PM IAS EDITORIAL ANALYSIS JULY 03

Editorial 1 : On improving rural mobile connectivity

Context

According to the latest Telecom Subscription Data, urban tele-density in the country is 127% while rural tele-density is 58%.

About

• Mobile devices have become an integral part of our lives. We use them to communicate with our friends and family, conduct financial transactions through UPI, connect to the Internet, etc.
• The connectivity for these devices is enabled via a cellular (mobile) wireless network.
•  A cellular network, such as a 5G network, includes a set of network equipment connected by communication links.
• They work together to move data between different devices and to other networks such as the Internet.
• A cellular network can be divided into two sub-networks: the Access Network (AN) and the Core Network (CN).

What are access and core networks?

• The AN consists of base stations that provide wireless connectivity to mobile devices in a limited geographical area, called the coverage area.
• A network operator usually installs base stations across the length and breadth of the region to be covered. These stations can be seen in the form of towers with boxes with antennae on top.
• The CN of a cellular network has equipment that provides connectivity to other networks, such as the Internet.
• Unlike AN base stations, the CN operates in a central location, and possibly far from any of the base stations.
• The CN is linked to a base station by an optical fibre link called the backhaul.
• Data from a user’s device must pass through both a base station and the CN to reach its desired destination, such as the Internet or another user’s device.
• Even if two users are nearby and are connected to the same or adjacent base stations, the data must pass through the central CN. The CN is essential to support user mobility, a key feature offered by cellular networks.

What impedes rural connectivity?

• Even though cellular networks seem omnipresent, their deployment and use vary significantly between urban and rural areas. This is especially true in developing countries like India.
• According to the latest Telecom Subscription Data from the Telecom Regulatory Authority of India, urban tele-density in the country is 127% while rural tele-density is 58%.
• Put another way, on average, an urban user has one or more mobile connections (1.27) whereas only one out of two rural users (0.58) is connected. This data suggests an urban-rural digital divide.
• An important factor impeding the deployment and/or use of cellular networks in rural areas is the relatively lower income of the people here.
• A big chunk of the rural population finds mobile services unaffordable. Other relevant characteristics of rural areas are lower population density, populations distributed in clusters (villages) often separated by vast empty spaces, and remoteness.
• Taking fibre infrastructure to a far-off village, say, in the Himalayas, to connect the base station there may neither be cost-effective nor easy.
• These features of the rural landscape require a communication system that can efficiently cover a large geographical area — yet there has been limited research focus on these factors.
• Most existing cellular networks cater to urban populations in economically developed countries, for example, the 5G network focuses on providing 10 Gbps data rate and 1 ms latency. Rural connectivity lags far behind.

What is the IEEE 2061-2024 standard?

• Affordable rural connectivity form the basis of the 2061-2024 standard.
• The standard defines a wireless network architecture for affordable broadband access in rural areas.
• The IEEE-2061 network also includes a CN and AN similar to cellular networks.
• However, the IEEE-2061 AN is heterogenous wherein different types of base stations coexist: it includes base stations covering large coverage areas — called macro-BS — supplemented by small coverage area Wi-Fi.
•  It is different from the 5G network, where the AN is homogeneous comprising base stations of the same type and typically smaller coverage area.
• The macro-BS in IEEE-2061 can be built with any cellular technology that can support a large coverage area.
• While the macro-BS provides large-area coverage but possibly lower data rate, Wi-Fi is deployed within villages to provide high-speed connectivity.
• A key capability of the system is that it allows a device to move from a Wi-Fi based connectivity to a macro-BS connectivity without any service disruption.
• This is enabled by an integrated AN control functionality in the IEEE-2061 network.
• As wireless systems evolve, both legacy and new technologies — including 4G, 5G, 6G, Wi-Fi and networks — will coexist and complement each other.
• In such a heterogenous network, an integrated AN control functionality like the one included in the IEEE-2061 standard will help avoid issues like call drops.

What is a middle-mile network?

• Further, the IEEE-2061 standard proposes the use of a multi-hop wireless middle-mile network to extend connectivity to areas where optical-fibre links are not available.
• A multi-hop wireless middle-mile provides cost-effective connectivity over long distances, eliminating the need for costly and difficult-to-deploy optical fibres.
• An IEEE-2061 network can flexibly use one or more technologies like satellites, or long-range Wi-Fi for the middle-mile.
• The IEEE-2061 AN also has a direct and alternate path to the Internet, unlike the (4G/5G) network, where Internet connectivity is possible only via the CN.
• Unlike the 4G/5G networks, an IEEE-2061 network can also avoid the CN for communication between nearby users, which can be directly routed within AN instead.

