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

The Digital Personal Data Protection (DPDP) Act, 2023, operationalises data privacy largely through a consent management framework. It aims to give data principles, ie, individuals, control over their personal data by giving them the power to track, change, and withdraw their consent from its processing. However, in practice, consent management is often not straightforward. For example, people may be frequently bombarded with requests, which can lead to fatigue and eventual overlooking of consent requests. This article discusses the way consent management is handled by the DPDP Act, and looks at how India can design the system to genuinely empower users while holding organisations accountable.

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Consent Management in the DPDP Act

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According to the DPDP Act, consent must be unambiguous, free, specific, and informed. It must also be easy for people to revoke their consent (DPO India, 2023). To this end, the Act creates Consent Managers- registered middlemen- who serve as a link between users and data custodians. 

The purpose of consent managers is to streamline and centralise the consent procedure. Users can view, grant, update, or revoke consent across various platforms using the dashboards they offer. They hope to improve transparency and lessen the strain on people to keep track of permissions across different services by standardising the way consent is presented (IAPP, 2024).

The Act draws inspiration from international frameworks such as the GDPR (General Data Protection Regulation), mandating that Indian users be provided with a single platform to manage permissions rather than having to deal with dispersed consent prompts from every service.

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The Challenges 

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Despite the mandate for an interoperable platform for consent management, several key challenges emerge. There is a lack of clarity on how consent management will be operationalised. This creates challenges of accountability and implementation.  Thus, : 

1. If the interface is poorly designed,  users could be bombarded with content permissions from apps/platforms/ services that are not fully compliant with the platform. 
2. If consent notices are vague, frequent, lengthy, or complex, users may continue to grant permissions without meaningful engagement. 
3. It leaves scope for data fiduciaries to use dark patterns to coerce customers into granting consent through poor UI/UX design.
4. The lack of clear, standardised interoperability protocols across sectors could lead to a fragmented system, undermining the goal of a single, easy-to-use platform.
5. Consent fatigue could easily appear in India's digital ecosystem, where apps, e-commerce websites, and government services all ask for permissions from over 950 million internet subscribers. Experiences from GDPR countries show that users who are repeatedly prompted eventually become banner blind, which causes them to ignore notices entirely.
6. Low levels of literacy (including digital literacy) and unequal access to digital devices among women and marginalised communities create complexities in the substantive coverage of privacy rights.  
7. Placing the burden of verification of legal guardianship for children and persons with disabilities (PwDs) on data fiduciaries might be ineffective, as SMEs may lack the resources to undertake this activity. This could create new forms of vulnerability for the two groups. 

Legal experts claim that this results in what they refer to as a legal fiction, wherein consent is treated as valid by the law despite the fact that it does not represent true understanding or choice (Lawvs, 2023). Additionally, research indicates that users hardly ever read privacy policies in their entirety. People are very likely to tick boxes without fully understanding what they are agreeing to. By drastically limiting user control, this has a bearing on the privacy rights of Indian citizens and residents.  (IJLLR, 2023).

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Impacts of Weak Consent Management:

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According to the Indian Journal of Law and Technology, in an era of asymmetry and information overload, privacy cannot be sufficiently protected by relying only on consent (IJLT, 2023). Almost every individual will be impacted by inadequate consent management.

1. For Users: True autonomy is replaced by the appearance of control. Individuals may unintentionally disclose private information, which undermines confidence in digital services.
2. For Businesses: Compliance could become a mere formality. Further, if acquired consent is found to be manipulated or invalid, it creates space for legal risks and reputational damage. 
3. For Regulators: It becomes difficult to oversee a system where consent is frequently disregarded or misinterpreted. When consent is merely formal, the law's promise to protect personal information is undermined.

