Artificial Intelligence in Geographical Indications Protection: Opportunities and Challenges

Paper Code: AIJACLAV06RP2025

Category: Research Paper

Date of Publication: May 19, 2025

Citation: Ms. Archana Kavil, “Artificial Intelligence in Geographical Indications Protection: Opportunities and Challenges", 5, AIJACLA, 58, 58-65 (2025), <https://www.aequivic.in/post/artificial-intelligence-in-geographical-indications-protection-opportunities-and-challenges>

Author Details: Ms. Archana Kavil, Research Scholar, Gandhi Institute of Technology and Management

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Abstract

                Geographical Indications (GIs) are vital for protecting region-specific products, ensuring authenticity, and preserving traditional knowledge. However, GI enforcement faces significant challenges, including counterfeiting, lack of awareness, and administrative inefficiencies. The rise of global trade and digital commerce has exacerbated these issues, leading to economic losses for genuine producers. Artificial Intelligence (AI) offers transformative solutions for GI protection through automated counterfeit detection, blockchain integration, and market surveillance. AI-powered image recognition and machine learning algorithms can identify unauthorized GI-tagged products, while blockchain-based tracking enhances transparency. Furthermore, AI-driven big data analytics assist policymakers in monitoring market trends and unauthorized use of GIs. Despite these advancements, AI implementation in GI protection presents legal and ethical concerns, including jurisdictional complexities, data privacy risks, and accessibility barriers for small-scale producers. This paper explores the potential of AI in enhancing GI enforcement, examining its applications, challenges, and regulatory considerations. It also evaluates AI's impact on GI governance and its role in strengthening the enforcement of GI protection.

1.       Introduction

1.1.  Understanding GIs: GIs are a form of intellectual property (IP) that identify goods originating from a specific region, attributing qualities, reputation, or characteristics to their geographic origin. GIs are protected under the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), which mandates WTO members to establish legal frameworks for their enforcement (WTO, 1994). Various jurisdictions, including the European Union (Regulation (EU) No. 1151/2012) and India (Geographical Indications of Goods (Registration and Protection) Act, 1999), have specific statutes regulating GI registration and protection.

1.2.  Economic and Cultural Significance of GIs: GIs contribute significantly to rural development, preserving traditional knowledge, and preventing cultural misappropriation. Studies, such as those by Rangnekar (2004)[1] and Das (2010)[2], highlight the role of GIs in enhancing product differentiation and market value. For instance, the Darjeeling Tea case[3] underscores the importance of robust GI enforcement in preventing misuse by non-authentic producers.[4]

1.3.  Challenges in GI Protection: Despite international agreements and other enforcement mechanisms in place, GI enforcement faces challenges such as counterfeiting, lack of consumer awareness, and limited resources for surveillance. A 2022 report by the European Union Intellectual Property Office (EUIPO) highlights the increasing sophistication of counterfeiters, who are now utilizing advanced technologies like artificial intelligence to produce and distribute fake products, complicating enforcement efforts.[5] This underscores the need for innovative solutions, including AI-driven monitoring systems, to effectively combat GI infringements.

1.4.  Role of AI in Intellectual Property Protection: Artificial Intelligence is increasingly deployed in various domains of intellectual property protection. AI-driven tools can assist in trademark and patent examination, infringement detection, and digital rights management.[6] These advancements suggest that AI can play a pivotal role in overcoming GI enforcement challenges as well.

2.       AI Applications in Geographical Indications Protection

2.1.  AI-Powered Counterfeit Detection: Counterfeiting remains a significant challenge in GI enforcement. AI-based image recognition and machine learning algorithms are increasingly used to detect counterfeit products sold online.[7] Companies like Amazon and Alibaba employ AI to identify fake goods using deep learning techniques, enabling quicker and more efficient removal of infringing products.[8] AI tools analyze product packaging, labels, and branding elements to compare them against verified goods, ensuring authenticity.

