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Trade Laws in the Digital Era: A Case for Integration of Artificial Intelligence

Washington DC has recently taken to public criticality of India's e-commerce policy as well as data localisation rules in place. They cite "discrimination" and "distortive of trade" for this reason of their displacement with the Indian policymakers. The American rationale revolves around the data-intensive requirements of the fresh Indian policies coupled with the maintenance of heightened levels pf import tariffs on several goods. This displays the lack of clarity and transparency in the Indian Trade Laws. The resultant of this would undoubtedly be adverse effects on the import chain of Indian businesses and would saturate the Indian import market, making it inaccessible for many sections of the society in India.

It is widely accepted that digitization is the next big thing for generations to come. The same has been observed in the subject matter of trade as well. Trade despite being a concept of ancient origin and application has been extra-susceptible to digitization. This is observed as, "by 2023, digital trade represented 25% of global trade."

Furthermore, there seems to be a significant spike in the international trade of intangible goods. So much so that, the 32% of share for intangible goods 2014 more than doubled in the year 2017. This is an outline for why the digital trade is on a substantial rise as trade of the intangibles can only be mediated through digital trade.

There has been recent trend to Artificial Intelligence (A.I.) introduction into all spheres the human life. A student makes considerable use of prompt-based A.I. to contextualize academic concepts. Professionals operating in the software domain use it for pre-emptive assistance in their coding quotas. Additionally, the intelligence's use in the robotics domain is also insurmountable. The subject matter of trade is perhaps the most suited for integration of the intelligence. Organizations participating in trade are considering the enabling factors of using A.I. into trade-related activities. This would ensure efficiency, effectivity, and profitability for traders alike.

Thus, there is a very good case for the integration of Artificial Intelligence into the trade laws of the digital era. This would pose to particularly beneficial to the part-takers of trade. In the recent past, we have witnessed the era of computerization and how some have reaped the benefits put of it, a similar case can be drawn for artificial intelligence as well.

Challenges of Digital Trade:

  1. Data Protection & Privacy: With the rise of digitization, also came the rise of misappropriation of data. In fact, it can be argued that digitization armed companies enabled to commit data related crimes. Simply put, this is seen as the dark side of digitization and there have been multiple instances of data misappropriation. What acted as the last straw is the cross-border transfer of data. This was viewed as an imminent threat to national security. This led nations such as India to localize their data stems. The said localization would act as an ensuring factor for data-security, national security and work as an encouraging factor for domestic innovation.
  2. Intellectual Property Rights: There is an inevitable shift in the understanding of ownership in the digitized era. This can be attributed to the significant rise in the exchange of intangible goods. There is a paradigm-defining difference in the ownership nitty-gritties of these intangibles when compared to their tangible counterparts. Further, the trade of the intangibles brings forth new challenges on board. These may range from online piracy, counterfeiting, theft of goodwill, etc. But, using Artificial Intelligence powered detection methods can have an instrumental effect in the identification as well as combating such IPR infringements.
  3. Jurisdiction & Enforcement: An inherent shortcoming of the digital space is its borderless nature. Another issue that is attributed to the intangibility of the digital space, they are not bound by physical, demographical, or geographical boarders. This raises complexities when questions arise as to their Jurisdiction. This problem is especially rampant in cases of cross border digital trade disputes. This is another such case-scenario where the application of A.I.- powered legal research tools can be of assistance by the efficient analysis and evaluation of precedents and frameworks (international).
  4. E-commerce Regulations: The sensitive issues like that consumer protection, taxation, and liability in said international e-commerce transactions need to be clarified. There ideally needs to be mutual consensus amongst the trade partaking countries in form of a treaty/agreement. Particularly, Indian consumers and traders face challenges of a unique nature as there are limited to no redressal mechanisms for their disputes with foreign sellers. A.I. implementation can sole this as A.I. powered chat boxes can be sued as an efficient and medium for a detailed consumer grievance redressal system.
  5. Need for Adaptability: One of the most restricting attributes of trade is its static nature. This however has no space for the already rapidly digital space. Hence, reliance on A.I. and machine learning would work to eliminate the static and would instil adaptability in digital trade. This is guaranteed as A.I.'s adaptability has already been tried and tested for, in a multitude of disciplines.