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Way Forward

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1. Layered and Simplified Notices: Simple language and layers of visual cues should be used in consent requests. Important details like the type of data being gathered, its intended use, and its duration should be made clear up front. Additional explanations are available for users who would like more information. This method enhances comprehension and lessens cognitive overload (Lawvs, 2023).
2. Effective Dashboards: Dashboards from consent managers should be user-friendly, cross-platform, and multilingual. Management is made simple by features like alerts, one-click withdrawal or modification, and summaries of active permissions. The system is more predictable and dependable when all services use the same format, which also reduces confusion (IAPP, 2024). 
3. Dynamic and Contextual Consent: Instead of appearing as generic pop-ups, consent requests should show up when they are pertinent to a user's actions. Users can make well-informed decisions without feeling overburdened by subtle cues, such as emphasising risks when sensitive data is requested (IJLLR, 2023). 
4. Accountability of Consent Managers: Organisations that offer consent management services must be accountable and independent, through clear certification, auditing, and specific legal accountability frameworks. Even when formal consent is given, strong trustee accountability guarantees that data is not misused (IJLT, 2023).
5. Complementary Protections Beyond Consent: Consent continues to be crucial, but some high-risk data processing might call for extra protections. These may consist of increased responsibilities for fiduciaries or proportionality checks. These steps improve people's general protection and lessen the need for frequent consent requests (IJLLR, 2023).

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Conclusion

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The core of the DPDP Act is to empower users to have control over their data through measures such as consent management. But requesting consent is insufficient; the system must make it simple for people to manage, monitor, and change it. Effectively designed, managed, and executed consent management has the potential to revolutionise user experience and trust in India's digital ecosystem if it is implemented carefully.To make consent management genuinely meaningful, it is imperative to standardise procedures, hold fiduciaries accountable, simplify interfaces, and investigate supplementary protections.

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References

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Building Trust with Technology: Consent Management Under India’s DPDP Act, 2023

Consent Fatigue and Data Protection Laws: Is ‘Informed Consent’ a Legal Fiction

Beyond Consent: Enhancing India's Digital Personal Data Protection Framework

Constitutional Implications Of Consent Fatigue In Data Protection: Rethinking Meaningful Consent In The Indian Context 

Top 10 operational impacts of India’s DPDPA – Consent management

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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/

Introduction

Deepfake technology, which combines the words "deep learning" and "fake," uses highly developed artificial intelligence—specifically, generative adversarial networks (GANs)—to produce computer-generated content that is remarkably lifelike, including audio and video recordings. Because it can provide credible false information, there are concerns about its misuse, including identity theft and the transmission of fake information. Cybercriminals leverage AI tools and technologies for malicious activities or for committing various cyber frauds. By such misuse of advanced technologies such as AI, deepfake, and voice clones. Such new cyber threats have emerged. 

India Topmost destination for deepfake attacks

According to Sumsub’s identity fraud report 2023, a well-known digital identity verification company with headquarters in the UK. India, Bangladesh, and Pakistan have become an important participants in the Asia-Pacific identity fraud scene with India’s fraud rate growing exponentially by 2.99% from 2022 to 2023. They are among the top ten nations most impacted by the use of deepfake technology. Deepfake technology is being used in a significant number of cybercrimes, according to the newly released Sumsub Identity Fraud Report for 2023, and this trend is expected to continue in the upcoming year. This highlights the need for increased cybersecurity awareness and safeguards as identity fraud poses an increasing concern in the area. 

How Deeepfake Works 

Deepfakes are a fascinating and worrisome phenomenon that have emerged in the modern digital landscape. These realistic-looking but wholly artificial videos have become quite popular in the last few months. Such realistic-looking, but wholly artificial, movies have been ingrained in the very fabric of our digital civilisation as we navigate its vast landscape. The consequences are enormous and the attraction is irresistible.  

Deep Learning Algorithms

Deepfakes examine large datasets, frequently pictures or videos of a target person, using deep learning techniques, especially Generative Adversarial Networks. By mimicking and learning from gestures, speech patterns, and facial expressions, these algorithms can extract valuable information from the data. By using sophisticated approaches, generative models create material that mixes seamlessly with the target context. Misuse of this technology, including the dissemination of false information, is a worry. Sophisticated detection techniques are becoming more and more necessary to separate real content from modified content as deepfake capabilities improve.

Generative Adversarial Networks

Deepfake technology is based on GANs, which use a dual-network design. Made up of a discriminator and a generator, they participate in an ongoing cycle of competition. The discriminator assesses how authentic the generated information is, whereas the generator aims to create fake material, such as realistic voice patterns or facial expressions. The process of creating and evaluating continuously leads to a persistent improvement in Deepfake's effectiveness over time. The whole deepfake production process gets better over time as the discriminator adjusts to become more perceptive and the generator adapts to produce more and more convincing content. 

