{"id":7588,"date":"2023-08-09T14:01:22","date_gmt":"2023-08-09T08:31:22","guid":{"rendered":"https:\/\/ijpiel.com\/?p=7588"},"modified":"2023-08-09T14:01:23","modified_gmt":"2023-08-09T08:31:23","slug":"the-regulatory-gap-in-ai-enabled-carbon-capture-and-storage-technology-part-i","status":"publish","type":"post","link":"https:\/\/ijpiel.com\/index.php\/2023\/08\/09\/the-regulatory-gap-in-ai-enabled-carbon-capture-and-storage-technology-part-i\/","title":{"rendered":"The Regulatory Gap in AI-enabled Carbon Capture and Storage Technology- Part I"},"content":{"rendered":"\n

Abstract<\/span><\/strong><\/p>\n\n\n\n

Artificial Intelligence\u2019s application in Carbon Capture and Storage technology presents\nsignificant benefits but also poses new challenges in legal regulation. Given the potential for\ntransboundary effects and the need for globally coordinated action, the existing legal principles\nneed to be re-evaluated and expanded upon to ensure the responsible use of AI, especially in\nCarbon Capture and Storage technology.<\/span><\/p>\n\n\n\n

Thus, firstly<\/em>, this Blog Post gives a thematic introduction to Carbon Capture and Storage\ntechnology in India. Secondly<\/em>, this Blog Post examines the use of Artificial Intelligence in Carbon\nCapture and Storage technology. Thirdly<\/em>, this Blog Post critically analyzes the need for regulating\nthe use of Artificial Intelligence in Carbon Capture and Storage technology. Lastly<\/em>, this Blog Post\ngives recommendations on how to solve the conundrum of the absence of appropriate laws and\nregulations governing the usage of Artificial Intelligence in Carbon Capture and Storage\ntechnology in India.<\/span><\/p>\n\n\n\n

1. Introduction <\/span><\/strong><\/p>\n\n\n\n

Climate change and its effects are evident through various natural disasters affecting all nations\ncollectively. Inaction is not an option, and with evident urgency, specific measures have become\nessential in fighting climate change. Carbon capture, utilization, and storage will be critical to\nmeet global targets in future decarbonization. By its placement at a crucial point in history, India\nis an essential cog in the wheel for fighting global climate change. Presently, India is fourth<\/a> (4th)\nin terms of the total installed capacity of renewable energy, fifth<\/a> (5th) in solar energy, and fourth<\/a>\n(4th) in wind energy. The Indian government has ambitiously set a target of installing renewable\nenergy capacity to 175 Gigawatts (\u201cGW\u201d) by 2022 and 500 GW by 2030<\/a>. While the Indian\ngovernment is taking many steps to reduce carbon footprint, very little attention has been given\nto carbon capturing.<\/span><\/p>\n\n\n\n

Carbon Capture and Storage (\u201cCCS<\/strong>\u201d) constitutes a critical piece of the solution matrix that the\nworld is progressively turning towards to combat the omnipresent threat of climate change. With\nanthropogenic carbon emissions reaching unprecedented levels, a significant global temperature increase is almost inevitable. Given this reality, the quest for effective mitigation measures is at\nits zenith, precisely where CCS technology comes to the fore.<\/span><\/p>\n\n\n\n

CCS is a technological intervention<\/a> that captures the carbon dioxide (\u201cCO2<\/strong>\u201d) emissions\nproduced from using fossil fuels in electricity generation and industrial processes, aiming to avert\ntheir discharge into the atmosphere. It is essentially a three-part process encompassing capture,\ntransportation, and secure storage. The technology\u2019s capability to capture up to 90% of CO2\nemissions<\/a> makes it a promising prospect in the arsenal against climate change.<\/span><\/p>\n\n\n\n

