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This is the first report on legal & policy aspects related to Web3 technologies, developed by VLiGTA, the research & innovation division of Indic Pacific Legal Research. In this report, we have offered a comprehensive overview of state of Web3 policy & governance outlooks in India. The report addresses the state of India’s successful Digital Public Infrastructure, and examines the state of technology governance as well. Further, the report focuses on several kinds of blockchain consensus algorithms, and the issues related to transitioning from using Web2 system infrastructure to Web3 system infrastructure. Sanad Arora’s contributions in Chapter 2 are remarkable. Akash Manwani’s contributions in Chapters 5 and 6 are unique, specific and relevant to the current discourse. It has been my honour to contribute to Chapters 3, 4 and 6, to offer informed perspectives and analyses. We have offered thought models and suggestions in the form of use cases of Web3 in areas such as data portability, voting, supply management, decentralised exchanges and zero-knowledge taxes. With this general technical report, we hope to offer more contributions in India’s Web3 policy space, in future. You can find a glance of the report here. You can access the complete report on the VLiGTA App. Price: 400 INR The conclusions and recommendations provided in the report are described here as well. Conclusion The choice between centralized and decentralized technology infrastructures should be made thoughtfully, considering the specific needs and objectives of each application. Decentralized approaches offer greater transparency and data integrity but may require careful scalability planning. On the other hand, centralized models can provide efficiency and centralized control but may face challenges related to transparency and accountability. Now, we already see that at Union and State levels, India is trying to develop and provide scalable and sustainable Web3 solutions. For sure, the CDAC proposals of a National Blockchain Service & the Unified Blockchain Network, termed under Blockchain-as-a-Service (BaaS) are ambitious and clear about their objectives. This report concludes with two kinds of recommendations – general and specific. We have offered tailor-made and practical recommendations, which may be workable and could be adapted with. Recommendations from VLiGTA-TR-004 Mitigating Structural Limitations Continuously assess the Digital Public Infrastructure (DPI) to identify and address structural limitations as Web3 technologies are integrated. Adopt a Web 2.5 approach that combines the strengths of both Web2 and Web3 ecosystems to mitigate potential limitations. Collaborate with stakeholders to develop strategies for a seamless transition to Web3 technologies while ensuring the DPI's robustness. Using certain blockchain consensus algorithms could certainly be helpful for the Government to invent taxonomies of governance, compliance and transparency when DPI components are built on chains. Utilizing open source Web3 and Web2 technologies in conjugation would change the economics of infrastructure-related solutions offered by the Government of India under India Stack, and even the proposed National Blockchain Service & the Unified Blockchain Network. The National Blockchain Service (NBS) and Unified Blockchain Network (UBN) proposals present significant opportunities for enhancing data management, tax filing, voting systems, and global supply chain tracking within India. Leveraging blockchain technology can contribute to increased transparency, security, and efficiency across these domains. Governance Clarity and Risk Mitigation Establish clear policies and governance frameworks for the adoption of Web3 infrastructure, encompassing both political and technical aspects. Ensure that decision-making processes are agile and effective, even in the face of complex challenges (polycrisis). Safeguard against policy paralysis and potential disruptions to administrative and regulatory systems through proactive risk mitigation measures. Decentralised Exchanges (DEXs) for Government-to-Government Transactions The prospective advantages arising from DEX implementation in the sphere of inter-ministerial and governmental financial operations are substantial. DEXs may be endowed with interoperability capabilities, enabling seamless fund transference amongst diverse governmental departments. This enhancement would serve to elevate the efficiency of financial transactions, concurrently mitigating the specter of fraudulent activities. DEXs could be harnessed as a facilitative medium for the exchange of Indian Central Bank Digital Currencies (CBDCs) among various government entities. This envisioned application stands to foster the adoption of CBDCs and streamline governmental financial management. DEXs hold the potential to abate the inherent risks associated with bureaucratic participation in financial transactions by endowing them with a secure and transparent conduit for fund exchanges. Consequently, governmental personnel could redirect their focus towards policy formulation and execution. DEXs can assume either an open-ended or close-ended configuration, affording governmental authorities the prerogative to select the most pertinent model aligned with their specific requisites. An open-ended DEX would grant unrestricted participation, while a close-ended variant would restrict access to authorized users. The degree of centralization, decentralization, and federalization of DEXs may fluctuate contingent upon the system's unique architectural design. Centralized DEXs would be subject to sole-entity control, whereas decentralized counterparts would operate within a network of nodes. Federalized DEXs would emerge as an amalgamation of these paradigms, featuring government-operated nodes and privately controlled nodes. DEXs are adaptable for deployment either in retail contexts, catering to individuals and commercial entities in fund exchange, or in government-to-government scenarios, serving as the conduit for intergovernmental fund transfers. However, judicious scrutiny and meticulous tailoring of the DEX system's specifications are imperative to ensure seamless alignment with the government's distinct exigencies. Consequently, the government should engage in a comprehensive feasibility assessment concerning the prospective integration of such a system in the immediate future. Purposeful Choices for Web3 Adoption Embrace a technology-neutral approach to accommodate various Web3 use cases while aligning with India's policy vision. Focus on development-oriented strategies that leverage Web3 technologies to address societal and economic challenges. Encourage socio-technical mobility by fostering an environment where both public and private sectors can adapt and innovate with Web3 tools. Leverage leapfrogged access points to ensure that Web3 technologies are accessible and beneficial to a broad spectrum of the population. National Blockchain Service (NBS) Implementation Implement the NBS infrastructure as per the proposed four-part regional approach to enhance accessibility and efficiency. Ensure seamless integration of the NBS with existing government systems like India Stack and Web2 DPI solutions for improved service delivery. Data Portability Consider leveraging blockchain technology to enable data portability, following the principles of data fluidity. Explore the use of Decentralized Applications (DApps) or Decentralized Autonomous Organizations (DAOs) for data portability within a blockchain framework. Categorize data based on risk levels to determine the extent of portability. Zero-Knowledge Taxes (ZKT) Develop a secure and tamper-proof blockchain network for ZKT implementation, ensuring data privacy. Consider adopting blockchain consensus algorithms for generating and verifying zero-knowledge proofs. Assess the cost and security factors in choosing between trusting third-party applications or a decentralized blockchain network for ZKT. Decentralized Voting Evaluate the potential benefits of blockchain-based decentralized voting, including enhanced transparency and security. Consider the adoption of cryptographic credentials for voters to ensure anonymity and authentication. Weigh the trade-offs between centralized and decentralized voting systems, taking into account specific use cases and the level of trust in centralized entities. Global Supply Chain Tracking Implement blockchain-powered supply chain tracking to improve transparency and traceability. Leverage blockchain to verify product authenticity and ethical certifications, providing consumers with trusted information. Carefully assess the choice between centralized and decentralized supply chain tracking based on the desired level of control and transparency. Get access to the complete report at 400 INR. Access the full report at https://vligta.app/product/reinventing-regulating-policy-use-cases-of-web3-for-india-vligta-tr-004/

Our Founder and Managing Partner, Abhivardhan is glad to hold a 2-hour virtual workshop with upGrad on Generative AI and Law. This workshop is a free event to attend virtually. upGrad has stated that they will provide a Certificate upon Completion. Abhivardhan will discuss about nuances related to the use of Generative Artificial Intelligence, and its use in the legal industry, especially when it comes to document analysis and legal research. The workshop will also cover some nuances related to the legal issues around Generative AI tools, especially on prompt engineering, cybersecurity and intellectual property-related issues. Register for the workshop for free at https://www.upgrad.com/generative-ai-law-workshop/ About Abhivardhan, our Founder and Managing Partner Throughout his journey, he has gained valuable experience in international technology law, corporate innovation, global governance, and cultural intelligence. With deep respect for the field, Abhivardhan has been fortunate to contribute to esteemed law, technology, and policy magazines and blogs. His book, "AI Ethics and International Law: An Introduction" (2019), modestly represents his exploration of the important connection between artificial intelligence and ethical considerations. Emphasizing the significance of an Indic approach to AI Ethics, Abhivardhan aims to bring diverse perspectives to the table. Some of his notable works also include the 2020 Handbook on AI and International Law, the 2021 Handbook on AI and International Law and the technical reports on Generative AI, Explainable AI and Artificial Intelligence Hype.

