepancy such as the flames seem unnatural, the lighting is off, some glitches etc. which are usually seen in any AI generated video. Further we checked the video with an online AI content detection tool hive moderation, which says the video is AI generated, meaning that the video was deliberately created to mislead viewers. It’s crucial to stay alert to such deceptions, especially concerning serious topics like wildfires. Being well-informed allows us to navigate the complex information landscape and distinguish between real events and falsehoods.

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Conclusion: 

This video claiming to display wildfires in Los Angeles is AI generated, the case again reflects the importance of taking a minute to check if the information given is correct or not, especially when the matter is of severe importance, for example, a natural disaster. By being careful and cross-checking of the sources, we are able to minimize the spreading of misinformation and ensure that proper information reaches those who need it most.

• Claim: The video shows real footage of the ongoing wildfires in Los Angeles, California
• Claimed On: X (Formerly Known As Twitter)
• Fact Check: Fake Video