One notable approach involves the use of Histogram of Oriented Gradients (HOG) for logo detection. This method focuses on analyzing the gradient orientations in localized portions of an image to detect specific patterns associated with authentic logos. By training Support Vector Machine (SVM) classifiers on these HOG features, the system can effectively distinguish between genuine and counterfeit logos, enhancing the accuracy of product authentication.[9]

Another technique employs Random Forest algorithms, which are ensembles of decision trees trained on various subsets of data. This method analyzes multiple features of a product, such as logo design, packaging details, and textual information, to assess its authenticity. The collective decision-making process of the Random Forest enhances the robustness and reliability of counterfeit detection systems.[10]

2.2.   Blockchain for GI Supply Chain Transparency: Blockchain technology, when integrated with AI, provides enhanced traceability of GI products from production to market. Blockchain-based smart contracts allow for automated verification and tracking of GI goods, reducing the risk of fraud. For example, in the wine industry, French GI-certified wines use blockchain systems to authenticate provenance, ensuring consumers receive genuine products.[11] Blockchain enhances this process by analyzing supply chain data and detecting anomalies that indicate potential counterfeiting.[12]

Blockchain provides a decentralized and immutable ledger that records every transaction and movement of a product through its supply chain. By embedding unique identifiers, such as QR codes or RFID tags, into products, the systems can track and verify the authenticity of goods in real-time. This combination ensures that any attempt to introduce counterfeit products into the supply chain is promptly detected and addressed.[13]

2.3.   AI-Driven Market Surveillance: AI-powered data analytics play a crucial role in monitoring online marketplaces for unauthorized GI usage. Web-scraping tools, combined with natural language processing (NLP), could track mentions of GI products across e-commerce platforms and social media.[14] This allows authorities to identify and act against misleading advertisements or illegitimate sellers. AI can significantly improve regulatory enforcement efforts by providing real-time monitoring and trend analysis in GI markets.

2.4.   Case Study: AI in GI Protection for Indian Handicrafts: India’s GI system protects various handicrafts, such as Pashmina shawls and Mysore silk. AI tools have been employed in collaboration with government agencies to identify counterfeit products in domestic and international markets. For instance, AI-driven textile analysis helps verify the authenticity of Pashmina wool by analyzing fiber patterns and compositions, reducing fraudulent claims.[15] These initiatives demonstrate AI’s effectiveness in safeguarding traditional craftsmanship and ensuring economic benefits for local artisans.

3.       Legal and Regulatory Barriers to AI Implementation in GI Protection

3.1.   Jurisdictional Complexities in AI-Governed GI Protection: The cross-border nature of GI infringement, particularly in digital commerce, raises jurisdictional challenges. Legal frameworks governing AI in GI enforcement differ across jurisdictions, creating inconsistencies in protection standards. The European Union’s regulatory approach emphasizes data transparency and AI accountability (EU AI Act, 2021), whereas developing nations face difficulties in implementing AI-based solutions due to limited technological infrastructure.[16]

3.2.   Privacy and Data Protection Concerns: AI-driven enforcement mechanisms relies heavily on data collection from e-commerce platforms, social media, and customs databases. However, data privacy regulations such as the General Data Protection Regulation (GDPR) impose restrictions on AI-driven surveillance. Studies suggest that balancing data privacy rights with AI deployment in business, focusing on the necessity of safeguarding personal data while fostering technological advancement is crucial.[17]

3.3.   Access to AI Technologies for Small-Scale Producers: A significant challenge in AI-driven enforcement mechanisms is accessibility for small-scale producers. Many traditional GI holders, particularly in developing regions, lack the resources to implement AI solutions.[18] Government intervention and public-private partnerships (PPPs) can help bridge this gap by providing funding and training for small producers to leverage AI technologies.

4.       Ethical Implications of AI in Geographical Indications Protection

4.1.   Bias and Discrimination in AI Enforcement: One of the major ethical concerns in AI-driven enforcement mechanisms is the risk of algorithmic bias. AI models trained on limited or biased datasets may disproportionately impact certain producers, particularly those from underrepresented regions. Studies highlight that AI-based enforcement tools can inadvertently favor well-documented data from economically advanced regions, while overlooking those from developing nations. To ensure fair AI implementation, continuous audits and diverse dataset training are essential.