In a world progressively powered by fossil fuels, CCS can considerably mitigate the adverse\nimpact of carbon emissions. Power plants and industries, the primary contributors to global CO2\nemissions, can leverage CCS technology to reduce their carbon footprint significantly. Energy\nconsumption is a significant contributor to the generation of carbon footprint. Traditionally,\nIndia has been greatly dependent on fossil fuels. In fact, India\u2019s oil imports are substantial and\nsignificantly affect the economy. The Electricity Act, 2003 (\u201c2003 Act<\/strong>\u201d) is an essential piece of\nlegislation, and the regulatory commissions perform an important function in promoting\nrenewable energy. In 2010, the Central Electricity Regulatory Commission (\u201cCERC<\/strong>\u201d)\nintroduced the concept of \u201cRenewable Energy Certificates\u201d (\u201cRECs<\/strong>\u201d), which are market\ninstruments used to promote renewable energy in electricity. Further, the CERC has been\nresponsible for other landmark steps, such as overhauling the Indian Electricity Grid Code,\nreducing regulatory and compliance burdens on businesses for ease of doing business in the\nrenewable energy sector, and introducing various important renewable energy legislations like the\n\u201cCERC (Terms and Conditions for Tariff determination from Renewable Energy Sources)\nRegulations, 2020\u201d and the \u201cCERC (Terms and Conditions for recognition and issuance of\nRenewable Energy Certificate for Renewable Energy Generation) Regulations, 2022.\u201d Due to\nsuch efforts by the ERCs in India, its renewable energy capacity has reached<\/a> 168.96 GW as of\n2023, showing the Indian government\u2019s aim to develop a substantial renewable energy capacity.<\/span><\/p>\n\n\n\n

In Hindustan Zinc Ltd. v. Rajasthan Electricity Regulatory Commission<\/em><\/a>, (2015) 12 SCC 611,\nthe Supreme Court of India (\u201cSC<\/strong>\u201d) held that the State ERC (\u201cSERC<\/strong>\u201d) was correct in promoting\nrenewable energy and mandating generating companies to follow its Regulations in this regard\nunder Section 86(1)(e) of the 2003 Act, read with the National Electricity Policy of 2005, the\nNational Tariff Policy of 2006 and the international obligations under the Kyoto Protocol to\nwhich India is a signatory. Further, the SC also held that the State ERC was correct in doing the same \u201c…to discharge the constitutional obligations as mandated under Article 21 \u2014 Fundamental Right of the\ncitizens and Article 48-A \u2013 the Directive Principles of State Policy and to discharge the Fundamental Duties by\nthe respondents as envisaged under Article 51-A(g) of the Constitution of India<\/em>.\u201d In the absence of a\ncoherent policy regarding CCS in India, the same principle can be followed as a stop-gap\narrangement. However, presently, there is no express law or policy in India to promote CCS.\nAlthough a Policy Report by NITI Aayog for policy framework and deployment of CCS<\/a> exists in\nIndia, it does not recommend any concrete laws or regulations that can be implemented to\ngovern and regulate CCS. Similarly, the 2003 Act has no provisions related to data protection,\nprivacy, and security which, in turn, makes the use of AI in CCS increasingly susceptible to\nvarious risks and challenges such as cyber-attacks, data leaks, unclear guidelines for the\nrequirement of consent for data sharing, and many other similar risks and challenges. Due to\nsuch a lacuna, this Blog Post has subsequently explained that a robust regulatory mechanism is\nneeded for the efficient and pervasive implementation of CCS.<\/span><\/p>\n\n\n\n

Regulatory bodies in India, notably the CERC and the various SERCs, wield significant influence\nin shaping India\u2019s energy sector through their power to set tariffs. These tariffs, by extension,\ndictate the financial viability of energy projects. While the promise of CCS technology in\nmitigating the devastating impacts of climate change is undeniable, it is essential to critically\nanalyze the economic considerations surrounding the adoption of CCS technology. As it stands,\nCCS is an expensive technology in terms of initial capital investment and ongoing operational\ncosts. Consequently, without financial incentives, its implementation at a significant scale may\nremain economically unfeasible. In this scenario, CERC and SERCs could play a\ntransformational role by providing tariff concessions for power projects that incorporate CCS.\nThis can be done in addition to the existing CCS policy framework in India. However, it is\ncrucial to explore the financial implications of these concessions critically as follows:<\/span><\/p>\n\n\n\n

