#FactCheck - "Deep fake Falsely Claimed as a photo of Arvind Kejriwal welcoming Elon Musk when he visited India to discuss Delhi’s administrative policies.”

Introduction

Your iPhone isn’t just a device: it’s a central hub for almost everything in your life. From personal photos and videos to sensitive data, it holds it all. You rely on it for essential services, from personal to official communications, sharing of information, banking and financial transactions, and more. With so much critical information stored on your device, protecting it from cyber threats becomes essential. This is where the iOS Lockdown Mode feature comes in as a digital bouncer to keep cyber crooks at bay.

Apple introduced the ‘lockdown’ mode in 2022. It is a new optional security feature and is available on iPhones, iPads, and Mac devices. It works as an extreme and optional protection mechanism for a certain segment of users who might be at a higher risk of being targeted by serious cyber threats and intrusions into their digital security.  So people like journalists, activists, government officials, celebrities, cyber security professionals, law enforcement professionals, and lawyers etc are some of the intended beneficiaries of the feature. Sometimes the data on their devices can be highly confidential and it can cause a lot of disruption if leaked or compromised by cyber threats. Given how prevalent cyber attacks are in this day and age, the need for such a feature cannot be overstated. This feature aims at providing an additional firewall by limiting certain functions of the device and hence reducing the chances of the user being targeted in any digital attack. 

How to Enable Lockdown Mode in Your iPhone

On your iPhone running on iOS 16 Developer Beta 3, you just need to go to Settings - Privacy and Security - Lockdown Mode. Tap on Turn on Lockdown Mode, and read all the information regarding the features that will be unavailable on your device if you go forward, and if you’re satisfied with the same all you have to do is scroll down and tap on Turn on Lockdown Mode. Your iPhone will get restarted with Lockdown Mode enabled. 

Easy steps to enable lockdown mode are as follows:

1. Open the Settings app.
2. Tap Privacy & Security.
3. Scroll down, tap Lockdown Mode, then tap Turn On Lockdown Mode.

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How Lockdown Mode Protects You

Lockdown Mode is a security feature that prevents certain apps and features from functioning properly when enabled. For example, your device will not automatically connect to Wi-Fi networks without security and will disconnect from a non-secure network when Lockdown Mode is activated. Many other features may be affected because the system will prioritise security standards above the typical operational functions. Since lockdown mode restricts certain features and activities, one can exclude a particular app or website in Safari from being impacted and limited by restrictions. Only exclude trusted apps or websites if necessary.

References:

1. https://support.apple.com/en-in/105120#:~:text=Tap%20Privacy%20%26%20Security.,then%20enter%20your%20device%20passcode
2. https://www.business-standard.com/technology/tech-news/apple-lockdown-mode-what-is-it-and-how-it-prevents-spyware-attacks-124041200667_1.html

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Introduction

In the age of social media, the news can spread like wildfire. A recent viral claim contained that police have started a nationwide scheme of free travel service for women at night. It stated that any woman who is alone and cannot find a vehicle to go home between 10 PM and 06 AM can contact the provided numbers and request a free vehicle. The viral message further contained the request to share and forward this information to everyone to get the women to know about the free vehicle service offered by police at night. However, upon fact check the claim was found to be misleading.

Social Impact of Misleading Information

The fact that such misleading information gets viral at a fast speed is because of its ability to impact and influence people through emotional resonance. Especially during a time when women's safety is a topic discussed in media sensationalism due to recently highlighted rape or sexual violence incidents, such fake viral claims often spark widespread public concern, causing emotional resonance to people and they unknowingly share or forward such messages in the spike of emotional and sensational appeal contained in such messages. The emotional nature of these viral texts often overrides scepticism, leading to immediate sharing without verification. 

Such nature of viral messages often tends to bring people to protest, raise awareness and create support networks, but in spite of emotional resonance people get targeted by misinformation and become the unintended superspreaders of fake news fueled by emotional and social media-driven reactions. Women’s safety in society is a sensitive topic and when people discover such viral claims to be misleading and fake, it often hurts the sentiments of society leading to significant social impacts, including distrust in social media, unnecessary panic and confusion.

CyberPeace Policy Vertical Advisory for Social Media Users

1. Think before Sharing: All netizens must practice caution while sharing anything and double-check its authenticity before sharing/forwarding or reposting it on your social media stories.
2. Don't be unintended superspreaders of Misinformation: Misinformation with emotional resonance and widespread sharing by netizens can lead to them becoming "superspreaders of misinformation" and making it viral quickly. Hence you must avoid such unintended consequences by following the best practices of being vigilant and informed by reliable sources. 
3. Exercise vigilance and scepticism: It is important that netizens exercise vigilance and they build cognitive abilities to recognise the red flags of misleading information. You can do so by following the official communication channels, looking for any discrepancy in the content of susceptible information and double-checking its authenticity before sharing it with anyone. 
4. Verify the information from official sources: Follow the official communication channels of concerned authorities for any kind of information, circulars, notifications etc. In case of finding any piece of information to be susceptible or misleading, intimate it to the relevant authority and the fact-checking organizations. 
5. Stay in touch with expert organizations: Cybersecurity experts and civil society organisations possess the unique blend of large-scale impact potential and technical expertise. Netizens can stay updated about recent developments in the tech-policy sphere and learn about internet best practices, and measures to counter misinformation through methods such as prebunking, debunking and more. 

Connect with CyberPeace

As an expert organisation, we have the ability to educate and empower huge numbers, along with the skills and policy acumen needed to be able to not just make people aware of the problem but also teach them how to solve it for themselves. At CyberPeace we regularly produce fact-check reports, blogs & advisories, and insights on prebunking & debunking measures and capacity-building programs with the aim of empowering netizens at the heart of our initiatives. CyberPeace has established the largest network of CyberPeace Corps volunteers globally. These volunteers play a crucial role in assisting victims, raising awareness, and promoting proactive measures. 

References: 

1. indiatvnews.com/fact-check/fact-check-are-police-providing-free-travel-services-to-women-at-night-know-truth-of-viral-claim-2024-08-28-949280#:~:text=What%20came%20out%20in%20fact,be%20cautious%20of%20these%20claims

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Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/