Advantages of A.I. Applications in Trade Law:

  1. Enhanced Efficiency: Regular, routine, and time-consuming can be easily automated by true A.I. implementation into said system structures. This would ideally entail the likes of light legal research; legal contract analysis and other menial tasks tat unnecessarily takes up human time and resource. This would then free up the said human resources for their allocation to more complex legal analyses and decision-makings.
  2. Improved Accuracy: Perhaps one of the strongest arguments in favour of A.I. application is its elimination of human error. This is equally relevant in the case-matter of digital trade law as well. Particularly so by completely bypassing the constraints of human error in the legal research and contractual analysis. This would inevitably lead to increased notions of accuracy and consistency in achieving apt legal outcomes.
  3. Cost Savings: Despite the common notion about the expensive nature of A.I., one must keep in mind the nascency associated nature of A.I. in its current form. On a contrary note, A.I. and its streamlined legal process demand would undoubtedly lead to cost savings for businesses and governments in the long run. This would be so as there would be a steady decline on the reliance of human labour. However, it is apparent that A.I. would require a steep one-time investment for its optimum application by optimization into specific organizational structures.
  4. Proactive Enforcement: The mindful implementation of A.I. would invariably result in enabling the proactive enforcement of trade laws. This because of the undeniable fact that A.I. and its implementation ensures the elimination of all the negative factors associated with excess human involvement. Hence, the said mindful implementation would minimize violations in the digital trade space while also securing and protecting the national interests of specific countries.
  5. Data-Driven Policymaking: A.I.'s ability to analyse as well as evaluate trade data contributes to its significance and advantageous application in the digital trade laws. The intelligence's most contributing factor being the valuable inputs and insights provided for the user of the said intelligence. This has varied applications, ranging from trade policy formulation, and the fostering of economic growth and competitiveness.

Disadvantages of A.I. Application in Trade Law:
  1. Transparency and Explainability: Considering the legal purview, transparency in A.I. decision making is crucial. Hence, developing "explainable A.I." systems that can clearly communicate their reasoning processes is essential for building trust and ensuring fairness. This also further play into the credibility of the intelligence and would heighten its user-satisfaction due to its clear and transparent nature.
  2. Bias and Fairness: A.I. systems trained on biased data sets can perpetuate these biases in their outputs. Consequently, mitigating these biases in A.I. applications for trade laws would require careful selection of training data and ongoing monitoring for potential biases. 13 This is particularly important for India, where ensuring fair trade practises for both domestic and international businesses is of crucial importance.
  3. Accountability: The successful implementation of A.I. would place heavy reliance on the intelligence. However, assigning responsibilities in cases where A.I. makes errors or biased decisions leads to legal as well as ethical complexities. Therefore, it becomes extremely pertinent to develop and present clear legal frameworks to navigate the complex issue of assigning accountably post A.I. implementation.
Real-World Application:
  1. A.I. in Patent Analysis: USA Patent Trademark Office - It is confidence-generating that the Unites States Patent and Trademark Office (USPTO) has been able to successfully implement A.I. for the cause of patent analysis. This shows the feasibility and the reality of A.I. and its implementation into the real-world. Furthermore, there has been the observation of significant and efficient gains in processing patent applications. This would indicate the actual benefit of such wide-scale implementation of A.I., and further leads to sanguinity in its use-case scenarios.