    \u25cf The Impact on Non-CCS Energy Tariffs:<\/u><\/span><\/strong><\/p>\n\n\n\n

     A critical factor to consider is the potential\nrepercussions of tariffs for non-CCS energy sources.       There is a risk that these\nconcessions could inadvertently lead to higher tariffs for non-CCS energy,       which could\nimpose an undue burden on consumers.<\/span><\/p>\n\n\n\n

    \u25cf The Sustainability of Concessions:<\/u><\/span><\/strong><\/p>\n\n\n\n

      The long-term sustainability of these CCS\nconcessions is another important factor to examine. It is       crucial to assess whether these CCS concessions would maintain their financial feasibility over the        long haul or strain the\noverall regulatory budget.<\/span><\/p>\n\n\n\n

    \u25cf Balance with Other Renewable Energy Incentives:<\/u><\/span><\/strong><\/p>\n\n\n\n

      Lastly, an equally crucial factor is\nthe balance of these concessions against other renewable energy      incentives. The goal\nshould be to ensure a fair and robust clean energy market where different     technologies\ncan compete on an even playing field. We must be cautious of not creating an\n    environment that disproportionately favors CCS technology at the expense of other viable        renewable energy sources.<\/span><\/p>\n\n\n\n

When combined with sustainable biomass, the potential of CCS allows for \u201cnegative emissions,\u201d\neffectively drawing CO2 from the atmosphere. This solution is attractive due to its compatibility\nwith existing infrastructure, enabling the power and industrial sectors to operate with reduced\nemissions. This provides an effective transitional path towards renewable energy without\ndisrupting today's energy demands or tomorrow's ecological balance. However, its deployment is\nhindered by technological complexity, substantial financial investment for infrastructure, and\nlack of a definite market for captured carbon. Additionally, safe and secure storage of captured\ncarbon, primarily through geologic sequestration, is a fundamental requirement, given potential\nrisks like induced seismicity, groundwater contamination, and the possible release of stored\ncarbon back into the atmosphere. Thus, despite not being a panacea for climate change, CCS is\ncrucial in the global strategy against it, especially as nations aim to fulfill their Paris Agreement\nobligations. However, to harness its full potential, it is vital to address associated challenges\nthrough robust regulatory oversight, emphasizing the need for a well-crafted legal framework for\nits regulation.<\/span><\/p>\n\n\n\n

What also becomes evident is the promise that Artificial Intelligence (\u201cAI<\/strong>\u201d) holds in navigating\nthese challenges and optimizing the technology. AI and technology have become an intertwined\npart of our lives. Some people like Richard Yonck believe that with the advancement and\nemergence of AI, our world is becoming increasingly intelligent and is heading towards a future\npromising evolved higher intelligence. In fact, as per the latest technology and research, scientists\nare working to create virtual copies of human beings to enable future treatments in medicine. 1\nAll fields will be increasingly dependent upon AI. In fact, several experts have highlighted<\/a> the\nrole of AI in understanding climate change and predicting various scenarios. However, all this\nwill need high-quality data collection, which is where regulation of AI and CCS will become necessary. Therefore, the intersection of these factors underscores the need for a comprehensive\nlegal framework that accommodates the role of AI in CCS technology. While the journey\ntowards a more sustainable future is undoubtedly complex, we can hope to steer our world\ntowards a path of resilience and recovery through such intersections of technology, policy, and\nlaw. <\/span><\/p>\n\n\n\n

2. AI and CCS Technology <\/span><\/strong><\/p>\n\n\n\n

A. Need for AI in CCS Technology <\/span><\/strong><\/em><\/p>\n\n\n\n

In the backdrop of the escalating global environmental crisis, AI can be seen as an inevitable ally\nfor the effective deployment of CCS technologies. The compelling need for AI in CCS primarily\nstems from the complex, data-intensive, and dynamic nature of carbon capture, storage, and\nutilization processes. The traditional means of managing these processes, typically characterized\nby rigid and manual interventions, are proving insufficiently agile and responsive to the nuanced\nchallenges posed by a rapidly changing environmental landscape.<\/span><\/p>\n\n\n\n