We are more than glad to release another technical report by the VLiGTA team. This report takes a business-oriented generalist approach on AI explainability ethics. We express our gratitude to Ankit Sahni for authoring a foreword to this technical report. This research is a part of the technical report series by the Vidhitsa Law Institute of Global and Technology Affairs, also known as VLiGTA® - the research & innovation division of Indic Pacific Legal Research. Responsible AI has been a part of the technology regulation discourse for the AI industry, policymakers as well as the legal industry. As ChatGPT and other kinds of generative AI tools have become mainstream, the call to implement responsible AI ethics measures and principles in some form becomes a necessary one to consider. The problem lies with the limited and narrow-headed approach of these responsible AI guidelines, because of fiduciary interests and the urge to be reactive towards any industry update. This is exactly where this report comes. To understand, the problems with Responsible AI principles and approaches can be encapsulated in these points: AI technologies have use cases which are fungible There exist different stakeholders for different cases on AI-related disputes which are not taken into consideration Various classes of mainstream AI technologies exist and not all classes are dealt by every major country in Asia which develops and uses AI technologies The role of algorithms in shaping the economic and social value of digital public goods remains unclear and uneven within law This report is thus a generalist and specificity-oriented work, to address & explore the necessity of internalising AI explainability measures into perspective. We are clear with a sense of perspective that not all AI explainability measures can be even considered limited to the domains of machine learning, and computer science. Barring some hype, there are indeed some transdisciplinary and legal AI explainability measures, which could be implemented. I am glad my co-authors from the VLiGTA team did justice to this report. Sanad Arora, the first co-author of this report, has extensively contributed on aspects related to the limitations of responsible AI principles and approaches. He has also offered insights on the issue of convergence of legal and business concerns related to AI explainability. Bhavana J Sekhar, the second co-author has offered her insights on developing AI explainability measures to practice conflict management when it comes to technical and commercial AI use cases. She has also contributed extensively on legal & business concerns pertaining to the enabling of AI explainability in Chapter 3. Finally, it has been my honour to contribute on the development of AI explainability measures to practice innovation management, when it comes to both technical and commercial AI use cases. I am glad that I could also offer an extensive analysis on the socio-economic limits of the responsible AI approaches at present. You can now access the complete report on the VLiGTA App: https://vligta.app/product/promoting-economy-of-innovation-through-explainable-ai-vligta-tr-003/ Recommendations from VLiGTA-TR-003 Converging Legal and Business Concerns Legal and Business concerns can be jointly addressed by XAI where data collected from XAI can be used to address the regulatory challenges and help in innovation, while ensuring accountability on the forefront. Additionally, information from XAI systems can assist in developing and improving specific tailor made risk management strategies and ensure risk intervention at the earliest. Explainable AI tools can rely on prototype models which will have self-learning approaches to adopt and learn model-agnosticexplanations is also highly flexible since it can only access the model’s output. Privacy-aware machine learning tools can also be incorporated into the development of explainable AI tools to avoid possible risks of data breaches and privacy. Compliances may be developed and used for development purposes, including the general mandates that are attributed to them. Conflict Management Compliance by design may become a significant aspect of encouraging the use of regulatory sandboxes and enabling innovation management in more productive ways as possible. In case sandboxes are rendered ineffective, real-time awareness and consumer education must be done, keeping in mind technology products and services accessible and human-centric by design. Risk Management strategies are advised to be incorporated at different stages of AI life cycle from the inception of Data collection and Data training. De-risking AI can involve model risk assessment by classifying AI model based on its risk (High, low, medium) and its contextual usage which will further assist in developers, stakeholders to jointly develop risk mitigation principles according to the level of risk incurred by AI. Deployment of AI explainability measures will require a level of decentralisation where transdisciplinary teams to work closely to provide complete oversight. Risk monitoring should be carried out by data scientists, developers and KMPs to share overlapping information and improve situational analysis of the AI system periodically. Innovation Management The element of trust is necessary and the workflow behind the purpose of data use must be made clear by companies. Even if the legal risks are not foreseeable, they can at least make decisions, which de-risk the algorithmic exploitation of personal & non-personal data, metadata and other classes of data & information. These involve technical and economic choices first, which is why unless regulators come up with straightforward regulatory solutions, companies must see how they can minimise the chances of exploitation and enhance the quality of their deliverables and keeping their knowledge management practices much safer.

DISCLAIMER: The contents of this blog article reflect the personal views of the authors alone and do not constitute the views of any of the authors' affiliated organizations. The contents of the blog article cannot be treated as legal advice under any circumstances. The main author of the article is a Senior Associate at Ratan Samal & Associates and an Arbitrator at the Asia Pacific Centre for Arbitration and Mediation & the Indian Institute of Arbitration and Mediation. This article is co-authored by Abhivardhan, Managing Partner at Indic Pacific Legal Research, Founder, VLiGTA and Chairperson & Managing Trustee at the Indian Society of Artificial Intelligence and Law. Introduction The unified indirect tax system of India, viz., the Goods and Services Tax (GST) has entered its sixth year. Despite its insurmountable potential, several disputes continue to increasingly exist. Even though a major portion of the disputes are against adjudication of the GST Department representing a dispute against the sovereign right, power and function of the Government to levy tax or to withhold refund or grant/ deny a tax incentive, an equally significant portion of GST disputes are also pertaining to contractual rights arising out of contracts entered into between parties where the subject matter relates substantially to the shifting of burden of GST, indemnification by the defaulting party to the aggrieved party for non- payment of GST to the Government, GST reimbursement arrangements, tax-sharing arrangements, deemed export disputes and the like. This article argues that even though a significant portion of disputes under the GST law are non- arbitrable as it pertains to disputes with the Government representing the Sovereign power to tax; contractual disputes arising between companies and other forms of entities and legal persons where the subject matter of the dispute pertains to GST are arbitrable. A clear demarcation and identification of the distinction between the two can significantly aid companies, entities and legal persons in correctly contesting their case before the appropriate forum. Identifying Non-Arbitrable Disputes under the Goods and Services Tax Law The GST law is a combination of multiple statutes, operating simultaneously on the respective subject matters as assigned to it, by its ‘charging mechanism’. The substratum of the GST statutes is ‘supply’ wherein tax is levied on the supply of goods or services or both goods and services. Due to the fact that the spirit of cooperative federalism is imbibed within the GST statutes, the Central Goods and Services Tax (CGST) Act, 2017 and the State Goods and Services Tax (SGST) Act, 2017 levy GST on all intra- State supplies of goods or services or both proportionately and in case a transaction takes place within a Union Territory then the CGST Act, 2017 and the Union Territory Goods and Services Tax (UTGST) Act, 2017 apply proportionately. By proportionate application, it is meant that if the rate of GST for a particular supply is 18%, then 9% CGST and 9% SGST or UTGST as the case may be, will apply. As far as inter-State supplies, imports and exports (including refunds thereof read with provisions of the CGST Act, 2017) are concerned, the Integrated Goods and Services Tax (IGST) Act, 2017 applies and there is no proportionate levy of tax since only one statute applies in such forms of supplies. Coming to the non-arbitrable aspects, the Supreme Court of India in Vidya Drolia & Ors. v. Durga Trading Corporation & Ors., (2021) 2 SCC 1 has held that taxation is the sovereign function of the State and is therefore, non- arbitrable. This means that disputes arising out of adjudication u/s 73 or 74, denial of refund u/s 54, denial of input tax credit u/s 16, cancellation of registration u/s 29, rejection of appeals, order of anti- profiteering u/s 171 of the CGST Act, 2017 and like matters where the dispute is against the Goods and Services Tax Department or the Central Government or the respective State Government, the said form of GST dispute will be non-arbitrable. Hence, the appellate route before the quasi-judicial appellate authority followed by the Goods and Services Tax Appellate Tribunal (not yet constituted), followed by the High Court and the Supreme Court will have to be opted, unless there is violation of fundamental rights, principles of natural justice violation, the order passed is wholly without jurisdiction or if the vires of a particular provision(s) of the GST statute(s) or its respective delegated legislation in the form of rules, notifications, circulars and the like are challenged, in the event of which a Writ Petition can be filed before the High Court directly without undergoing the appellate route. Assessing the Arbitrability of Goods and Services Tax Disputes from Contractual Arrangements This part of the article delves into few of the most common forms of contractual arrangements which are reflected in contractual arrangements, often as a part of clauses of the respective contract. Contractual Shifting of the Burden of GST The contractual shifting of the burden of GST is one of the most common forms of clauses which can be seen in several contracts especially in the construction sector and in contracts with the Government and with public sector undertakings and has also been extant under the erstwhile indirect tax laws. However, it is necessary to point out that the incidence of tax under the GST statutes will not change and the legal person liable to pay tax as per the charging mechanism will have to bear the tax with a subsequent contractual right to recover the amount from the other party in case the other party had agreed under the contract to bear such tax. Under GST, it is the supplier of goods or services or both, as the case maybe, who has to pay the tax under the forward charge mechanism. A few exceptions exist where the recipient of goods or services or both, as the case may be, has been made liable to pay tax under the reverse charge mechanism. For example, if in a transaction where the recipient was supposed to pay GST under the reverse charge mechanism has entered into a contract with its supplier that it is the supplier who will have to bear the GST, then in such circumstances, while filing of the monthly returns in FORM-GSTR-3B, the recipient will have to pay the tax amount under reverse charge mechanism and it will not be open for the recipient to insist recovery from the supplier due to the