Centre Proposes New Bills for Criminal Law

Introduction

Established in the US, one of the world’s largest cab networks came into existence in 2010 and, since its inception, has expanded all over the globe with operations in 10,000 cities across 71 countries. It made a remarkable start in India in 2017 and, since then, has seen a rise in the customers and drivers for the company. India is among the largest markets for Uber, with 600,000 monthly drivers and 8.5 million monthly riders.

GeM

Government e-Marketplace (GeM) is a one-stop portal to facilitate online procurement of common-use Goods & Services required by various Government Departments / Organizations / PSUs. GeM aims to enhance transparency, efficiency and speed in public procurement. It provides the tools of e-bidding, reverses e-auction and demand aggregation to facilitate government users achieve the best value for their money. Government e-Marketplace owes its genesis to the recommendations of two Groups of Secretaries to the Prime Minister in January 2016. They recommended setting up a dedicated e-market for different goods & services procured or sold by Government/PSUs besides reforming DGS&D. Subsequently, the Finance Minister, in his Budget speech for FY 2016-17, announced setting up of a technology-driven platform to facilitate procurement of goods and services by various Ministries and agencies of the Government. The portal was launched on 9th August 2016 by the Commerce & Industry Minister.

Uber-GeM collaboration

The cab network giant has registered on the portal of the Government E-marketplace and has declared that it will offer its services to Government officials from Ministries and PSUs. The project is currently in its pilot phase and shall be executed systematically to cover all the ministries and PSUs in the nation. The officials can book cabs at a fixed price with no cancellation or surge fees on the rides. The authorised officials will be able to book a cab from the portal and select from the list of drivers available. It will be a cashless/cardless ride for the officials; additional vehicle categories for government riders have been added, namely, GeM Yatraa Hatch and GeM Yatraa Sedan, and there will be hourly rentals for multiple-stops, allowing the government officials to enjoy the flexible and easily accessible network of cabs in major cities.

Advantages

Such collaboration between Government institutions and corporates will go a long way to secure a stable equilibrium in the market. Uber, a US-based company, enjoys a vast user base in India and has created new job avenues. The advantages of the collaboration between GeM and Uber are as follows-

Easy accessibility

This will undoubtedly provide ease in accessibility in terms of being in a new place, and language barriers will no longer exist with such options for Government officials.

Increased jobs for drivers

With more cabs being engaged with ministries and PSUs, it is pertinent that the requirement for drivers will grow, thus increasing the employability rate in India and allowing the user to have an uninterrupted experience.

Ease of travel and commuting

This move will provide flexibility, thus leading to more ease in travel in cases of emergencies or places inaccessible by trains or other modes of transport.

Rise in travel and tourism

Coupled with the other factors, the opportunities for the users to visit different places will be an added advantage which will help boost the tourism industry, thus creating a balance in the market.

Sustainable Government corporate relationship

Such collaborations between the government and corporates will be substantial, signifying the ease of doing business in India. They will also act as a beacon of example for compliance with opportunities for the other companies and stakeholders.

Opportunities for collaboration with ingenious start-ups

With such major corporate joining hands with the government, the indigenous start-ups will have various opportunities to engage with companies and recreate similar businesses rooted in India, thus transforming the economy.

Conclusion

Transportation and communication play a vital role in our lives, thus, such collaboration will go a long way in creating a better and more uniform user experience in the country. This also goes a long way to showcase that the Governmental platforms also offer services of a global standard. Such portals exist in South Korea, Singapore, the US and Europe. The network of cabs can only be sustained using the locals as drivers, hence these collaborations are win-win for all as the market dynamics are improving, employability will increase, and improved user experience will be seen.

Introduction

Assisted Reproductive Technology (“ART”) refers to a diverse set of medical procedures designed to aid individuals or couples in achieving pregnancy when conventional methods are unsuccessful. This umbrella term encompasses various fertility treatments, including in vitro fertilization (IVF), intrauterine insemination (IUI), and gamete and embryo manipulation. ART procedures involve the manipulation of both male and female reproductive components to facilitate conception.

The dynamic landscape of data flows within the healthcare sector, notably in the realm of ART, demands a nuanced understanding of the complex interplay between privacy regulations and medical practices. In this context, the Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011, play a pivotal role, designating health information as "sensitive personal data or information" and underscoring the importance of safeguarding individuals' privacy. This sensitivity is particularly pronounced in the ART sector, where an array of personal data, ranging from medical records to genetic information, is collected and processed. The recent Assisted Reproductive Technology (Regulation) Act, 2021, in conjunction with the Digital Personal Data Protection Act, 2023, establishes a framework for the regulation of ART clinics and banks, presenting a layered approach to data protection.