To begin with, the complex process of capturing carbon emissions at their source demands a\nlevel of accuracy and efficiency that can only be realized through sophisticated machine-learning\nalgorithms. These algorithms, adept at processing large volumes of data, can predict optimal\nconditions for carbon capture, dynamically adjust these conditions in response to changes, and\nidentify potential risks and inefficiencies in the capture process. The consequence of such a\nproactive and nuanced approach is not only a reduction in carbon emissions but also an\nimprovement in the overall performance and efficiency of emission-intensive industries.<\/span><\/p>\n\n\n\n

Similarly, AI proves instrumental in the storage and utilization components of CCS technology.\nThe selection of suitable geological storage sites, the monitoring of stored carbon to prevent\nleakages, and the conversion of captured carbon into usable products \u2013 these are all areas replete\nwith uncertainties and variabilities that necessitate a data-driven, predictive approach. Leveraging\nAI\u2019s predictive analytics capabilities, researchers can model different storage scenarios, predict\nand mitigate potential risks, and optimize carbon utilization processes, thereby maximizing the\nenvironmental and economic benefits of CCS.<\/span><\/p>\n\n\n\n

B. Benefits of AI in CCS Technology <\/span><\/strong><\/em><\/p>\n\n\n\n

At its core, AI is about harnessing computational power to imitate human intelligence and\ndecision-making. This computational strength can be effectively employed to navigate the\nintricate challenges posed by CCS technology. A case in point would be the optimization of CCS\nsystems. AI algorithms, when employed, can analyze vast amounts of data relating to the\nperformance of CCS systems under different conditions. By parsing through this data, these\nalgorithms can ascertain the most efficient configurations, thereby enhancing the system\u2019s CCS\ncapacity and reducing the associated costs concurrently.<\/span><\/p>\n\n\n\n

AI can further assist in predicting the behavior of underground storage reservoirs, a critical\naspect of the CCS process. AI\u2019s predictive modeling can inform operators about the possible\nfuture behavior of these reservoirs, allowing for pre-emptive measures to avoid issues like\nleakage or seismic events. With safety and environmental concerns being paramount, AI\u2019s\npredictive capabilities play a pivotal role in risk mitigation and safety assurance, underlining AI\u2019s\nindispensability in CCS technology.<\/span><\/p>\n\n\n\n

AI-driven data analysis can also contribute significantly to climate action by highlighting the\nunseen aspects of carbon emissions. AI can detect and quantify emissions from individual power\nplants, creating a comprehensive emissions map. Such insights can equip policymakers and\nbusinesses with the necessary data to make informed decisions, adding another layer of\naccountability to the climate action discourse.<\/span><\/p>\n\n\n\n

The case of the FarmBeats project funded by Microsoft\u2019s \u201cAI for Earth\u201d programme<\/a> exemplifies\nthe potential of AI in climate action. This project assists farmers in optimizing their use of\nresources, thereby reducing their environmental impact. Through the strategic use of sensors and\ndrones, data on various environmental factors is collected. AI algorithms analyze this data to\nmake resource optimization recommendations. This illustrates how AI can enhance\nenvironmental sustainability across different sectors by optimizing resource use and reducing\ncarbon emissions.<\/span><\/p>\n\n\n\n

AI can also be influential in managing energy systems such as smart grids, reducing energy waste,\nand increasing efficiency. Predictive algorithms can anticipate energy demand and supply,\nallowing for adjustments in production and distribution to prevent wastage and overproduction. As we transition to more sustainable energy systems, AI\u2019s role in managing these transitions and\nreducing greenhouse gas emissions becomes increasingly critical.<\/span><\/p>\n\n\n\n

AI\u2019s role in enhancing the potential of CCS technology does not stop at the doors of technology\noptimization and predictive analytics; it extends further, aiding in the domain of novel\ntechnology development. Here, nascent technologies such as Direct Air Capture (\u201cDAC<\/strong>\u201d) can\nuse AI to optimize and scale. DAC is a promising CCS methodology that, at a large scale, could\nplay a pivotal role in climate change mitigation. However, presently, the technology is in its\ninfancy and needs substantial optimization and efficiency improvements. This is where AI comes\ninto play, given its proven track record in enhancing the efficiency and effectiveness of complex\nsystems. Machine learning algorithms can be employed to study the complex chemistry involved\nin DAC processes. By iteratively learning from these processes, AI can potentially help enhance\nthe capture efficiency and suggest novel methods or materials to further the DAC technology.\nThe impact of such advancements is two-fold. Primarily, it aids in mitigating climate change by\ndeveloping more efficient CCS technologies. Additionally, it paves the way for economically\nviable CCS technologies, promoting their widespread adoption and furthering the cause of\nclimate action.<\/span><\/p>\n\n\n\n