contract. However, after such payment is made by the recipient, the recipient of the supply would be entitled to recover from the supplier in pursuance of the contractual arrangement between them which foists GST liability on the supplier. In the absence of such contractual arrangement, the recipient would have paid the tax without any further rights for recovering the amount from the supplier, but it is only due to the contractual arrangements for shifting the burden of tax, does the recipient have the right to recover the GST amount from its supplier. In the presence of an arbitration clause in a contract where shifting of burden of taxes have been agreed upon, a dispute between the recipient who is foisted liability under the reverse charge mechanism by the respective GST statute with the Government would be non-arbitrable since it would be a right in rem and also representing the sovereign right of the Government to levy and collect tax from the recipient as per the charging mechanism under GST whereas the subsequent dispute between the recipient and the supplier wherein the supplier had agreed to the shifting of burden of tax would be an arbitrable dispute being a right in personam which arises out of a contract. Similarly, in a transaction where GST is to be paid by the supplier under the forward charge mechanism and a contractual arrangement exists between the supplier and the recipient that the supplier will bear the entire GST amount, the recipient can choose to deduct GST and disentitle the supplier from collecting the tax amount from the recipient, resulting in the supplier paying the taxes from its own pockets instead of collecting it from the recipient as would have been the scenario under normal circumstances. Similar to the aforesaid, a dispute between the supplier and the recipient in respect of the deduction of GST amount from the payment would be a right in personam and arbitrable as per the arbitration agreement envisaged in the contract. The Supreme Court of India in Rashtriya Ispat Nigam Limited v. M/s Dewan Chand Ram Saran, (2012) 5 SCC 306 set aside the judgment of the Bombay High Court which had interfered with an Arbitral Award interpreting a clause of the contract which was pertaining to the shifting of burden of Service Tax. In this case, the parties had entered into a contract wherein the contractor who was the service provider was to bear the entire Service Tax amount. In the absence of the contract, the service provider would collect such tax from the service recipient and pay it to the Government Treasury. However, due to the contractual shifting of burden, the service recipient in the instant case deducted the Service Tax component from the payment of consideration. This resulted in the service provider invoking Arbitration against the service recipient wherein the Arbitrator held that as per the contractual terms between the parties the service recipient was correct in deducting the payments of Service Tax as the burden was on the service provider to bear Service Tax. Upon challenge before the Bombay High Court, the Arbitral Award was interfered and set-aside and upon further appeals to the Supreme Court, the Supreme Court held that the Arbitrator had interpreted the contract correctly and the Bombay High Court’s interference with the Arbitral Award was unjustified. The Delhi High Court in Spectrum Power Generation Limited v. Gail (India) Limited, (2022) SCC OnLine Del 4262 was faced with a dispute arising out of a Gas Sale Agreement wherein the petitioner company had invoked arbitration after failed attempts of conciliation and had filed a petition u/s 11(6) of the Arbitration and Conciliation Act, 1996 before the Delhi High Court for the appointment of an Arbitrator. The respondent company’s argument was that the dispute was non-arbitrable since it was pertaining to a dispute of contractual shift of burden of GST and Value Added Tax (VAT) on gas. However, the Delhi High Court held that such disputes were arbitrable and allowed the petition, resulting in the appointment of an Arbitrator by the Delhi High Court u/s 11(6) of the Arbitration and Conciliation Act, 1996. The Bombay High Court in Angerlehner Structural and Civil Engineering Company v. Municipal Corporation of Greater Bombay, (2022) 103 GSTR 336 in an Arbitration Execution Application was also faced with the question as to whether there was contractual shifting of burden of taxes between the parties. The Court held that no such contractual arrangement existed between the parties and the withholding of GST by the recipient was unjustified and accordingly, the recipient was directed to pay the GST amount to the supplier with interest who in turn would deposit it in the Government Treasury. Therefore, the legal principle which emerge from the aforesaid judgments and discussions is that contractual arrangements for shifting the burden of tax are valid forms of contract and in case of any dispute in respect of the same, such disputes are arbitrable as per the arbitration agreement in the contract. Reimbursement and Tax-Sharing Arrangements Parties may even enter into contractual arrangements pertaining to reimbursement of GST and may also enter into GST sharing arrangements and similar to the scenario for contractual shifting of burden of taxes, the legal person chargeable to tax as per the charging mechanism will have to pay GST and in case of a dispute pertaining to contractual clauses of reimbursement of GST and tax-sharing arrangements, arbitration can be invoked as those would be arbitrable disputes. The Delhi High Court in Indian Railway Catering & Tourism Corporation (IRCTC) Ltd. v. Deepak & Co., (2022) 104 GSTR 475, inter alia, upheld the Award passed by the Arbitrator which granted reimbursement of GST with interest. Although the reasoning of the Arbitrator was upheld on the basis of contractual interpretation, the judgment is also indicative of the fact that reimbursement of GST would be an arbitrable dispute as a contractual right in personam. Indemnification of the Recipient by the Supplier for Default in Payment of GST by the Supplier There is an upsurge in disputes pertaining to input tax credit under GST arising because of the fact that the supplier is not paying tax to the Government Treasury. In the normal chain of transactions, the recipient of goods or service (purchaser) pays the consideration amount as well as the amount of GST charged in the tax invoice raised by the supplier (seller) and the supplier is liable to pay such GST collected from the recipient to the Government Treasury. However, in many cases it is being seen that the supplier, despite having collected tax from the recipient is not depositing it in the Government Treasury resulting in recovery action being taken against the supplier as well as the recipient. Even after having discharged its obligations, the recipient is faced with difficulties due to the inaction of the supplier resulting in the ineligibility of the input tax credit for the recipient in pursuance of Section 16(2)(c) of the CGST Act, 2017. This of course, does not apply to instances where the supplier and the recipient are acting in collusion to defraud the Government but applies in cases where the recipient was under the bona fide belief that its supplier is a genuine dealer and despite the consideration and the GST amount having been paid in full and in time by the recipient to the supplier, the supplier does not deposit GST in the Government Treasury. Parties may choose to incorporate clauses in their contract pertaining to their respective transactions where contingent to the recipient purchaser facing any difficulties from the GST Department due to non- payment of GST in the Government Treasury by the supplier, the recipient will be entitled to be indemnified for the demand created against the recipient by the GST Department. Under normal circumstances, even after paying the GST amount in full to the supplier for depositing in the Government Treasury, due to the inaction or the non- compliance by the supplier, the recipient is saddled with having to reverse input tax credit along with interest at the rate of 24% u/s 50(3) of the CGST Act, 2017 and with penalty u/s 122 r.w.s. 73 or 74 of the CGST Act, 2017 as the case may be. This is why having a contingent contractual clause for indemnity can aid the recipient in being indemnified of the input tax credit reversal, interest and penalty amount suffered by it due to the inaction and non- compliance by the supplier to deposit tax to the Government Treasury. It is noteworthy that since the dispute in this respect would be regarding indemnification arising out of a contingent contract between parties to the contract, such a dispute would be arbitrable. Certain supplies under GST have been treated as deemed exports. When a supplier makes a supply of goods to a recipient registered with an Export Promotion Council or a Commodity Board recognized by the Department of Commerce including Export Oriented Units, such supplies would be treated as deemed exports even though such goods do not leave the territory of India. Additionally, for being treated as deemed exports under GST, the goods must also be exported by the recipient registered with an Export Promotion Council or a Commodity Board recognized by the Department of Commerce including Export Oriented Units to export such goods to a place outside the territory of India within 90 days of issuance of the tax invoice by the supplier, the tax invoice issued must contain the GSTIN of the supplier, the shipping bill or the bill of export must contain the tax invoice number, the recipient must transport the goods directly from the port, inland container depot, airport, land customs station or a registered warehouse from where the goods shall be directly exported and copies of shipping bill or bill of export, export manifest, tax invoice and export report must be provided to the supplier as well as the jurisdictional officer. The benefit of a transaction being treated as a deemed export under GST is that the supplier has to pay tax at a concessional rate of tax after collecting such concessional tax amount from the recipient. The benefit of such concessional rate of tax is provided to deemed export supplies since the transaction is being made in the course and furtherance of export that ultimately results in the generation of valuable foreign currency and therefore, no taxes must be exported in the entire chain of export. Coming to the arbitrability perspective, since there are insurmountable conditions to be fulfilled by the recipient, in case of non-compliance by the recipient of any of the conditions, it is the supplier that faces action from the GST Department wherein tax at the full rate is demanded along with interest and penalty. This is capable of causing significant difficulties for the suppliers in deemed export transactions. In case of scenarios where the recipient does not export the goods within 90 days of issuance of tax invoice by the supplier and in case of non-compliance with the conditions of the export related documents being submitted by the recipient to its jurisdictional officer, in the presence of a contractual arrangement mandating the aforesaid requirements, the supplier would be entitled to invoke arbitration alleging breach of the contractual clauses. This would enable the supplier to recover the tax at the full rate along with interest and penalty paid by it against the demand created by the GST Department due to the non- fulfilment of conditions of deemed export by the recipient of such goods. Conclusion There are manifold possibilities of disputes arising out of contractual arrangements pertaining to GST and only the most common forms of disputes arising between parties in this respect have been discussed in the present article. It is evident from the aforesaid discussions that the presence of contractual arrangements under GST are arbitrable in case of disputes or differences arising out of such contractual clauses and that arbitrating such disputes would significantly assist parties in avoiding payment of the tax, interest and penalty liabilities from their own pockets due to the default of the other party in the transaction as the aggrieved party will be able to invoke Arbitration for recovering the said amounts from the defaulting party.