 Effect on Community

The extensive use of Deepfake technology has serious ramifications for several industries. As technology develops, immediate action is required to appropriately manage its effects. And promoting ethical use of technologies. This includes strict laws and technological safeguards. Deepfakes are computer trickery that mimics prominent politicians' statements or videos. Thus, it's a serious issue since it has the potential to spread instability and make it difficult for the public to understand the true nature of politics. Deepfake technology has the potential to generate totally new characters or bring stars back to life for posthumous roles in the entertainment industry. It gets harder and harder to tell fake content from authentic content, which makes it simpler for hackers to trick people and businesses.

Ongoing Deepfake Assaults In India

Deepfake videos continue to target popular celebrities, Priyanka Chopra is the most recent victim of this unsettling trend. Priyanka's deepfake adopts a different strategy than other examples including actresses like Rashmika Mandanna, Katrina Kaif, Kajol, and Alia Bhatt. Rather than editing her face in contentious situations, the misleading film keeps her look the same but modifies her voice and replaces real interview quotes with made-up commercial phrases. The deceptive video shows Priyanka promoting a product and talking about her yearly salary, highlighting the worrying development of deepfake technology and its possible effects on prominent personalities.

Actions Considered by Authorities

A PIL was filed requesting the Delhi High Court that access to websites that produce deepfakes be blocked. The petitioner's attorney argued in court that the government should at the very least establish some guidelines to hold individuals accountable for their misuse of deepfake and AI technology. He also proposed that websites should be asked to identify information produced through AI as such and that they should be prevented from producing illegally. A division bench highlighted how complicated the problem is and suggested the government (Centre) to arrive at a balanced solution without infringing the right to freedom of speech and expression (internet).  

Information Technology Minister Ashwini Vaishnaw stated that new laws and guidelines would be implemented by the government to curb the dissemination of deepfake content. He presided over a meeting involving social media companies to talk about the problem of deepfakes. "We will begin drafting regulation immediately, and soon, we are going to have a fresh set of regulations for deepfakes. this might come in the way of amending the current framework or ushering in new rules, or a new law," he stated.

Prevention and Detection Techniques

To effectively combat the growing threat posed by the misuse of deepfake technology, people and institutions should place a high priority on developing critical thinking abilities, carefully examining visual and auditory cues for discrepancies, making use of tools like reverse image searches, keeping up with the latest developments in deepfake trends, and rigorously fact-check reputable media sources. Important actions to improve resistance against deepfake threats include putting in place strong security policies, integrating cutting-edge deepfake detection technologies, supporting the development of ethical AI, and encouraging candid communication and cooperation. We can all work together to effectively and mindfully manage the problems presented by deepfake technology by combining these tactics and adjusting the constantly changing terrain.

Conclusion

Advanced artificial intelligence-powered deepfake technology produces extraordinarily lifelike computer-generated information, raising both creative and moral questions. Misuse of tech or deepfake presents major difficulties such as identity theft and the propagation of misleading information, as demonstrated by examples in India, such as the latest deepfake video involving Priyanka Chopra. It is important to develop critical thinking abilities, use detection strategies including analyzing audio quality and facial expressions, and keep up with current trends in order to counter this danger. A thorough strategy that incorporates fact-checking, preventative tactics, and awareness-raising is necessary to protect against the negative effects of deepfake technology. Important actions to improve resistance against deepfake threats include putting in place strong security policies, integrating cutting-edge deepfake detection technologies, supporting the development of ethical AI, and encouraging candid communication and cooperation. We can all work together to effectively and mindfully manage the problems presented by deepfake technology by combining these tactics and making adjustments to the constantly changing terrain. Creating a true cyber-safe environment for netizens. 

References:

• https://yourstory.com/2023/11/unveiling-deepfake-technology-impact
• https://www.indiatoday.in/movies/celebrities/story/deepfake-alert-priyanka-chopra-falls-prey-after-rashmika-mandanna-katrina-kaif-and-alia-bhatt-2472293-2023-12-05
• https://www.csoonline.com/article/1251094/deepfakes-emerge-as-a-top-security-threat-ahead-of-the-2024-us-election.html
• https://timesofindia.indiatimes.com/city/delhi/hc-unwilling-to-step-in-to-curb-deepfakes-delhi-high-court/articleshow/105739942.cms
• https://www.indiatoday.in/india/story/india-among-top-targets-of-deepfake-identity-fraud-2472241-2023-12-05
• https://sumsub.com/fraud-report-2023/