C. Challenges of AI in CCS Technology <\/span><\/strong><\/em><\/p>\n\n\n\n

However, deploying AI in CCS technology is not devoid of challenges. High-quality data is\nnecessary for AI models to make accurate predictions and recommendations. Since climate data\ncan be sparse, incomplete, or of poor quality, AI\u2019s effectiveness can be limited. This necessitates\nan investment in data collection and validation to enhance the accuracy and reliability of AI\nmodels. This is where the regulation of AI and the collection of data becomes of paramount\nimportance. Trust and transparency are also essential considerations when deploying AI. AI\nalgorithms can be opaque and difficult to interpret, creating challenges in policy decisions based\non this technology. Therefore, efforts should be directed toward developing transparent AI\nsystems that can be audited and explained, fostering public trust and acceptance. It is imperative\nthat all data collection is undertaken transparently and with informed consent.<\/span><\/p>\n\n\n\n

3. The Imperative for Legal Regulation of AI in CCS Technology<\/span><\/strong><\/p>\n\n\n\n

A. Transparency and Accountability Concerns <\/span><\/strong><\/em><\/p>\n\n\n\n

As we critically analyze the need for the legal regulation of AI in CCS technology, the first point\nto address is the multifaceted nature of AI applications in this field and the diversity of legal\nquestions they engender. From the design of machine learning algorithms to the handling of\nmassive amounts of environmental data, from decision-making processes involving strategic\nplanning of CCS initiatives to optimizing the operations of CCS systems, AI applications span a\nwide range of activities, each raising unique legal and ethical issues.<\/span><\/p>\n\n\n\n

Transparency is crucial in engendering trust and acceptance in AI systems, particularly when\nthese systems are employed in areas as significant and impactful as CCS. However, the often\n\u201cblack box\u201d nature of AI algorithms, where their internal workings remain opaque even to their\ndesigners, presents a considerable challenge to ensuring transparency. Due to these challenges,\nthe following questions arise:<\/span><\/p>\n\n\n\n

    \u25cf Can AI decisions be trusted when their bases remain unclear?<\/span><\/strong><\/p>\n\n\n\n

The answer is likely\nto be complex and dependent on multiple factors. The trustworthiness of AI decisions\nwill heavily depend on the robustness of the AI system\u2019s design and implementation, the\nquality and relevance of the data it is trained on, and the level of human oversight in its\ndeployment and use. Nevertheless, without a clear understanding of the basis of AI\ndecisions, a significant trust deficit remains, complicating its widespread acceptance,\nparticularly in critical areas like CCS. Therefore, it is imperative that AI laws consider the\nnecessary mechanisms to mitigate this trust deficit. Such mechanisms might include\nmandating explanations in AI design or advocating for hybrid decision-making models\nwhere human judgment works in tandem with AI.<\/span><\/p>\n\n\n\n

    \u25cf Can we fully understand and predict the implications of these AI decisions?<\/span><\/strong><\/p>\n\n\n\n

The\nanswer is less straightforward. The \u201cblack box\u201d nature of AI makes it challenging to fully\ncomprehend and foresee the potential ramifications of AI-based decisions, particularly in\ncomplex and dynamically evolving contexts like CCS. This ambiguity might lead to\nunintended and possibly harmful outcomes, posing risks to the credibility and reliability\nof AI-integrated systems. Thus, the framing of AI laws should incorporate guidelines for\nrigorous testing, monitoring, and validation of AI systems, particularly when applied in\ncritical sectors. Moreover, legal and regulatory mechanisms should be implemented for accountability and redress in cases where AI systems lead to unintended negative\nconsequences.<\/span><\/p>\n\n\n\n

Therefore, these questions need appropriate consideration for transparency while framing AI\nlaws and regulations.<\/span><\/p>\n\n\n\n