In 2022, if we understand the interconnected role of the design behind our mainstream social media applications, and the algorithms used to run them, a new trend has become quite real, which would entertain some intriguing legal questions surrounding areas of concern such as competition law, digital accessibility and technology policy. Social media influencers, content creators and even technology geeks, have noticed this trend that various social media applications, be it Instagram or Twitter or any, are now behaving as recommendation media applications. The 10-second video trends promoted by Tiktok , for example, kind of promoted the algorithmic tendencies of recommending content of some favorable parameters, thereby giving a hard time to YouTube and Instagram. Even Spotify has been affected by the 10-second video trends, making recommendation media the recent version of social media.

The 27-nation group has introduced the first AI regulations in the world two with a focus on limiting dangerous but narrowly targeted applications. Lately we have witnessed the increased role of AI in our day to day lives, and it becomes important to regulate the AI models to ensure the integrity and security of Nations. Chatbots and other general-purpose AI systems received very little attention before the coming of ChatGPT, which further reinstated the importance to regulate such models before it creates turbulence in the World Economy. The EU Commission published a proposal for an EU Artificial Intelligence Act in back April 2021, which provoked a heated debate in the EU Parliament amongst political parties, stakeholders, and EU Member States, leading to thousands of amendment proposals. The EU Parliament has approved the passage of the AI Act, which definitely evokes issues of implementation with respect to the AI legislation. In the European Parliament, the provisional AI Act would need to be approved by the joint committee, then debated and voted on by the full Parliament, after which the AI Act is adopted into law. The objectives of the European Union Artificial Intelligence Act are summarised as follows: address risks specifically created by AI applications propose a list of high-risk applications set clear requirements for AI systems for high risk applications define specific obligations for AI users and providers of high risk applications propose a conformity assessment before the AI system is put into service or placed on the market propose enforcement after such an AI system is placed in the market propose a governance structure at European and national level. Defining AI The definitions offered by the participating governments are summarised in the FCAI report. Despite the fact that there is "no single definition" of artificial intelligence, many efforts have been made in that direction. Many attempts have been made to define the term as it will determine the scope of the Legislation. Also, it has to strike the balance between being too narrow to exclude the certain types of AI that needs regulation and too broad a definition risks sweeping up common algorithmic systems that do not produce the types of risk or harm However, the concept in the AI Act is the first definition of AI for regulatory reasons. Earlier definitions of AI appeared in frameworks, guidelines, or appropriations language. The definition that is finally established in the AI Act is likely to serve as a benchmark for other AI policies in other nations, fostering worldwide consensus. According to Article 3(1) of the AI Act, an AI system is “software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.” Risk-based approach to regulate AI A "proportionate" risk-based approach is promised by the AI Act, which imposes regulatory burdens only when an AI system is likely to pose high risks to fundamental rights and safety. The AI Act divides risk into four categories: unacceptable risk, high risk, limited risk, and low risk. These categories are targeted at particular industries and applications. One important topic under discussion by the Parliament and the Council will be the regulation and classification of applications at the higher levels, specifically those deemed to be unacceptably risky, such as social scoring, or high risk, AI interaction with with children in the context of personal development or personalised education. The EU AI Act lays out general guidelines for the creation, commercialisation, and application of AI-driven systems, products, and services on EU soil. The proposed regulation outlines fundamental guidelines for artificial intelligence that are relevant to all fields. Through a required CE-marking process (CE marking indicates that a product has been assessed by the manufacturer and deemed to meet EU safety, health and environmental protection requirements), it establishes requirements certification of High-Risk AI Systems. This pre-market compliance regime also applies to datasets used for machine learning training, testing, and validation in order to guarantee equitable results. The Act aims to formalise the high requirements of the EU's trustworthy AI paradigm, which mandates that AI must be robust in terms of law, ethics, and technology while upholding democratic principles, human rights, and the rule of law. If we talk about India, The Companies Act 2013, lays down the compliance that needs to be met by a company but currently AI Models or the providers do not come under the ambit. India is not planning to develop AI regulatory plans at this point of time but taking inspiration from the EU legislation, it can ensure strict compliance measures for the upcoming players in the industry by taking a cue. This risk-based based pyramid (Figure 1) is combined with a contemporary, layered enforcement mechanism in the draught Artificial Intelligence Act. This implies, among other things, that applications with a low risk are subject to a laxer legal framework, while those with a high risk are prohibited. As danger rises between these two ends of the spectrum, rules become harsher. These range from light, externally assessed compliance requirements throughout the life cycle of the application to strict, non-binding self-regulatory soft law impact evaluations coupled with codes of conduct. Ban on the use of facial biometrics in law enforcement Some Member States want to exclude from the AI Regulation any use of AI applications for national security purposes (the proposals exclude AI systems developed or used “exclusively” for military purpose). Germany has recently argued for ruling out remote real-time biometric identification in public spaces but allowing retrospective identification (e.g., during the evaluation of evidence), and asks for an explicit ban on the use of AI systems substituting human judges, for risk assessments by law enforcement authorities and for systematic surveillance and monitoring of employee performance. AI-related revision of the EU Product Liability Directive (PLD) In EU, manufacturers are subject to strict civil law liability under the PLD for damages resulting from defective products, regardless of negligence. To integrate new product categories arising from digital technologies, like AI, a modification was required. The PLD specifies conditions under which a product will be believed to be "defective" for the purposes of a claim for damages, including the presumption of a causal link if the product is proven to be defective and the damage is ordinarily consistent with that defect. With regard to AI systems, the revision of the PLD aims to clarify that: AI systems and AI-enabled goods are considered “products” and are thus covered by the PLD; and when AI systems are defective and cause damage to property, physical harm or data loss, the damaged party can seek no-fault compensation from the provider of the AI system or from a manufacturer integrating the system into another product. providers of software and digital services affecting the functionality of products can be held liable in the same way as hardware manufacturers; manufacturers can be held liable for subsequent changes made to products already placed on the market, e.g., by software updates or machine learning; and Talking in Indian context, the Consumer Protection Act talks of product liability and marked an end of the buyer beware doctrine and the introduction of seller beware as the new doctrine governing the Consumer Protection Act. Section 84 of the Act enumerates the situations where a product manufacturer shall be liable in a claim for compensation under a product liability action for a harm caused by a defective product manufactured by the product manufacturer. But this doesn't apply to AI models currently running in India and keeping in mind the future needs, we must ensure provisions on Protection of consumers on priority basis. Impact on Businesses AI has enormous potential for progress in both technology and society. It is transforming how businesses produce value across a range of sectors, including healthcare, mining, and financial services. Companies must handle the risks associated with the technology if they want to use AI to innovate at the rate necessary to stay competitive and maximise the return on their AI investments. Businesses who are experiencing the greatest benefits from AI are much more likely to say that they actively manage risk than those whose outcomes are less promising. As per the Provisions of the Act, it includes fines of up to €30 million or 6 percent of global revenue, making penalties even heftier than those incurred by violations of Regulation Act. The use of prohibited systems and the violation of the data-governance provisions when using high-risk systems will incur the largest potential fines. All other violations are subject to a lower maximum of €20 million or 4 percent of global revenue, and providing incorrect or misleading information to authorities will carry a maximum penalty of €10 million or 2 percent of global revenue. Although enforcement rests with member states, as is the case for GDPR, it is expected that the penalties will be phased in, with the initial enforcement efforts concentrating on those who are not attempting to comply with the regulation. The regulation would have extraterritorial reach, meaning that any AI system providing output within the European Union would be subject to it, regardless of where the provider or user is located. Individuals or companies located within the European Union, placing an AI system on the market in the European Union, or using an AI system within the European Union would also be subject to the regulation. Endnote The unique legal-ethical framework for AI expands the way of thinking about regulating the Fourth Industrial Revolution (4IR) which includes the coming of cutting-edge technology in the form of Artificial Intelligence, and applying the proposed laws will be a completely new experience. From the first line of code, awareness is necessary for responsible, trustworthy AI. The future of our society is being shaped by the way we develop our technologies. Fundamental rights and democratic principles are important in this vision. AI impact and conformance evaluations, best practices, technological roadmaps, and conduct codes are essential tools to help with this awareness process. These technologies are used to monitor, validate, and benchmark AI systems by inclusive, multidisciplinary teams. Ex ante and life-cycle audits will be everything. The new European rules will forever change the way AI is formed. Not just EU, but in the coming days, other countries too would be in need to set-up a regulatory framework on AI and this GDPR would definitely guide them.