Conclusion

In sum, the IEEE 2061-2024 is the second IEEE standard to come out of the research efforts of Prof. Karandikar’s lab at IIT Bombay. If adopted, IEEE 2061 can help provide affordable connectivity to rural populations. Its novel concepts, including the CN bypass, and integrated AN control may also pave the way towards a scalable mobile network in the future.

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Editorial 2 : Digital jurisprudence in India, in an AI era

Context

This rapidly-evolving technology does pose a challenge to existing legal frameworks and judicial precedents that have been designed for a pre-AI world.

Generative AI (GAI)

• Even though Generative AI (GAI) stands as a transformative force, wielding power to revolutionise society in ground-breaking ways, existing legal frameworks and judicial precedents that have been designed for a pre-AI world may struggle to effectively govern this rapidly-evolving technology.
• Generative AI refers to a type of artificial intelligence that is capable of generating new content, such as images, text, music, and even videos, based on patterns it learns from existing data.
• It’s used in various applications, from creating art to assisting in drug discovery and generating realistic human-like conversations.

Safe harbour and liability fixation

• One of the most persistent and contentious issues in Internet governance has been the fixing of liability on “intermediaries” for content hosted by them.
• The landmark Shreya Singhal judgment addressed this by upholding Section 79 of the IT Act which grants intermediaries ‘safe harbour’ protection against hosting content, contingent upon meeting the due diligence requirements outlined in Section 3(1)(b) of the Information Technology (Intermediaries Guidelines) Rules.
• However, its application to Generative AI tools remains challenging.There are contrasting views on the role of GAI tools.
• Some argue that they should be considered intermediaries since they are used almost like a search engine even though they do not host links to third-party websites.
• Others argue that they are mere “conduits” for user prompts, where altering the prompt leads to changes in output — essentially making the generated content akin to third-party speech, and, therefore, attracting lesser liability for the content generated.
• In Christian Louboutin Sas vs Nakul Bajaj and Ors (2018), the Delhi High Court held that safe harbour protection applies solely to “passive” intermediaries, referring to entities functioning as mere conduits or passive transmitters of information.
• However, in the context of Large Language Models (LLMs), making a distinction between user-generated and platform-generated content is increasingly challenging.
• Additionally, liability in the case of AI chatbots arises once the information is reposted on other platforms by the user; mere response to a user prompt is not considered dissemination.

The copyright conundrum

• Section 16 of Indian Copyright Act 1957 specifically provides that “no person” shall be entitled to protection of copyright except by the provisions of the Act.
• As in India, reluctance persists regarding the provisions of copyright protection to works generated by AI globally.
• The 161st Parliamentary Standing Committee Report found that the Copyright Act of 1957 is “not well equipped to facilitate authorship and ownership by Artificial Intelligence”.
• Under current Indian law, a copyright owner can take legal action against anyone who infringes on his/her work with remedies such as injunctions and damages.
• However, the question of who is responsible for copyright infringement by AI tools remains unclear.
• As previously argued, classifying GAI tools, whether as intermediaries, conduits, or active creators, will complicate the courts’ ability to assign liability.
•  ChatGPT’s ‘Terms of Use’ attempt to shift liability to the user for any illegal output. But the enforceability of such terms in India is uncertain.
• The landmark K.S. Puttaswamy judgment (2017) by the Supreme Court of India established a strong foundation for privacy jurisprudence in the country, leading to the enactment of the Digital Personal Data Protection Act, 2023 (DPDP).
• While traditional data aggregators or consent managers raise privacy concerns during the collection and distribution of personal information, Generative AI introduces a new layer of complexity.
• The DPDP Act introduces the “right to erasure“ as well as “right to be forgotten”. However, once a GAI model is trained on a dataset, it cannot truly “unlearn” the information it has already absorbed. This raises a critical question.

Steps to pursue

• First, learning by doing. Consider granting GAI platforms temporary immunity from liability following a sandbox approach.
• This approach allows responsible development while gathering data to identify legal issues that could inform future laws and regulations.
• Second, data rights and responsibilities. The process of data acquisition for GAI training requires an overhaul.
• Solutions could include revenue-sharing or licensing agreements with data owners.
• Third, licensing challenges. Licensing data for GAI is complex as web-data lacks a centralised licensing body similar to copyright societies in the music industry.
• A potential solution is the creation of centralised platforms, akin to stock photo websites such as Getty Images, which simplify licensing, streamline access to necessary data for developers and ensure data integrity against historical bias and discrimination.
• The jurisprudence around Generative AI (GAI) is hazy and yet to be evolved. It demands a comprehensive re-evaluation of existing digital jurisprudence.

Way forward

A holistic, government-wide approach and judicious interpretations by the constitutional courts are essential to maximise the benefits of this powerful technology, but safeguarding individual rights and protecting them against unwelcome harm all the while.