Then comes the issue of accountability. When an AI system integrated with CCS technology\nmakes a decision that leads to unintended consequences, the following questions arise:<\/span><\/p>\n\n\n\n

    \u25cf Who is to be held accountable? Is it the designers of the AI system, the operators\nof      the CCS technology, or the policymakers who allowed its use?<\/span><\/strong><\/p>\n\n\n\n

It is imperative to\nnote that the answer is not straightforward. It is significantly influenced by the specifics\nof the situation, including the context of the decision-making, the nature of the\nunintended consequences, and the role of different stakeholders in the AI system\u2019s\ndesign, operation, and oversight. Generally, one could argue that the responsibility\nshould fall on the entity that exercises the most control over the AI system\u2019s design,\nimplementation, and operation. However, this becomes complicated in practice due to\nthe highly integrated and interdependent nature of AI systems. Designers of the AI\nsystem may argue that they only provided the tools and that it was the operators who\nmisused or misapplied them. Conversely, the operators might argue that they operated\nthe system within its intended purpose, and it is the designer\u2019s responsibility if the system\ndid not perform as expected. On the other hand, policymakers might argue that they\nmerely allowed the AI technology\u2019s use based on the information available at the time.\nThis blurring of lines of responsibility necessitates the need for comprehensive\nregulations that clearly outline obligations and accountability in different scenarios. This\nwould involve establishing legal frameworks that specifically address accountability in AI\nsystems, possibly drawing from existing legal concepts such as \u201cproduct liability\u201d and\n\u201cprofessional negligence.\u201d<\/span><\/p>\n\n\n\n

    \u25cf What if the AI system was trained on biased data, leading to biased decisions,\n       exacerbating environmental injustices or inefficiencies in CCS efforts \u2013 who bears\nthe         responsibility then?<\/span><\/strong><\/p>\n\n\n\n

\nArguably, the responsibility should fall on the designers or\ntrainers of the AI system. They are typically responsible for data selection and should\nensure that the data used is representative and unbiased. This underscores the need for robust data governance practices in AI system design, which should be reinforced by\nlegal and ethical guidelines. However, in practice, the scenario could be more complex.\nFor instance, the designers might have used the best available data at the time, and the\nbiases might have been unknown or unavoidable. Furthermore, it could be argued that\noperators and policymakers also share responsibility for not adequately scrutinizing the\nAI system and its potential biases before its deployment.<\/span><\/p>\n\n\n\n

Therefore, the aforementioned are just a few examples illustrating the critical need for legal\nregulation to clearly define accountability in the application of AI in CCS technology.<\/span><\/p>\n\n\n\n

As we shift towards a more AI-integrated future in CCS, the legal framework needs to address\nthe issue of bias in AI decisions. Unconscious biases in training data can result in AI systems\nperpetuating or exacerbating these biases in their decisions, as earlier elaborated for the CCS\ntechnology. This has significant implications for CCS efforts and their impacts on society,\npotentially leading to unequal distribution of resources or burdens. Clear regulations are needed\nto ensure AI systems are designed and trained to minimize bias and promote equitable outcomes\nin CCS technology.<\/span><\/p>\n\n\n\n

B. Data Privacy Concerns in Light of GDPR Provisions <\/span><\/strong><\/em><\/p>\n\n\n\n

The European Union (\u201cEU<\/strong>\u201d) General Data Protection Regulation (\u201cGDPR<\/strong>\u201d), as an archetypal\nlegal framework, has set a global standard for data privacy and protection. In the context of AI\nintegration within CCS technology, understanding GDPR is paramount due to the volume and\nnature of data involved in these CCS technologies.<\/span><\/p>\n\n\n\n

However, it is important to note that, while the GDPR serves as a beacon of rigorous data\nprotection standards, its applicability to all countries, particularly India, is limited due to several\nfactors. Firstly, different socio-cultural norms and understandings of privacy may make direct\ntransposition problematic. Secondly, the GDPR's infrastructure and enforcement mechanisms\nmay be beyond the capacity of developing countries like India. Lastly, regulatory discrepancies\nbetween the EU and other jurisdictions may lead to legal challenges and complexities. Due to\nthis, there arises an imperative need for countries like India to adopt their own data privacy and\nprotection legislations wherein GDPR principles may be adopted to a limited extent, but not in\nits entirety.<\/span><\/p>\n\n\n\n