Please note: this article is a long-read.

Please note: this is a Policy Brief by Anukriti Upadhyay, former Research Intern at the Indian Society of Artificial Intelligence and Law. In a 3-page letter to Satya Nadella, Twitter's company, X Corp. had stated that Microsoft had violated an agreement over its data and had declined to pay for that usage. And in some cases, Microsoft had used more Twitter data than it was supposed to. Microsoft also shared the Twitter data with government agencies without permission, the letter said. To sum up, Twitter is trying to charge Microsoft for its data which has earned huge amount of profit to Microsoft. Mr. Musk, who bought Twitter last year for $44 billion, has said that it is urgent for the company to make money and that it is near bankruptcy. Twitter has since then introduced new subscription products and made other moves to gain more revenue. Also, in March, the company had stated it would charge more for developers to gain access to its stream of tweets. Elon Musk and Microsoft have had a bumpy relationship recently. Among other things, Mr. Musk has concerns with Microsoft over OpenAI. Musk, who helped found OpenAI in 2015, has said Microsoft, which has invested $13 billion in OpenAI, controls the start-up’s business decisions. Of course, Microsoft has disputed that characterisation. Microsoft’s Bing chatbot and OpenAI’s ChatGPT are built from what are called large languages models, or LLMs, which build their skills by analysing vast amounts of data culled from across the internet. The letter to Satya Nadella does not specify if Twitter will take legal action against Microsoft or ask for financial compensation. It demands that Microsoft abide by Twitter’s developer agreement and examine the data use of eight of its apps. Twitter has hired legal services which seeks report by June on how much Twitter data the company possesses, how that data was stored and used, and when government-related organizations gained access to that data. Twitter’s rules prohibit the use of its data by government agencies, unless the company is informed about it first. The letter adds that Twitter’s data was used in Xbox, Microsoft’s gaming system; Bing, its search engine; and several other tools for advertising and cloud computing. “the tech giant should conduct an audit to assess its use of Twitter's content.” Twitter claimed that the contract between the two parties allowed only restricted access to the twitter data but Microsoft has breached this condition and has generated abnormal profits because of using Twitter’s API. Currently, there are many tools available (from Microsoft, Google, etc.) to check the performance of AI systems, but there is no regulatory oversight. And that is why, experts believe that companies, new and old, need to put more thought into self-regulation. This dispute has highlighted the need to keep a check on the utilization of data by companies to develop their AI models and regulate them. Data Law and Oversight Concerns In this game of tech giants to win the race of AI development, the biggest impact is always bestowed upon the society. Any new development is prone to attract illegal activities that can have a drastic effect on the society. Even though the Personal Data Protection Bill is yet to become law, big tech firms like Google, Meta, Amazon and various e-commerce platforms are liable to be penalised for sharing users’ data with each other if consumers flag such instances. Currently in India, under the Consumer Protection Act, 2019, the department can take action and issue directions to such firms. Since the data belongs to a consumer, if the consumer feels that their data is being shared amongst firms without their express consent, they are free to approach us under the Consumer Protection Act. If we look at the kind of data which is shared between firms, any search on Google by a person leads to the same feeds being shown on Facebook. This means that user data is being shared by big tech firms. In case the data is not shared with the express consent of users concerned, they can approach the Consumer Protection Forums. The same is relevant to the Twitter-Microsoft dispute, wherein the data used by the latter was put up by the Twitter users on their twitter account and the same was getting used by Microsoft without the user’s consent. If we analyse WhatsApp's data sharing policies for example, Meta has stated that it can share business data with Facebook. But at the same time, the Competition Commission of India has objected to this as a monopolistic practice and the matter is in court. Consumers have the right to seek redressal against unfair / restrictive trade practices or unscrupulous exploitation of consumers. Protecting personal data should be an essential imperative of any democratic republic. Once it becomes law, citizens can intimate all digital platforms they deal with to delete their past data. The firms concerned will then need to collect data afresh from users’ and clearly spell out the purpose and usage. They will be booked for data breach if they depart from the purpose for which it was collected. Data minimisation, purpose limitation and storage limitation are the hallmarks which cannot be compromised with. Data minimisation means firms can only collect the absolute minimum required data. Purpose limitation will allow them to use data only for the purpose for which it has been acquired. With storage limitation, once the service is delivered, firms will need to delete the data. With the rapid development of AI, a number of ethical issues have cropped up. These include: the potential of automation technology to give rise to job losses the need to redeploy or retrain employees to keep them in jobs the effect of machine interaction on human behaviour and attention the need to address algorithmic bias originating from human bias in the data the security of AI systems (e.g., autonomous weapons) that can potentially cause damage While one cannot ignore these risks, it is worth keeping in mind that advances in AI can - for the most part - create better business and better lives for everyone. If implemented responsibly, artificial intelligence has immense and beneficial potential. Investment and Commercial Licensing AI has been called the electricity of the 21st century. While the uses and benefits of AI are exponentially increasing, there are challenges for businesses looking to harness this new technological advancement. Chief among the challenges are: The ethical use of AI, Legal compliance regarding AI and the data that fuels AI, Protection of IP rights and the appropriate allocation of ownership and use rights in the components of AI. Businesses also need to determine whether to build AI themselves or license it from others. Several unique issues impact AI license agreements. In particular, it is important to address the following key issues: “IP ownership and use rights, IP infringement, Warranties, specifically performance promises and Legal compliance.” Interestingly, IP treaties simply have not caught up to AI yet. While aspects of AI components may be protectable under patents, copyrights, and trade secrets, IP laws primarily protect human creativity. Because of the focus on human creation, issues may arise under IP laws if the AI output is created by the AI solution instead of a human creator. Since the IP laws do not squarely cover AI, as between an AI provider and user, contractual terms are the best way to attempt to gain the benefits of IP protections in AI license agreements. How Does it Affect the Twitter-Microsoft Relationship Considering this issue, the parties could designate certain AI components as trade secrets. Protect AI components by: limiting use rights; designating AI components as confidential information in the terms and conditions; and restricting use of confidential information. Include assignment rights in AI evolutions from one party or the other. Determine the license and use rights the parties want to establish between the provider and the user for each AI component. Clearly articulate the rights in the terms and conditions. The data sharing agreement must cover which party will provide and own the training data, prepare and own the training instructions, conduct the training, and revise the algorithms during the training process and own the resulting AI evolutions. As for data ownership, the parties should identify the source of the data and ensure that data use complies with applicable laws and any third-party data provider requirements. Ownership and use of production data for developing AI models must be set out in the form of terms and conditions which party provides and which party owns the production data that will be used. If the AI solution is licensed to the user on-premises (the user is running the AI solution in the user’s systems and environment), it is likely that the user will supply and own the production data. However, if the AI solution is cloud-based, the production data may include the data of other users. In a cloud situation, the user should specify whether the provider may use the user’s data for the benefit of the entire AI user group or solely for the user’s particular purposes. It is important to note that limiting the use of production data to one user with an AI solution may have unintended results. In some AI applications, the use of a broader set of data from multiple users may increase the AI solution’s accuracy and proficiency. However, counsel must weigh the benefits of permitting a broader use of data against the legal, compliance, and business considerations a user may have for limiting use of its production data. When two or more parties are each contributing to the AI evolutions, the license agreement should appoint a contractual owner. The parties must then determine who will own AI evolutions or whether AI evolutions will be jointly owned, which presents additional practical challenges. The use of AI presents ethical issues and the organizations must consider how they will use AI and define principles and implement policies regarding the ethical use of AI. One portion of the AI ethical use consideration is legal compliance, which is another issue that is more challenging for AI than for traditional software or technology licensing. AI-based decisions must satisfy the same laws and regulations that apply to human decisions. AI is different from many other technologies because AI can produce legal harms against people and some of that legal harm might not only violate ethical norms, but may also be actionable under law. It is important to address legal compliance concerns with the provider before entering into an AI license agreement to determine which party is responsible for compliance. Some best practices that could be adopted, are proposed as follows: To deal with legal compliance issues in investment and licensing, companies can conduct diligence on data sharing to determine if there are any legal or regulatory risk areas that merit further inquiry. Develop policies around data sharing and involve the various stakeholders in the policy-making process to ensure that thoughtful consideration is given about when it is appropriate to use the data and in what contexts. Implement a risk management framework that includes a system of ongoing monitoring and controls around the use of AI. Consider which party should obtain third-party consents for data use due to potential privacy and data security issues. AI is transforming our world rapidly and without much oversight. Developers are free to innovate, as well as to create tremendous risk. Very soon leading nations will need to establish treaties and global standards around the use of AI, not unlike current discussions about climate change. Governments will need to both: Establish laws and regulations that protect ethical and productive uses of AI. Prohibit unethical, immoral, harmful, and unacceptable uses. These laws and regulations will need to address some of the IP ownership, use rights, and protection issues discussed in this article. However, these commercial considerations are secondary to the overarching issues concerning the ethical and moral use of AI. In line with the increased attention on corporate responsibility and issues like diversity, sustainability, and responsibility to more than just investors, businesses that develop and use AI will need policies and guidance against which the use of AI should be assessed and utilised. These policies and guidance are worthy of board-level attention. Technology lawyers who in these early days assist clients with AI issues must monitor developments in these areas and, wherever possible, act as facilitators and leaders of thoughtful discussions regarding AI. Also, adapting the precautionary measures will save a lot of legal cost for the companies and will ensure that the data is not misused or oversued.