In the context of GI protection, this bias can result in the preferential recognition and enforcement of well-established GIs, while traditional products from developing economies receive inadequate protection. For example, AI systems trained predominantly on European wine and cheese GIs may be more adept at identifying counterfeits of those products but may struggle to authenticate lesser-known handicrafts or agricultural products from South Asia or Africa. This imbalance could marginalize small-scale producers and indigenous communities, whose unique products rely heavily on robust GI enforcement for market recognition. To counteract this, AI models must be developed using diverse datasets that represent a wide range of GI products, ensuring that digital enforcement mechanisms benefit all regions equitably.

4.2.  Data Privacy and Surveillance Risks: The deployment of AI in enforcement mechanisms often involves extensive data collection from online marketplaces, supply chains, and consumer interactions. This raises concerns about compliance with privacy regulations such as the GDPR and other national data protection laws. Unauthorized surveillance, data breaches, or misuse of AI-generated insights can have serious implications for producers and consumers alike. Implementing robust data governance frameworks is crucial to mitigating these risks.[19]

In the context of GI protection, AI-driven monitoring systems often rely on tracking sales, production details, and even geographical markers to verify authenticity. However, collecting and processing such sensitive data—particularly from small-scale producers and indigenous communities—raises concerns about data ownership and consent. Many GI-registered products, such as traditional handicrafts or agricultural goods, originate from rural artisans who may lack awareness of how their data is being used. Without strict governance, AI-enabled surveillance could lead to the exploitation of proprietary knowledge, unauthorized data commercialization, or even price manipulation by larger entities with access to such datasets. To safeguard GI producers, AI-driven enforcement must align with ethical data collection practices, ensuring transparency, informed consent, and regulatory oversight to prevent misuse while still enabling effective counterfeit detection and market surveillance.

4.3.   Impact on Traditional Knowledge and Indigenous Rights: GIs often protect products linked to indigenous communities and traditional knowledge. AI-driven monitoring systems risk misrepresenting or commodifying such heritage without adequate community involvement. Ethical considerations necessitate inclusive policymaking, ensuring that AI frameworks respect cultural sensitivities and do not contribute to misappropriation.[20] Community-led AI solutions, where local producers retain decision-making power, can help align AI deployment with indigenous rights frameworks.

4.4.   Transparency and Accountability in AI Decision-Making: AI-driven enforcement mechanisms relies on complex machine learning models, often operating as "black-box" systems with limited interpretability.[21] This lack of transparency could raise accountability concerns, especially in legal disputes over counterfeit claims or false positives in infringement detection. Further, AI governance in IP enforcement should mandate explainability and human oversight in decision-making.[22] Transparent AI auditing mechanisms and regulatory oversight bodies can help address these challenges.

5.       Policy Recommendations for AI-Driven GI Protection

5.1.  Developing Standardized AI Regulations for GI Enforcement: Given the international nature of GIs, harmonized AI regulations are essential to ensuring consistent enforcement across jurisdictions. Policymakers should collaborate with international organizations like the WTO, WIPO, and national IP offices to establish standardized AI-based enforcement guidelines.

5.2.   Enhancing Accessibility to AI for Small-Scale Producers: To prevent AI-driven GI enforcement mechanisms from becoming a tool exclusive to developed countries, governments should facilitate AI accessibility for small-scale producers of developing countries. Public-private partnerships (PPPs), subsidies, and capacity-building programs can help local artisans and traditional producers leverage AI tools for authentication and market surveillance. Open-source AI solutions tailored to regional GI enforcement needs can also bridge the technological divide.

5.3.   Strengthening Blockchain Integration for GI Transparency: Combining AI with blockchain technology enhances GI authentication and supply chain transparency. Policymakers should support research and pilot programs integrating AI with blockchain for real-time GI tracking. Countries like France have already pioneered blockchain-based certification systems for wine and cheese GIs, demonstrating the feasibility of this approach.[23] Expanding such initiatives globally can enhance consumer trust in GI authenticity.

