Editorial 1 : Can States tax mining activities?
Context
Upholding the principles of federalism, the Supreme Court clarified that the power of State legislatures to tax mineral activities within their respective territories is not constrained by Parliament’s Mines and Minerals (Development and Regulation) Act, 1957 (1957 Act).
What was the case?
- Section 9 of the 1957 Act requires those who obtain leases to conduct mining activities to “pay royalty in respect of any mineral removed” to the individual or corporation who leased the land to them.
- The key question for consideration was whether the royalties paid by mine leaseholders to State governments under the 1957 Act should be classified as “tax.”
- The case has its genesis in a dispute between India Cement Ltd and the Tamil Nadu government which arose after the company secured a mining lease in Tamil Nadu.
- Although India Cement was already paying royalties, the government imposed a cess — an additional tax on land revenues, including royalties.
- The company challenged this in the Madras High Court contending that the cess on royalties effectively constituted a tax on royalties, the imposition of which exceeded the State’s legislative authority.
- In 1989, a seven-judge Bench of the Supreme Court in India Cement Ltd. v. State of Tamil Nadu decided in favour of India Cement by reasoning that States only have the power to collect royalties and not impose taxes on mining activities.
- It pointed out that the Union government exercises overriding authority over the “regulation of mines and mineral development” under Entry 54 of the Union List, as specified by law (in this case, the 1957 Act). Thus, States are not empowered to levy additional taxes on this subject.
What is the difference between royalty and tax?
- The majority ruling clarified the distinction between royalty and tax.
- It defined royalty as the “contractual consideration” paid by the mining lessee to the lessor for the right to extract minerals.
- In contrast, a tax was characterised as an “imposition by a sovereign authority.”
- The judges underscored that taxes are determined by law and can only be levied by public authorities to fund welfare schemes and public services.
- Meanwhile, royalties are paid to a lessor in exchange “for parting with their exclusive privileges in the minerals”.
Can States tax mining activities?
- Entry 50 of the State List under the seventh Schedule of the Constitution gives States the exclusive authority to make laws regarding “taxes on mineral rights”, but this power is limited by any laws Parliament may pass concerning mineral development
- On the other hand, Entry 54 of the Union List gives the Centre the power to regulate “mines and mineral development,” especially when Parliament decides it is necessary in public interest.
- During the proceedings, the Centre argued that Entry 50 in the State List had allowed Parliament to impose “any limitations” on taxes on mineral rights through the promulgation of laws relating to mineral development — in this case, the 1957 Act.
- However, the majority reasoned that since royalties could not be classified as a tax, they do not fall within the category of “taxes on mineral rights”as defined in Entry 50 of the State List.
- As a result, it was held that the 1957 Act merely provided States with another source of revenue through royalties, without interfering with their authority to levy taxes on mineral rights under Entry 50.
- While the Centre is empowered to regulate mining development under Entry 54 of the Union List, the court clarified that this authority does not include the power to impose taxes, which is exclusively under the jurisdiction of the State legislatures.
- However, the Centre wanted to modify the existing legislative framework under the 1957 Act to divest States of their power to levy a tax, it could do so.
- The majority also held that States have the power to tax the land where mines and quarries are located by virtue of Article 246 read with Entry 49 (taxes on lands and buildings) of the State List.
- In other words, mineral-bearing lands also fall within the description of lands under Entry 49 of List 2, the CJI declared, adding that the income of the land yield can be adopted as a measure of tax.
The issues on the verdict
- The royalties paid under the 1957 Act should be considered as tax for developing the country’s mineral resources as quoted by Justice Nagarathna.
- She pointed out that a central legislation, like the 1957 Act, was intended to promote mineral development and this objective could be severely undermined if States were allowed to impose levies and cesses (additional taxes) on top of the royalties they collect.
- Elucidating upon the likely consequences of allowing States to tax mineral rights, the judge highlighted that this would lead to an “unhealthy competition between the States to derive additional revenue” resulting in a steep, uncoordinated, and uneven increase in the cost of minerals.
- Such a scenario, she warned, might exploit the national market for arbitrage, where differences in pricing could be manipulated for profit, disrupting the market’s stability.
Conclusion
If applied retroactively, it could result in significant financial benefits for mineral-rich States such as West Bengal, Odisha, and Jharkhand, which have enacted local laws to impose additional taxes on mining lessees.
Editorial 2 : Teaching computers to forget
Introduction
Policymakers have been grappling with the rising complexity of Machine Learning (ML) models that churn huge swathes of data through Large Language Models (LLMs) and deep neural networks. The complexity has made it difficult for data fiduciaries to effectively “correct, complete, update and erase” sensitive data from computer systems.
The antithesis of ML
- In order to deal with this problem, a possible solution that has ignited interest among researchers and companies alike is the idea of Machine Unlearning (MUL).
- It is the antithesis of ML. An algorithm is added to the AI model for the purpose of identifying and deleting false, incorrect, discriminatory, outdated, and sensitive information.
- The concept builds on the challenge of removing information due to the constant churning of data by these LLMs.
- So much so that it gets difficult to keep track of the data as it can be utilised for multiple objectives, creating a complex web of algorithms, also known as data lineage, that adversely affect its quality, leading to manipulation, adversarial outputs, and difficulty in locating and removing sensitive information.
- Moreover, as there is no sandbox approach for choosing and processing data in these models, there is also a proven possibility of hackers inserting manipulated data to produce biased results (data poisoning).
- Consequently, MUL is gaining traction as a viable option among data fiduciaries such as IBM where the models are being tested for enhanced unlearning accuracy, intelligibility, reduced unlearning time and cost efficiency.
Three approaches
- The question, however, remains how a MUL model can be implemented to effectively fulfil the obligation.
- There could be three approaches based on their viability for on-ground implementation: private, public, and international.
- In the private approach, data fiduciaries will be primarily responsible for testing MUL algorithms, which can then be applied across their training models for efficient deletion based on specific requirements.
- This voluntary approach gives companies much headroom to enhance their AI models and preserve users’ rights without undue government intervention.
- However, the problem occurs in expertise and affordability to execute these models, which might discourage smaller companies from testing the solutions.
- In the public approach, the government has the responsibility to prepare the statutory blueprint, either through soft-law or hard-law approaches, to obligate data fiduciaries to fulfil their legal obligations.
- The data reflect a high possibility of government intervention in the near future if a major breakthrough in a MUL model parallels the rising regulatory landscape.
- The government can issue guidelines under the respective Data or AI Protection Regime mandating that data fiduciaries implement a plausible MUL model.
- On the contrary, the government can itself prepare a MUL model as part of its Digital Public Infrastructure for the perusal of data fiduciaries to implement across platforms uniformly.
- This is especially useful in developing countries where the state has substantive stakes in the DPI for the country’s overall development. Moreover, it addresses the problem of affordability and expertise for smaller companies.
- The international approach emphasises the role of nation states in coming together and preparing a framework to be adopted uniformly at a domestic level.
- As the efficacy of this approach is not clear amid geopolitical frictions, the onus effectively shifts to the role of international standard-setting organisations such as the International Electrotechnical Commission to come up with MUL standards that can be applied across jurisdictions.
Way forward
The MUL is still in the preliminary stages. Therefore, stakeholders must address technical and regulatory considerations to ensure its effective implementation in this evolving landscape of AI.