Artificial intelligence is booming the industry, but the question remains about the regulation as this is only a precaution that can put constraints on innovation. For example, a government report in Singapore highlighted the risks posed by AI but concluded that ‘it is telling that no country has introduced specific rules on criminal liability for artificial intelligence systems. Being the global first-mover on such rules may impair Singapore’s ability to attract top industry players in the field of AI[1].’ These concerns are well-founded. As in other areas of research, overly restrictive laws can stifle innovation or drive it elsewhere. Yet the failure to develop appropriate legal tools risks allowing profit-motivated actors to shape large sections of the economy around their interests to the point that regulators will struggle to catch up. This has been particularly true in the field of information technology. For example, social media giants like Facebook monetized users’ personal data while data protection laws were still in their infancy[2]. Similarly, Uber and other first-movers in what is now termed the sharing or ‘gig’ economy exploited platform technology before rules were in place to protect workers or maintain standards. As Pedro Domingo once observed, people worry that computers will get too smart and take over the world; the real problem is that computers are too stupid and have already taken over[3]. Much of the literature on AI and the law focuses on a horizon that is either so distant that it blurs the line with science fiction or so near that it plays catch-up with the technologies of today. That tension between presentism and hyperbole is reflected in the history of AI itself, with the term ‘AI winter[4]’ coined to describe the mismatch between the promise of AI and its reality. Indeed, it was evident back in 1956 at Dartmouth when the discipline was born. To fund the workshop, John McCarthy and three colleagues wrote to the Rockefeller Foundation with the following modest proposal: [W]e propose for 2 months and 10 men needed for the study of artificial intelligence will be carried out in the summer of 1956 ……… The study was on the conjecture nature of learning where the machines should be made intelligent to stimulate it. In this study, an attempt will be made to find out how machines use language for the concept to solve problems, reserved for humans. We think that significant advancement can be made and only a selected group of people will work on the summer project.” The innovation in the field of AI was started a long time ago but there were no precautions and regulations to put the use of AI in control. Every entity on the planet Earth can agree to the term that AI can be more fearful than one’s thought. Just as the statement by the AI robot Sofia “she plans to take over the human being and their existence. Moreover, the website run by AI shows the last picture of humans as very degraded beings. As said in the statement by Pablo Picasso[5] “the new mechanical brains are useless, they only provide an answer that was taught to them” As countries around the world struggle to capitalize on the economic potential of AI while minimizing avoidable harm, a paper like this cannot hope to be the last word on the topic of regulation. But by examining the nature of the challenges, the limitations of existing tools, and some possible solutions, it hopes to ensure that we are at least asking the right questions. As it is said every space in nature and physics needs to be fulfilled otherwise it would create a hole -a black hole. The paper "Neurons Spike Back: A Generative Communication Channel for Backpropagation" presents a new approach to training artificial neural networks that is based on an alternative communication channel for backpropagation. Backpropagation is the most widely used method for training neural networks, and it involves the use of gradients to adjust the weights of the network. The authors propose a novel approach that uses spikes as a communication channel to carry these gradients. The paper begins by introducing the concept of spiking neural networks (SNNs) and how they differ from traditional neural networks. SNNs are modelled after the way that biological neurons communicate with each other through spikes or action potentials. The authors propose using this communication mechanism to transmit the gradients during backpropagation. But before that we need to understand what is deep learning and the neural networks and deep neural networks. Inductive & Deductive Machines in Neural Spiking Inductive machines are also known as unsupervised learning machines. They are used to identify patterns in data without prior knowledge of the output. Inductive machines make use of a clustering algorithm to group similar data together. An example of an inductive machine is the self-organizing map (SOM). SOMs are used to create a two-dimensional representation of high-dimensional data. For example, if you have a dataset consisting of several features such as age, gender, income, and occupation, an SOM can be used to create a map of this data where similar individuals are placed close together. On the other hand, deductive machines are also known as supervised learning machines. They are used to learn from labeled data and can be used to make predictions on new data. An example of a deductive machine is the multi-layer perceptron (MLP). MLPs consist of multiple layers of interconnected nodes that are used to classify data. For example, if you have a dataset consisting of images of cats and dogs, an MLP can be trained on this data to classify new images as either a cat or a dog. Neural spiking is the process of representing information using patterns of electrical activity in the neurons of the brain. Inductive and deductive machines can both be used to model neural spiking, but they differ in their approach. Inductive machines can be used to identify patterns in the spiking activity of neurons without prior knowledge of the output. Deductive machines, on the other hand, can be used to predict the spiking activity of neurons based on labeled data. How Deep Learning + Neural Networks Work Deep learning is a subset of machine learning that utilizes artificial neural networks to learn from large amounts of data. Neural networks, in turn, are models that are inspired by the structure and function of the human brain. They are capable of learning and recognizing patterns in data, and can be trained to perform a wide range of tasks, from image recognition to natural language processing. At the heart of a neural network are nodes, also known as neurons, which are connected by edges or links. Each node receives input from other nodes and computes a weighted sum of those inputs, which is then passed through an activation function to produce an output. The weights of the edges between nodes are adjusted during training to optimize the performance of the network.[6] In a deep neural network, there are typically many layers of nodes, allowing the network to learn increasingly complex representations of the data. This depth is what sets deep learning apart from traditional machine learning approaches, which typically rely on shallow networks with only one or two layers. Deep learning has been applied successfully to a wide range of tasks, including computer vision, natural language processing, and speech recognition. One of the most well-known applications of deep learning is image recognition, where deep neural networks have achieved state-of-the-art performance on benchmark datasets such as ImageNet. However, deep learning also has some limitations. One of the main challenges is the need for large amounts of labeled data to train the networks effectively. This can be a significant barrier in areas where data is scarce or difficult to label, such as medical imaging or scientific research. Another limitation of deep learning is its tendency to be overfitted to the training data. This means that the network can become too specialized to the specific dataset it was trained on and may not generalize well to new data. To address this, techniques such as regularization and dropout have been developed to help prevent overfitting. Despite these limitations, deep learning has had a significant impact on many areas of research and industry. In addition to its successes in computer vision and natural language processing, deep learning has also been used to make advances in drug discovery, financial forecasting, and autonomous vehicles, to name a few examples. One of the reasons for the success of deep learning is the availability of powerful hardware, such as GPUs, that can accelerate the training of neural networks. This has allowed researchers and engineers to train larger and more complex networks than ever before, and to explore new applications of deep learning. Another important factor in the success of deep learning is the availability of open-source software frameworks such as TensorFlow and PyTorch. These frameworks provide a high-level interface for building and training neural networks and have made it much easier for researchers and engineers to experiment with deep learning. Spiking Neural Networks A spiking neural network (SNN) is a type of computer program that tries to work like the human brain. The human brain uses tiny electrical signals called "spikes" to send information between different parts of the brain. SNNs try to do the same thing by using these spikes to send information between different parts of the network. SNNs work by having lots of small "neurons" that are connected together. These neurons can receive input from other neurons, and they send out spikes when they receive enough input. The spikes are then sent to other neurons, which can cause them to send out their own spikes. SNNs can be used to do things like recognize images, control robots, and even help people control computers with their thoughts. They can also be used to study how the brain works and to build computers that work more like the brain[7]. The basic structure of an SNN consists of a set of nodes, or neurons, that are interconnected by synapses. When a neuron receives input from other neurons, it integrates that input over time and produces a spike when its activation potential reaches a certain threshold. This spike is then transmitted to other neurons in the network via the synapses. There are several ways to implement SNNs in practice. One common approach is to use rate-based encoding, where information is represented by the firing rate of a neuron over a certain time period. In this approach, the input to the network is first converted into a series of spikes, which are then transmitted through the network and processed by the neurons.