A note on data generated by ART

Data flows in any sector are scarcely uniform and often not easily classified under straight-jacket categories. Consequently, mapping and identifying data and its types become pivotal. It is believed that most data flows in the healthcare sector are highly sensitive and personal in nature, which may severely compromise the privacy and safety of an individual if breached. The Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011 (“SPDI Rules”) categorizes any information pertaining to physical, physiological, mental conditions or medical records and history as “sensitive personal data or information”; this definition is broad enough to encompass any data collected by any ART facility or equipment. These include any information collected during the screening of patients, pertaining to ovulation and menstrual cycles, follicle and sperm count, ultrasound results, blood work etc. It also includes pre-implantation genetic testing on embryos to detect any genetic abnormality. 

But data flows extend beyond mere medical procedures and technology. Health data also involves any medical procedures undertaken, the amount of medicine and drugs administered during any procedure, its resultant side effects, recovery etc. Any processing of the above-mentioned information, in turn, may generate more personal data points relating to an individual’s political affiliations, race, ethnicity, genetic data such as biometrics and DNA etc.; It is seen that different ethnicities and races react differently to the same/similar medication and have different propensities to genetic diseases. Further, it is to be noted that data is not only collected by professionals but also by intelligent equipment like AI which may be employed by any facility to render their service. Additionally, dissemination of information under exceptional circumstances (e.g. medical emergency) also affects how data may be classified. Considerations are further nuanced when the fundamental right to identity of a child conceived and born via ART may be in conflict with the fundamental right to privacy of a donor to remain anonymous. 

Intersection of Privacy laws and ART laws: 

In India, ART technology is regulated by the Assisted Reproductive Technology (Regulation) Act, 2021 (“ART Act”). With this, the Union aims to regulate and supervise assisted reproductive technology clinics and ART banks, prevent misuse and ensure safe and ethical practice of assisted reproductive technology services. When read with the Digital Personal Data Protection Act, 2023 (“DPDP Act”) and other ancillary guidelines, the two legislations provide some framework regulations for the digital privacy of health-based apps.

The ART Act establishes a National Assisted Reproductive Technology and Surrogacy Registry (“National Registry”) which acts as a central database for all clinics and banks and their nature of services. The Act also establishes a National Assisted Reproductive Technology and Surrogacy Board (“National Board”)  under the Surrogacy Act to monitor the implementation of the act and advise the central government on policy matters. It also supervises the functioning of the National Registry, liaises with State Boards and curates a code of conduct for professionals working in ART clinics and banks. Under the DPDP Act, these bodies (i.e. National Board, State Board, ART clinics and banks) are most likely classified as data fiduciaries (primarily clinics and banks), data processors (these may include National Board and State boards) or an amalgamation of both (these include any appropriate authority established under the ART Act for investigation of complaints, suspend or cancellation of registration of clinics etc.) depending on the nature of work undertaken by them. If so classified, then the duties and liabilities of data fiduciaries and processors would necessarily apply to these bodies. As a result, all bodies would necessarily have to adopt Privacy Enhancing Technologies (PETs) and other organizational measures to ensure compliance with privacy laws in place. This may be considered one of the most critical considerations of any ART facility since any data collected by them would be sensitive personal data pertaining to health, regulated by the Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011 (“SPDI Rules 2011”). These rules provide for how sensitive personal data or information are to be collected, handled and processed by anyone.

The ART Act independently also provides for the duties of ART clinics and banks in the country. ART clinics and banks are required to inform the commissioning couple/woman of all procedures undertaken and all costs, risks, advantages, and side effects of their selected procedure. It mandatorily ensures that all information collected by such clinics and banks to not informed to anyone except the database established by the National Registry or in cases of medical emergency or on order of court. Data collected by clinics and banks (these include details on donor oocytes, sperm or embryos used or unused) are required to be detailed and must be submitted to the National Registry online. ART banks are also required to collect personal information of donors including name, Aadhar number, address and any other details. By mandating online submission, the ART Act is harmonized with the DPDP Act, which regulates all digital personal data and emphasises free, informed consent.

Conclusion

With the increase in active opt-ins for ART, data privacy becomes a vital consideration for all healthcare facilities and professionals. Safeguard measures are not only required on a corporate level but also on a governmental level. It is to be noted that in the 262 Session of the Rajya Sabha, the Ministry of Electronics and Information Technology reported 165 data breach incidents involving citizen data from January 2018 to October 2023 from the Central Identities Data Repository despite publicly denying. This discovery puts into question the safety and integrity of data that may be submitted to the National Registry database, especially given the type of data (both personal and sensitive information) it aims to collate. At present the ART Act is well supported by the DPDP Act. However, further judicial and legislative deliberations are required to effectively regulate and balance the interests of all stakeholders.