5.4.   Balancing AI Innovation with Ethical Safeguards: Governments must strike a balance between fostering AI innovation and enforcing ethical safeguards. Ethical AI certification programs, impact assessments, and accountability frameworks should be integrated into AI-GI governance. The adoption of human-in-the-loop AI models, where final enforcement decisions involve human oversight, can ensure fairness in GI protection.[24]

5.5.   Promoting Consumer Awareness and Participation: Empowering consumers with knowledge and tools is essential for the effective protection of Geographical Indications (GIs). Educating consumers about the significance of GIs and the distinguishing features of authentic products can enhance their ability to make informed purchasing decisions. Studies have shown that consumers with a higher awareness of GIs are more likely to value and seek out these products, associating them with quality and authenticity. To facilitate consumer participation in GI protection, the development of user-friendly mobile applications is recommended. These applications can utilize AI algorithms to enable consumers to verify the authenticity of GI products in real-time by scanning labels, logos, or QR codes. Such technology not only aids in the detection of counterfeit goods but also fosters a collaborative effort between producers and consumers in safeguarding the integrity of GI products. By integrating consumers into the enforcement process, the overall effectiveness of GI protection measures can be significantly enhanced.

6.       Comparative Analysis: AI in GI Protection Across Jurisdictions

6.1.  European Union: AI and Robust GI Protection Mechanisms: The EU maintains one of the most developed GI protection frameworks, integrating AI tools for counterfeit detection and regulatory compliance monitoring. The European Union Intellectual Property Office (EUIPO) employs AI-powered image recognition to detect counterfeit GI goods across online marketplaces.[25] Additionally, the EU AI Act sets clear guidelines on AI transparency, ensuring responsible deployment in IP enforcement.

6.2.  United States: AI-Enabled Trademark and GI Enforcement: While the US primarily protects GIs under trademark law, AI-driven enforcement mechanisms play a growing role in identifying GI infringements. The US Patent and Trademark Office (USPTO) has incorporated AI tools in trademark examination and market surveillance, with AI-driven image recognition detecting misleading product labels (USPTO, 2022). However, the US lacks a dedicated federal framework governing AI-GI application, leading to fragmented enforcement efforts.

6.3.   India: Challenges and Opportunities in AI-GI Integration: India, home to over 400 registered GIs, has begun exploring AI applications in GI authentication. Government initiatives, such as the use of AI-based textile analysis for Pashmina shawl authentication, highlight AI’s potential in protecting traditional handicrafts. However, limited technological infrastructure and accessibility barriers hinder broader AI adoption. Strengthening digital infrastructure and providing AI training to local producers can enhance India's AI-GI framework.

6.4.  China: AI-Driven GI Enforcement in a Digital Marketplace: China, a leading player in digital commerce, has invested in AI-powered enforcement mechanisms for GI protection. E-commerce giants like Alibaba employ AI to detect counterfeit products, using machine learning and big data analytics for real-time monitoring. However, concerns remain regarding enforcement consistency and transparency, as state-controlled AI surveillance raises data privacy and trade regulation concerns.

7.       Conclusion

AI presents a transformative opportunity to strengthen GI enforcement, enhancing counterfeit detection, market surveillance, and supply chain transparency. However, its deployment must be accompanied by robust ethical safeguards, policy interventions, and international regulatory cooperation. By addressing jurisdictional complexities, ensuring transparency, and making AI accessible to small-scale producers, policymakers can harness AI’s full potential in protecting geographical indications.

Moreover, with the widespread adoption of smartphones—now numbering over 3.5 billion globally[26] —consumers can play a more active role in verifying the authenticity of GI products. AI-driven mobile applications integrated with blockchain technology can enable end-users to authenticate products in real-time. By simply scanning product packaging, logos, certification marks, or unique QR codes, consumers can access verifiable data stored on a decentralized ledger. AI algorithms can analyze packaging features, text, fonts, and color patterns to differentiate between genuine and counterfeit goods, while blockchain ensures that product traceability remains tamper-proof.

This integration of AI and blockchain not only empowers consumers with greater confidence in their purchases but also strengthens the overall GI ecosystem by reducing fraud and ensuring fair market practices. Future research should explore advancements in user-friendly AI authentication tools, as well as global collaboration between technology developers, GI producers, and regulatory authorities. By fostering an inclusive and equitable AI-GI framework, the authenticity and cultural significance of geographically indicated products can be preserved for generations to come.

[1] Dwijen Rangnekar, The Socioeconomics of Geographical Indications: A Review of Empirical Evidence from Europe (UNCTAD-ICTSD Project on IPRs and Sustainable Development, 2004).

[2] Kasturi Das, ‘Prospects and Challenges of Geographical Indications in India’ (2010) 13 J World Intell Prop 148.