    Additionally, the credibility factor must also be taken into consideration; a national office in the USA implementing A.I. would without a doubt have incurred stringent testing procedures. This further makes the case for viability of A.I. implementations in similar national level offices. An inference from this can be made and applied to India considering the long overdue need to streamlining the patenting process and foster innovations in India.
  2. A.I. in Customs Enforcement: Example of the European Union - Another sigh of relief for the A.I. optimists are the utilization of A.I. by the European customs authorities. This further builds on the previously established credibility initiated by the US Patent Office. In this case, the Customs Enforcement bodies in different European nations are using the likes of generative A.I. ascertain possible customs violations. Reports show that this method has been successful in deterring smuggling and other illegal activities while also facilitating legitimate trade. Taking into considerations India's aggregated immigration problem and drug-import related issues in the country, possibilities open up for applying a similar model on its borders to mitigate the same.
  3. A.I. in Trade Negotiations: Example of Singapore - The Government Administration of Singapore is displaying increased levels of commitment to A.I. implementation. The most significant measures taken in this direction by the administration is significant financial investment (362 million + 180 million USD), plans for an A.I. Marketplace, National A.I. Offices and Model A.I. Governance Framework. This solidifies the claims of the wide applications and implementations of A.I., however, the findings of this are yet expected as these initiatives are new. Regardless, India can analyse the successes and the failures of Singapore's applications of A.I. to better execute their own iteration.
The Way Forward:
  1. Balancing Benefits and Challenges: Considering the aforementioned advantages and disadvantages of A.I. integration in digital trade laws; it is of utmost importance to use a balanced approach that embraces the benefits of A.I. integration while addressing the challenges as well.
  2. Stakeholder Collaboration & International Harmonization: It is further important for the collaboration among governments, businesses, civil societies, and academia to develop ethical and responsible frameworks for A.I. integration into trade law. Additionally, implementations of this A.I. integration scheme must advocate for international co-operation and harmonization of trade laws to ensure a level playing field in a highly globalized world.
In conclusion, as we traverse the dynamic landscape of trade laws in the digital era, it becomes evident that embracing the integration of Artificial Intelligence (AI) is not just advantageous but imperative. The challenges range from data protection concerns to jurisdictional complexities in the digital realm, emphasize the need for adaptive regulations.

Despite the challenges, the immense potential of AI integration remains a compelling reason for organizations to embrace this trans-formative technology. From enhancing efficiency and accuracy to fostering cost saving strategies, AI emerges as a formidable force in shaping the future of trade law.

Looking ahead, it's crucial to adopt a balanced approach that prioritizes economic growth while safeguarding data privacy and ethical considerations. Collaboration among governments, businesses, civil societies, and academia is vital in developing responsible AI frameworks. Moreover, international cooperation and organization of trade laws are essential to ensuring a level playing field in our increasingly globalized world.

In this era of rapid technological advancement, proactive engagement with AI in trade law is not merely a question of possibility but inevitability. Those who embrace this trans-formative technology today will be the architects of tomorrow's digital trade landscape. The time for action is now, and by harnessing the potential of AI, India and the global community can navigate the complexities of the digital age and unlock unprecedented opportunities for growth and prosperity.

  1. Pti. (2019, April 9). US criticises India's data localisation norms, draft e-commerce policy. The Economic Times. indias-data-localisation-norms-draft-e-commerce-policy/articleshow/68794927.cms?from=mdr
  2. OECD Global Forum on Trade. (2023). Key issues in digital trade. issues-in-digital-trade.pdf
  3. Digital trade is growing faster than non-digital trade. (OECD). oecd.og. key-issues-in-digital-trade.pdf
  4. Fu, X., & Ghauri, P. N. (2020). Trade in intangibles and the global trade imbalance. The World Economy, 44(5), 1448-1469.
  5. Foresight, T. (2023, March 29). AI-Powered digital trade: What to expect in 2025 and beyond.
  6. Geist, M. A. (2021, February 1). AI and International Regulation.
  7. Davos 2023: The state of global trade and investment. (2023, January 23). World Economic Forum.
  8. Shaffer, G. (2021). Trade Law in a Data-Driven Economy: The need for modesty and resilience. World Trade Review, 20(3), 259-281.
  9. Webinar on artificial intelligence and consumer protection: Risks for consumers. (n.d.). UNCTAD.
  10. Artificial intelligence. (2024, February 6). USPTO. intelligence
  11. Parliament adopts its position on major reform of EU Customs Code | News | European Parliament. (n.d.). position-on-the-major-reform-of-eu-customs code
  12. Singapore Artificial Intelligence. (2023, June 11). International Trade Administration |

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