#Fact Check: Pakistan’s Airstrike Claim Uses Video Game Footage

Introduction

The Telecom Regulatory Authority of India (TRAI), on March 13 2023, published a new rule to regulate telemarketing firms. Trai has demonstrated strictness when it comes to bombarding users with intrusive marketing pitches. In a report, TRAI stated that 10-digit mobile numbers could not be utilised for advertising. In reality, different phone numbers are given out for regular calls and telemarketing calls. Hence, it is an appropriate and much-required move in order to suppress and eradicate phishing scammers and secure the Indian Cyber-ecosystem at large.

What are the new rules?

The rules state that now 10-digit unregistered mobile numbers for promotional purposes would be shut down over the following five days. The rule claim that calling from unregistered mobile numbers had been banned was published on February 16. In this case, using 10-digit promotional messages for promotional calling will end within the following five days. This step by TRAI has been seen after nearly 6-8 months of releasing the Telecommunication Bill, 2022, which has focused towards creating a stable Indian Telecom market and reducing the phoney calls/messages by bad actors to reduce cyber crimes like phishing. This is done to distinguish between legitimate and promotional calls. According to certain reports, some telecom firms allegedly break the law by using 10-digit mobile numbers to make unwanted calls and send promotional messages. All telecom service providers must execute the requirements under the recent TRAI directive within five days.

How will the new rules help?

The promotional use of a cellphone number with 10 digits was allowed since the start, however, with the latest NCRB report on cyber crimes and the rising instances and reporting of cyber crimes primarily focused towards frauds related to monetary gains by the bad actors points to the issue of unregulated promotional messages. This move will act as a critical step towards eradicating scammers from the cyber-ecosystem, TRAI has been very critical in understanding the dynamics and shortcomings in the regulation of the telecom spectrum and network in India and has shown keen interest towards suppressing the modes of technology used by the scammers. It is a fact that the invention of the technology does not define its use, the policy of the technology does, hence it is important to draft ad enact policies which better regulate the existing and emerging technologies.

What to avoid?

In pursuance of the rules enacted by TRAI, the business owners involved in promotional services through 10-digit numbers will have to follow these steps-

• It is against the law to utilise a 10-digit cellphone number for promotional calls.
• You should stop doing so right now.
• Your mobile number will be blocked in the following five days if not.
• Users employed by telemarketing firms are encouraged to refrain from using the system in such circumstances.
• Those working for telemarketing firms are encouraged not to call from their mobile numbers.
• Users should phone the company’s registered mobile number for promotional purposes.

Conclusion

The Indian netizen has been exposed to the technology a little later than the western world. However, this changed drastically during the Covid-19 pandemic as the internet and technology penetration rates increased exponentially in just a couple of months. Although this has been used as an advantage by the bad actors, it was pertinent for the government and its institutions to take an effective and efficient step to safeguard the people from financial fraud. Although these frauds occur in high numbers due to a lack of knowledge and awareness, we need to work on preventive solutions rather than precautionary steps and the new rules by TRAI point towards a safe, secured and sustainable future of cyberspace in India.

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/

Introduction

The Senate bill introduced on 19 March 2024 in the United States would require online platforms to obtain consumer consent before using their data for Artificial Intelligence (AI) model training. If a company fails to obtain this consent, it would be considered a deceptive or unfair practice and result in enforcement action from the Federal Trade Commission (FTC) under the AI consumer opt-in, notification standards, and ethical norms for training (AI Consent) bill. The legislation aims to strengthen consumer protection and give Americans the power to determine how their data is used by online platforms. 

The proposed bill also seeks to create standards for disclosures, including requiring platforms to provide instructions to consumers on how they can affirm or rescind their consent. The option to grant or revoke consent should be made available at any time through an accessible and easily navigable mechanism, and the selection to withhold or reverse consent must be at least as prominent as the option to accept while taking the same number of steps or fewer as the option to accept.

The AI Consent bill directs the FTC to implement regulations to improve transparency by requiring companies to disclose when the data of individuals will be used to train AI and receive consumer opt-in to this use. 
The bill also commissions an FTC report on the technical feasibility of de-identifying data, given the rapid advancements in AI technologies, evaluating potential measures companies could take to effectively de-identify user data.

The definition of ‘Artificial Intelligence System’ under the proposed bill

ARTIFICIALINTELLIGENCE SYSTEM- The term artificial intelligence system“ means a machine-based system that—

1. Is capable of influencing the environment by producing an output, including predictions, recommendations or decisions, for a given set of objectives; and
2. 2. Uses machine or human-based data and inputs to

(i) Perceive real or virtual environments;

(ii) Abstract these perceptions into models through analysis in an automated manner (such as by using machine learning) or manually; and

(iii) Use model inference to formulate options for outcomes.