On a national level, the collection of personal data and information and Sensitive Personal Data\nand Information (\u201cSPDI<\/strong>\u201d) in India is currently overseen by the \u201cInformation Technology\n(Reasonable Security Practices and Procedures and Sensitive Personal Data or Information)\nRules, 2011\u201d (\u201c2011 Rules<\/strong>\u201d). However, acknowledging the rapid development of AI and internet\nindustries, this existing framework is undergoing a considerable transformation. The\nforthcoming \u201cDigital Personal Data Protection Bill, 2022\u201d (\u201c2022 Bill<\/strong>\u201d) and the draft \u201cDigital\nIndia Act, 2023\u201d (\u201c2023 Act<\/strong>\u201d) promise significant changes in the collection and processing of\npersonal data and information and SPDI.<\/span><\/p>\n\n\n\n

Despite the developments mentioned above, a glaring lacuna within the Indian legal and\nregulatory landscape, both old and new, is the failure to contemplate the challenges and\nimplications of AI. This shortcoming holds substantial consequences for data collection and\nprocessing in innovative sectors like CCS technology, where AI plays a critical role. Taking\nguidance from GDPR and harmonizing it with India's unique context could be a step towards\nfilling this gap in India's data protection and privacy laws, especially for deploying AI in CCS\ntechnology in India.<\/span><\/p>\n\n\n\n

    i. Collection and Processing of Data: Article 6 of the GDPR<\/u><\/span><\/strong><\/em><\/p>\n\n\n\n

The primary concern of any data-intensive system, such as AI in CCS technology, is the\ncollection and processing of data. Article 6 of the GDPR<\/a> stipulates the lawfulness of processing\npersonal data. According to Article 6, personal data can be processed<\/a> if the data subject has\ngiven explicit consent, or if the processing is necessary for the performance of a contract,\ncompliance with a legal obligation, protection of vital interests, the performance of a task carried\nout in the public interest or in the exercise of official authority, or for the purposes of legitimate\ninterests pursued by the data controller or a third party.<\/span><\/p>\n\n\n\n

\nApplying this provision to AI in CCS technology, data processing must either be necessary for\nthe technology\u2019s function or occur with the explicit consent of individuals whose data is being\nprocessed. Here, the challenge lies in the scope and diversity of data needed, which may\ninadvertently include personal data. This necessitates robust mechanisms for obtaining informed\nconsent and for ascertaining the necessity of data processing for CCS operations.<\/span><\/p>\n\n\n\n

    ii. Data Minimization and Purpose Limitation: Article 5 of the GDPR<\/u><\/span><\/strong><\/em><\/p>\n\n\n\n

\nArticle 5 of the GDPR<\/a> introduces the principles of data minimization and purpose limitation.\nData minimization means that personal data must be \u201c\u2026adequate, relevant and limited to what is\nnecessary in relation to the purposes for which they are processed.<\/em>\u201d Purpose limitation, on the other hand,\nmeans that personal data must be \u201c\u2026collected for specified, explicit and legitimate purposes and not further\nprocessed in a manner that is incompatible with those purposes\u2026<\/em>\u201d.<\/span><\/p>\n\n\n\n

\nThese principles pose significant challenges in the context of AI applications within CCS\ntechnology. AI systems typically rely on vast data sets for effective functioning. Therefore, the\nprinciple of data minimization needs judicious handling of environmental data, ensuring that any\npersonal data utilized is strictly necessary for the functioning of the AI system in CCS\ntechnology. Similarly, purpose limitation necessitates a clear delineation of why and how\nenvironmental data for CCS technology is processed, and any subsequent use of the data must\nalign with these stated purposes.<\/span><\/p>\n\n\n\n

    iii. Rights of Data Subjects: Articles 15-22 of the GDPR<\/u><\/span><\/strong><\/em><\/p>\n\n\n\n