[8] One example of an application of SNNs is in image recognition. In a traditional neural network, an image is typically represented as a set of pixel values that are fed into the network as input. In an SNN, however, the image can be represented as a series of spikes that are transmitted through the network. This can make the network more efficient and reduce the amount of data that needs to be processed. Another example of an application of SNNs is in robotics. SNNs can be used to control the movement of robots, allowing them to navigate complex environments and perform tasks such as object recognition and manipulation. By using SNNs, robots can operate more efficiently and with greater accuracy than traditional control systems. SNNs are also being explored for their potential use in brain-computer interfaces (BCIs). BCIs allow individuals to control computers or other devices using their brain signals, and SNNs could help improve the accuracy and speed of these systems. One challenge in implementing SNNs is the need for specialized hardware that can efficiently process and transmit spikes. This has led to the development of neuromorphic hardware, which is designed to mimic the structure and function of the brain more closely than traditional digital computers. Despite these challenges, SNNs are a promising area of research that has the potential to improve the efficiency and accuracy of a wide range of applications, from image recognition to robotics to brain-computer interfaces. As researchers continue to explore the capabilities of SNNs, we can expect to see new and innovative applications of this technology emerge in the years to come. The authors then present the results of experiments that compare their approach to traditional backpropagation methods. They demonstrate that their method achieves comparable results in terms of accuracy but with significantly lower computational cost. They also show that their method is robust to noise and can work effectively with different types of neural networks. Overall, the paper presents a compelling argument for the use of spiking neural networks as a communication channel for backpropagation. The proposed method offers potential advantages in terms of computational efficiency and noise robustness. The experiments provide evidence that the approach can be successfully applied to a range of neural network architectures. References [1] Penal Code Review Committee (Ministry of Home Affairs and Ministry of Law, August 2018) 29. China, for its part, included in the State Council’s AI development [2] Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power [3] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World [4] AI is whatever hasn’t been done yet.’ See Douglas R Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (Basic Books 1979) 601. [5] William Fifield, ‘Pablo Picasso: A CompInterviewrview’ (1964) [6]NeuronsSpikeBack.pdf (mazieres.gitlab.io) [7] https://analyticsindiamag.com/a-tutorial-on-spiking-neural-networks-for-beginners/ [8] https://cnvrg.io/spiking-neural-networks/

Generative AI has become an industry topic of utmost vogue, that perhaps we see a period of intellectual trespassing on this piece of technology. There is nothing surprising about it, since the technology itself has huge computational capabilities and represents a new form of technology portability, in the age of artificial intelligence. While some investors and start-up entrepreneurs may claim the rise of generative AI as a measure to develop early-stage AGI (artificial general intelligence), we believe the real picture is too complex to be side-lined or presumed. In this report, all of the authors have provided a wider picture of the generative AI landscape in the context of the global economy. We have adopted a classification-based approach, where we have sorted out some of the most mainstream use cases of generative AI tools, and provided ontological categories to such applications. Yashudev Bansal, Research Intern, Indian Society of Artificial Intelligence and Law, has contributed to the overview of the generative AI landscape and offered amazing insights on the use cases of large language models for use cases such as legal management and drafting. Kapil Naresh, Founder, Juriaide, has offered profound insights and a proper dissection of various legal issues related to (1) Proprietary Issues related to Intellectual Property Law, (2) Data Quality, Privacy & Content Issues, (3) Pseudonymous Disruption and (4) Digital Public Infrastructure. It has been an honour to provide insights and exploration of legal issues such as (1) Artificial Intelligence Hype, (2) Product-Service Classifications, (3) Unclear Derivatives & Derivatives of Derivatives, (4) Proprietary Issues related to Intellectual Property Law and (5) Digital Public Infrastructure. To conclude, it has been my pleasure to develop this report after weeks of deliberation among the authors of this report, and my VLiGTA Research Team. I express my special regards to Sanad Arora, Junior Research Associate at VLiGTA and Vagish Yadav, Advocate, the High Court of Allahabad, for their moral support. You can read an overview of the report here. We are grateful to Rodney D Ryder, Founding Partner, Scriboard for authoring a Foreword to this technical report. Anyone who is interested to discuss about the nuances of the report, can contact us at vligta@indicpacific.com. The report is now available on the VLiGTA.App: https://vligta.app/product/deciphering-regulative-methods-for-generative-ai-vligta-tr-002/

Lawyering is not limited to litigation. The diversity of professional opportunities for legal professionals exist in various forms and it will only increase in the information age. Now, in this article, let us understand the concept of a multi-polar world order (in International Relations (IR)), and what implications does a multipolar world, have on the legal profession as well as the field of law. We are already seeing some trends in the modern world, which will be discussed in this article.

This article is co-authored by Tara Ollapally, CAMP Arbitration & Mediation Practice. Introduction: Tend and Befriend Responses to Conflict Conflicts are ubiquitous, unavoidable, and almost always uncomfortable. An inevitable consequence of human interaction, conflict, if managed well can be a source of innovation, creativity, growth and meaningful relationships. Any conflict originates from differences. Differences in ideas, values, or perceptions of facts. These differences if not handled well will lead to disagreements, and disagreements if not respectfully managed will lead to disputes which eventually could lead to all-out conflict. If it is escalation of the differences that causes conflicts, it is also inversely true that to resolve conflicts we must de-escalate the situation to resolve them more efficiently. The importance of handling a conflict at the earliest stems from the intrinsic link between cause and consequence.[1] The primary reason for conflicts is the urge to protect something or someone deeply attached to the conflicting parties.[2] Christopher Moore gives a particularly easy understanding of different types of conflicts, since resolving the different types of conflict will require different approaches. He calls it the ‘Circle of Conflict’[3]. As per Moore, conflicts are divided into 5 types, Value Conflicts, Relationship Conflicts, Structural Conflicts, Interest Conflicts, and Data Conflicts. Responses to Conflict Neurobiology research, first understood and described by Walter Cannon in 1932 has understood the human stress response to most commonly be Fight, Flight, Freeze[4] - to get aggressive and fight, to run away from the conflict or to freeze and not take any action hoping for the situation to pass.[5] Recent research from UCLA has shed light on another common response to stress – Tend and Befriend[6]- to build a connection between the conflicting parties, allowing for vulnerability and understanding.[7] This research shows that humans have used social relationship not only as a basic accommodation to the exigencies of life, but also as a primary resource for dealing with stressful circumstances.[8] In this article we share that Mediation as a dispute resolution process promotes the Tend and Befriend response. To holistically address disputes, systems must be designed to evoke this natural human response. A mature legal system that acknowledges building bridges and fostering relationships as a way our species responds to conflict will make mediation a recognised process in its dispute resolution system. Mediation as a way to enhance the Tend and Befriend response Formal legal systems are traditionally an adversarial process wherein conflicting parties are set up as adversaries and a determination of right/ wrong is made by a neutral third person. This process triggers the fight response in conflict. Mediation, as a dispute resolution process, is designed around enhancing collaboration and brings two conflicting parties together to understand, dialogue and reach an amicable solution. It creates a conducive environment whereby the parties are able to form a connection and build on it. It triggers the response under stress to affiliate and connect.