References

• The Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011
• Caring for Intimate Data in Fertility Technologies https://dl.acm.org/doi/pdf/10.1145/3411764.3445132 
• Digital Personal Data Protection Act, 2023
• https://www.wolterskluwer.com/en/expert-insights/pharmacogenomics-and-race-can-heritage-affect-drug-disposition  

In a recent ruling, a U.S. federal judge sided with Meta in a copyright lawsuit brought by a group of prominent authors who alleged that their works were illegally used to train Meta’s LLaMA language model. While this seems like a significant legal victory for the tech giant, it may not be so. Rather, this is a good case study for creators in the USA to refine their legal strategies and for policymakers worldwide to act quickly to shape the rules of engagement between AI and intellectual property.

The Case: Meta vs. Authors

In Kadrey v. Meta, the plaintiffs alleged that Meta trained its LLaMA models on pirated copies of their books, violating copyright law. However, U.S. District Judge Vince Chhabria ruled that the authors failed to prove two critical things: that their copyrighted works had been used in a way that harmed their market and that such use was not “transformative.” In fact, the judge ruled that converting text into numerical representations to train an AI was sufficiently transformative under the U.S. fair use doctrine. He also noted that the authors’ failure to demonstrate economic harm undermined their claims. Importantly, he clarified that this ruling does not mean that all AI training data usage is lawful, only that the plaintiffs didn’t make a strong enough case.

Meta even admitted that some data was sourced from pirate sites like LibGen, but the Judge still found that fair use could apply because the usage was transformative and non-exploitative.

A Tenuous Win

Chhabria’s decision emphasised that this is not a blanket endorsement of using copyrighted content in AI training. The judgment leaned heavily on the procedural weakness of the case and not necessarily on the inherent legality of Meta’s practices.

Policy experts are warning that U.S. courts are currently interpreting AI training as fair use in narrow cases, but the rulings may not set the strongest judicial precedent. The application of law could change with clearer evidence of commercial harm or a more direct use of content. 

Moreover, the ruling does not address whether authors or publishers should have the right to opt out of AI model training, a concern that is gaining momentum globally.

Implications for India

The case highlights a glaring gap in India’s copyright regime: it is outdated. Since most AI companies are located in the U.S., courts have had the opportunity to examine copyright in the context of AI-generated content. India has yet to start. Recently, news agency ANI filed a case alleging copyright infringement against OpenAI for training on its copyrighted material. However, the case is only at an interim stage. The final outcome of the case will have a significant impact on the legality of these language models being able to use copyrighted material for training.

Considering that India aims to develop “state-of-the-art foundational AI models trained on Indian datasets” under the IndiaAI Mission, the lack of clear legal guidance on what constitutes fair dealing when using copyrighted material for AI training is a significant gap.  

Thus, key points of consideration for policymakers include: 

• Need for Fair Dealing Clarity: India’s fair-dealing provisions under the Copyright Act, 1957, are narrower than U.S. fair use. The doctrine may have to be reviewed to strike a balance between this law and the requirement of diverse datasets to develop foundational models rooted in Indian contexts. A parallel concern regarding data privacy also arises. 
• Push for Opt-Out or Licensing Mechanisms: India should consider whether to introduce a framework that requires companies to license training data or provide an opt-out system for creators, especially given the volume of Indian content being scraped by global AI systems.
• Digital Public Infrastructure for AI: India’s policymakers could take this opportunity to invest in public datasets, especially in regional languages, that are both high quality and legally safe for AI training.
• Protecting Local Creators: India needs to ensure that its authors, filmmakers, educators and journalists are protected from having their work repurposed without compensation, since power asymmetries between Big Tech and local creators can lead to exploitation of the latter.

Conclusion

The ruling in Meta’s favour is just one win for the developer. The real questions about consent, compensation and creative control remain unanswered. Meanwhile, the lesson for India is urgent: it needs AI policies that balance innovation with creator rights and provide legal certainty and ethical safeguards as it accelerates its AI ecosystem. Further, as global tech firms race ahead, India must not remain a passive data source; it must set the terms of its digital future. This will help the country move a step closer to achieving its goal of building sovereign AI capacity and becoming a hub for digital innovation.

References

• https://www.theguardian.com/technology/2025/jun/26/meta-wins-ai-copyright-lawsuit-as-us-judge-rules-against-authors 
• https://www.wired.com/story/meta-scores-victory-ai-copyright-case/ 
• https://www.cnbc.com/2025/06/25/meta-llama-ai-copyright-ruling.html 
• https://www.mondaq.com/india/copyright/1348352/what-is-fair-use-of-copyright-doctrine 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113095#:~:text=One%20of%20the%20key%20pillars,models%20trained%20on%20Indian%20datasets. ‍
• https://www.ndtvprofit.com/law-and-policy/ani-vs-openai-delhi-high-court-seeks-responses-on-copyright-infringement-charges-against-chatgpt