\nGDPR, in Articles 15 to 22<\/a>, delineates the rights of data subjects, which include the right to\naccess, rectification, erasure (also known as the \u201cright to be forgotten<\/strong>\u201d), restriction of\nprocessing, notification obligation, data portability, objection to processing, and rights related to\nautomated decision making, including profiling.<\/span><\/p>\n\n\n\n

\nThese rights entail numerous obligations for entities utilizing AI in the CCS technology.\nEnsuring individuals\u2019 rights to access their data and demand rectification or erasure necessitates\nrobust data management systems. Moreover, given that AI in CCS technology involves\nautomated decision-making, mechanisms must be in place to allow individuals to challenge such\ndecisions, particularly when they significantly affect the individual.<\/span><\/p>\n\n\n\n

    iv. Data Protection by Design and by Default: Article 25 of the GDPR<\/u><\/span><\/strong><\/em><\/p>\n\n\n\n

\nArticle 25 of the GDPR<\/a> mandates \u201cData Protection by Design and by Default.\u201d This means that\ndata protection considerations must be embedded in the design stage of any system or process\nand applied by default throughout its operation. For AI applications within CCS technology, this\nprinciple is highly pertinent. AI systems within CCS technology must be designed to prioritize\ndata protection. This might involve integrating features to ensure data minimization, consent\nmanagement, and secure data storage and processing.<\/span><\/p>\n\n\n\n

\nTherefore, when considering the integration of AI into CCS technology, GDPR\u2019s provisions\nprovide vital guidelines for addressing data privacy concerns. Strict adherence to these provisions\nwill not only ensure compliance with data privacy laws but will also foster greater trust and\nconfidence in the use of AI for CCS, thereby potentially enhancing the effectiveness of these\ntechnologies in combating climate change. However, translating these provisions into practice in\nthe context of AI in CCS technology will need a concerted effort from policymakers, AI\ndevelopers, and all other stakeholders, underscoring the need for a collaborative approach to\ndata protection in this emerging and critical field.<\/span><\/p>\n\n\n\n

Disclaimer<\/span><\/strong><\/p>\n\n\n\n

The views and opinions expressed by the Authors are personal.\n<\/em><\/strong><\/span><\/p>\n\n\n\n

About the Authors<\/span><\/strong><\/p>\n\n\n\n

\nMr. Varun Pathak is a Partner (Dispute Resolution) at Shardul Amarchand Mangaldas & Co.,\nNew Delhi. He is an Advocate-on-Record at the Supreme Court of India. He has completed his\nLL.M. in Corporate and Commercial Laws from the London School of Economics (LSE).<\/span><\/p>\n\n\n\n

\nMr. Pushpit Singh is a 5 th -Year B.B.A. LL.B. Student at Symbiosis Law School, Hyderabad. He is\na freelancing Corporate and Disputes Paralegal. He is also an Indian Institute of Arbitration and\nMediation (IIAM) Panel Arbitrator.<\/span><\/p>\n\n\n\n

Editorial Team<\/span><\/strong><\/p>\n\n\n\n

Managing Editor: Naman Anand<\/em>
Editors-in-Chief(Blog): Abeer Tiwari & Muskaan Singh<\/em>
Editor-in-Chief (Journal) and Senior Editor: Hamna Viriyam<\/em>
Associate Editor: Pushpit Singh<\/em>
Junior Editor: Ishaan Sharma<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Abstract Artificial Intelligence\u2019s application in Carbon Capture and Storage technology presents significant benefits but also poses new challenges in legal regulation. Given the potential for transboundary effects and the need for globally coordinated action, the existing legal principles need to be re-evaluated and expanded upon to ensure the responsible use of AI, especially in Carbon […]<\/p>\n","protected":false},"author":258,"featured_media":7587,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":"","wp_social_preview_title":"","wp_social_preview_description":"","wp_social_preview_image":0},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/posts\/7588"}],"collection":[{"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/users\/258"}],"replies":[{"embeddable":true,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/comments?post=7588"}],"version-history":[{"count":86,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/posts\/7588\/revisions"}],"predecessor-version":[{"id":7674,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/posts\/7588\/revisions\/7674"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/media\/7587"}],"wp:attachment":[{"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/media?parent=7588"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/categories?post=7588"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ijpiel.com\/index.php\/wp-json\/wp\/v2\/tags?post=7588"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}