[9] The ‘tend and be-friend’ approach is built on this response, where human beings come together to protect themselves. Mediation provides instrumental social support that involves providing tangible assistance as part of a social network of mutual assistance and obligations[10]. Although collaborative processes were ingrained in our traditional social system, 300 years of the formal court system has greatly impacted the collaborative response in conflict. The wise old person in the village who the community turned to and evoked the “tend and befriend’ response was replaced by the powerful village head who incited the response to fight. Formal systems that were built on the Anglo Saxon model completely replaced traditional systems that promoted dialogue, preserved relationships and focussed on win/win outcomes. To nurture and reacquaint ourselves to the tend and befriend response, strong action is needed. The Mediation Bill, 2021 which is currently under consideration at the Parliament proposes mandatory mediation for civil and commercial disputes. We welcome this step and believe that if well implemented, it could provide the impetus to develop a whole new way to resolve disputes – a way that is not only inherently natural but also badly needed in our country today. Mandatory Mediation for India “Constitutional morality is not a natural sentiment. It has to be cultivated." - B.R. Ambedkar, Annihilation of Caste India has consistently used strong laws to drive social change - whether it was the 1843 Indian Slavery Act that abolished slavery and helped changed minds about this abominable practice or The Hindu Child Marriage Restraint Act, 1929 replaced by the prohibition of Child Marriage Act, 2006 that prohibited child marriage and imposed sanctions for the same or the The Child Labour (Prohibition and Regulation) Act 1986[11] that prohibited the employment of children under 14 years or the Protection of Women from Domestic Violence Act, 2005; India has successfully used strong laws to bring about social transformation. To encourage a collaborative mind set, mediation must be strongly encouraged through legislation. A culture of ‘mediation first’ can be effectively promoted through policy. Countries round the world have successfully experimented with mandatory mediation models to not only reduce burden on courts but also encourage behavioural change when responding to conflict. Mandatory Mediation Internationally Italy serves as one of the most leading examples of a successful mandatory mediation law and policy. Voluntary mediation was first introduced as an option to disputants in Italy in 2003 but was hardly used. In 2010 Italian lawmakers introduced mandatory mediation legislation, recognising a clear reluctance by parties to engage in mediation and to address the heavily overburdened courts. Legislative Decree No. 28/2010 required mandatory mediation for certain kinds of disputes.[12] Before a filing in court, parties and lawyers are required to engage in an initial mediation session with an ability, thereafter, to easily opt out of mediation. Tax reliefs were offered to parties who engaged in the mediation process, and it was to be quadrupled if an agreement was achieved. This mandatory initial mediation session model has not only drastically increased the number of cases that attempted and settled in mediation but also recorded a substantial decrease in the number of court filings.[13] In Singapore, Mediation is divided into court-annexed and private. In 2010, the State Courts increased the use of mediation in civil disputes by adopting the 'ADR Form at the Summons for Directions' stage. Both attorneys and clients are required to sign a document certifying that they have explored ADR possibilities and indicating their decision regarding the same. In 2012, a "presumption of ADR" was implemented, which requires all civil cases to be automatically directed to mediation or other types of ADR unless one or more parties opted out. Refusing to employ ADR for reasons considered unacceptable by the registrar results in financial fines under Rules of Court Order[14]. Mediation in the European Union has also had more success when it involves elements of mandatory nature.[15] Turkey introduced mandatory mediation for certain categories of disputes and has recorded a drop of up to 70% in court filings in those categories[16]. Greece, and the UK are also using strong mandatory mediation policies to increase the culture of collaboration and reduce pendency in courts. India is in desperate need of multiple solutions to address the crisis of 4.7 crore cases pending in our courts[17]. An efficient [18] process that promotes a culture of dialogue and respectful understanding must be a choice for every Indian. India attempted mandatory mediation by amending the Commercial Courts Act, 2015, which did not yield desired results. Unfortunately it did not include a strong sanction for non-appearance and provided an exception for cases that needed interim relief. This became the Achilles heel in the law and rendered it practically useless. The draft Mediation Bill 2021 that is currently pending before the Indian Parliament proposes mandatory mediation for all civil and commercial cases before the institution of a suit. If it is drafted in a manner that ensures a strong push towards mediation but also allows for disputants to easily access the courts after a meaningful initial attempt, we are creating the possibility of a mediation first culture that will reduce court filings and promote peace. Needless to say, strong professionals who understand the process and are skilled to facilitate dialogue and resolution is a non-negotiable element in making this policy a success. Conclusion As a human species, we now know that our human brain is capable of evoking a response of tend and befriend under stress. This response stimulates the evolved neocortex part of our brain where rational decisions and creative problem solving is possible[19]. As a legal system we are in desperate need of options and alternatives – our courts, the only option for dispute resolution in India, are facing an impossible case load that is only increasing. As a society, our ability to dialogue, understand each other and collaborate is essential for us to be able to solve our most urgent problems on which our survival depends. Laws play a significant role in influencing behavioural change. A law that encourages dialogue and collaboration of the disputants and promotes an efficient process that finds quick, sustainable resolution seems like a win/win option for India. We welcome India’s move to introduce mandatory mediation. All eyes now, on a well drafted law that will get the disputant to the mediation table but also preserves every Indian’s fundamental right to access to justice. References [1] Sriram Panchu, Mediation: Practice and Law (The Path to Successful Dispute Resolution), 3rd Edition. [2] Beer and Packard, The Mediator’s Handbook, 4th Edition. [3] Christopher Moore, The Mediation Process: Practical Strategies for Resolving Conflict, 3rd., (San Francisco: Jossey-Bass Publishers, 2004) [4] Canon 1932 [5] Shelley E. Taylor, Laura Cousino Klein, Brian P. Lewis, Tara L. Gruenewald, Regan A. R. Gurung, and John A. Updegraff, Biobehavioral Responses to Stress in Females: Tend-and-Befriend, Not Fight-or-Flight, Psychological Review 2000, Vol. 107, No. 3, 411-429 (https://scholar.harvard.edu/marianabockarova/files/tend-and-befriend.pdf) [6] ibid [7] Derba Gerardi, Perspectives on Leadership, The American Journal of Nursing, (September 2015), Vol 115 No 9, 61. [8] Shelley E. Taylor, Tend and Befriend Theory, Handbook of Theories of Social Psychology. Sage Publications, 2011. [9] Shelley E. Taylor, Tend and Befriend: Biobehavioural Bases of Affiliation under Stress, Current Directions under Psychological Science, (December 2006), Vol 15 No 6, 273. [10] Shelley E. Taylor, Tend and Befriend Theory, Handbook of Theories of Social Psychology. Sage Publications, 2011. [12] Disputes related to condominiums, property, division of goods (or partition), family-business covenants and agreements, wills and inheritance, leases, loans, business rents, medical and paramedical malpractice, libel, insurance, and banking and financial contracts. Legislative Decree No. 28 of 4th March 2010, Italy. [13] Leonardo D’ Urso, Italy’s ‘Required Initial Mediation Session’: Bridging The Gap between Mandatory and Voluntary mediation https://www.adrcenterfordevelopment.com/wp-content/uploads/2020/04/Italys-Required-Initial-Mediation-Session-by-Leonardo-DUrso-5.pdf [14] Code of Ethics and Basic Principles of Court Mediation, available at http://www.subccourts.gov.sg, under “Civil Justice Division, Court Dispute Resolution/Mediation”. [15] Giuseppe De Palo; Romina Canessa, Sleeping - Comatose Only Mandatory Consideration of Mediation Can Awake Sleeping Beauty in the European Union, 16 Cardozo J. Conflict Resol. 713 (2014). [16] Tuba Bilecik, Turkish Mandatory Mediation Expands Into Commercial Disputes, http://mediationblog.kluwerarbitration.com/2019/01/30/turkish-mandatory-mediation-expands-into-commercial-disputes/ [17] Over 4.70 crore cases pending in various courts: Govt https://economictimes.indiatimes.com/news/india/over-4-70-crore-cases-pending-in-various-courts-govt/articleshow/90447554.cms?from=mdr [18] In private mediation nearly 70% of cases settle within 3 months. In court mediation programs, specifically at the Bangalore Mediation Centre of the Karnataka High Court the settlement rate is 66% in 90 days (Strengthening Mediation in India: A Report on Court-Connected Mediations, Vidhi Centre for Legal Policy Table 8) [19] Cloke, K., 2013. Bringing Oxytocin into the Room: Notes on the Neurophysiology of Conflict About the Author Mohit Mokal is a Senior Associate, Mediation at CAMP Arbitration & Mediation Practice and Tara Ollapally is the Co-Founder & Mediator at CAMP Arbitration & Mediation Practice. The opinions expressed in this article are those of the authors. They do not purport to reflect the opinions or views of Indic Pacific Legal Research LLP or its members.