[3] Tea Board of India v. ITC Ltd., 2011 SCC Online Cal 322

[4] SC Srivastava, ‘Protecting the Geographical Indication for Darjeeling Tea’ in Peter Gallagher, Patrick Low and Andrew L Stoler (eds), Managing the Challenges of WTO Participation: 45 Case Studies (2005) Cambridge University Press.

[5] EUIPO, Impact of Artificial Intelligence on the Infringement and Enforcement of Copyright and Designs (European Union Intellectual Property Office, 2022)

[6] T Liu and Z Yu, ‘The Relationship Between Open Technological Innovation, Intellectual Property Rights Capabilities, Network Strategy, and AI Technology Under the Internet of Things’ (2022) 15(3–4) Oper Manag Res 793.

[7] A Alaei and M Delalandre, ‘A Complete Logo Detection/Recognition System for Document Images’ (2014) 11th IAPR International Workshop on Document Analysis Systems, 324.

[8] Daniel Chee King Chow, ‘Alibaba, Amazon, and Counterfeiting in the Age of the Internet’ (2020) 40 NW J Intl L & Bus 157; Ohio State Public Law Working Paper No 497 (2019)

[9] Richu Varghese, et al., ‘An AI-Based Fake Products Identification System’ (2022) Availabe at: https://www.researchgate.net/publication/366445003_An_AI-Based_Fake_Products_Identification_System 

[10] Ibid

[11] Y Kang, et.al., ‘Enhancing Traceability in Wine Supply Chains through Blockchain: A Stackelberg Game-Theoretical Analysis’ (2023) J Theor Appl Electron Commerce Res

[12] R Jadhav and others, ‘System for Identifying Fake Product Using Blockchain Technology’ (2022) 7th International Conference on Communication and Electronics Systems 851.

[13] S Kalpana Devi and others, ‘Fake Product Identification with the Help of Blockchain Technology’ (2021) Innovations in Power and Advanced Computing Technologies (i-PACT) (2021)

[14] Vijayaragavan Pichiyan, et. al, ‘Web Scraping Using Natural Language Processing: Exploiting Unstructured Text for Data Extraction and Analysis’ (2023) Procedia Computer Science.

[15] Muzafar Rasool Bhat and others, ‘Pashmina Authentication on Imagery Data Using Deep Learning’ (2024) 39 AI & Society 2297

[16] Moti Melkamu, ‘Artificial Intelligence Implementation Challenges in Industries: Developing Countries Prospective’ (2025) 2(1) J Trends Challenges Artif Intell 175

[17] Oscar James and Edward Lucas, Ethical AI: Balancing Innovation and Data Privacy in the Digital Business Landscape (2024) Available at:

https://www.researchgate.net/publication/384441999_Ethical_AI_Balancing_Innovation_and_Data_Privacy_in_the_Digital_Business_Landscape#fullTextFileContent 

[18] Adebayo Aderibigbe and others, ‘Artificial Intelligence in Developing Countries: Bridging the Gap Between Potential and Implementation’ (2023) 4 Comput Sci & IT Res J 185

[19] Adrienn Lukács and Szilvia Váradi, ‘GDPR-Compliant AI-Based Automated Decision-Making in the World of Work’ (2023) 50 Comput Law & Security Rev 105848

[20] European Parliamentary Research Service, The Impact of Artificial Intelligence on Learning, Teaching, and Education (European Parliament, 2020)

[21] P Linardatos, et.al, ‘Explainable AI: A Review of Machine Learning Interpretability Methods’ (2020) 23(1) Entropy (Basel) 18

[22] Luca Nannini et.al, ‘Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK’ (2023) ACM Digital Library

[23] Gloria Luzzani and others, ‘Blockchain Technology in Wine Chain for Collecting and Addressing Sustainable Performance: An Exploratory Study’ (2021) Sustainability

[24] U Agudo, et al., ‘The Impact of AI Errors in a Human-in-the-Loop Process’ (2024) 9(1) Cogn Res Princ Implic

[25] EUIPO, Strategic Plan 2025 (European Union Intellectual Property Office, 2025) Available at:

https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/contentPdfs/about_euipo/strategic_plan/SP2025_en.pdf 

[26] https://www.statista.com/forecasts/1143723/smartphone-users-in-the-world