Importance of the proposed AI Consent Bill USA

1. Consumer Data Protection: The AI Consent bill primarily upholds the privacy rights of an individual. Consent is necessitated from the consumer before data is used for AI Training; the bill aims to empower individuals with unhinged autonomy over the use of personal information. The scope of the bill aligns with the greater objective of data protection laws globally, stressing the criticality of privacy rights and autonomy.

2. Prohibition Measures: The proposed bill intends to prohibit covered entities from exploiting the data of consumers for training purposes without their consent. This prohibition extends to the sale of data, transfer to third parties and usage. Such measures aim to prevent data misuse and exploitation of personal information. The bill aims to ensure companies are leveraged by consumer information for the development of AI without a transparent process of consent.

3. Transparent Consent Procedures: The bill calls for clear and conspicuous disclosures to be provided by the companies for the intended use of consumer data for AI training. The entities must provide a comprehensive explanation of data processing and its implications for consumers. The transparency fostered by the proposed bill allows consumers to make sound decisions about their data and its management, hence nurturing a sense of accountability and trust in data-driven practices.

4. Regulatory Compliance: The bill's guidelines call for strict requirements for procuring the consent of an individual. The entities must follow a prescribed mechanism for content solicitation, making the process streamlined and accessible for consumers. Moreover, the acquisition of content must be independent, i.e. without terms of service and other contractual obligations. These provisions underscore the importance of active and informed consent in data processing activities, reinforcing the principles of data protection and privacy.

5. Enforcement and Oversight: To enforce compliance with the provisions of the bill, robust mechanisms for oversight and enforcement are established. Violations of the prescribed regulations are treated as unfair or deceptive acts under its provisions. Empowering regulatory bodies like the FTC to ensure adherence to data privacy standards. By holding covered entities accountable for compliance, the bill fosters a culture of accountability and responsibility in data handling practices, thereby enhancing consumer trust and confidence in the digital ecosystem.

Importance of Data Anonymization

Data Anonymization is the process of concealing or removing personal or private information from the data set to safeguard the privacy of the individual associated with it. Anonymised data is a sort of information sanitisation in which data anonymisation techniques encrypt or delete personally identifying information from datasets to protect data privacy of the subject. This reduces the danger of unintentional exposure during information transfer across borders and allows for easier assessment and analytics after anonymisation. When personal information is compromised, the organisation suffers not just a security breach but also a breach of confidence from the client or consumer. Such assaults can result in a wide range of privacy infractions, including breach of contract, discrimination, and identity theft.

The AI consent bill asks the FTC to study data de-identification methods. Data anonymisation is critical to improving privacy protection since it reduces the danger of re-identification and unauthorised access to personal information. Regulatory bodies can increase privacy safeguards and reduce privacy risks connected with data processing operations by investigating and perhaps implementing anonymisation procedures.

The AI consent bill emphasises de-identification methods, as well as the DPDP Act 2023 in India, while not specifically talking about data de-identification, but it emphasises the data minimisation principles, which highlights the potential future focus on data anonymisation processes or techniques in India.

Conclusion

The proposed AI Consent bill in the US represents a significant step towards enhancing consumer privacy rights and data protection in the context of AI development. Through its stringent prohibitions, transparent consent procedures, regulatory compliance measures, and robust enforcement mechanisms, the bill strives to strike a balance between fostering innovation in AI technologies while safeguarding the privacy and autonomy of individuals.

References:

• https://fedscoop.com/consumer-data-consent-training-ai-models-senate-bill/#:~:text=%E2%80%9CThe%20AI%20CONSENT%20Act%20gives,Welch%20said%20in%20a%20statement
• https://www.dataguidance.com/news/usa-bill-ai-consent-act-introduced-house#:~:text=USA%3A%20Bill%20for%20the%20AI%20Consent%20Act%20introduced%20to%20House%20of%20Representatives,-ConsentPrivacy%20Law&text=On%20March%2019%2C%202024%2C%20US,the%20U.S.%20House%20of%20Representatives
• https://datenrecht.ch/en/usa-ai-consent-act-vorgeschlagen/
• https://www.lujan.senate.gov/newsroom/press-releases/lujan-welch-introduce-billto-require-online-platforms-receive-consumers-consent-before-using-their-personal-data-to-train-ai-models/

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