Writer: Dedi Supriadi, S.H.,M.M (International Trade Lawyer)
The Crisis of Traditional Trade Remedies in the Algorithmic Economy
The architecture of international trade law, primarily established during the Uruguay Round of negotiations, was designed to address the movement of tangible goods produced through traditional industrial processes. At the heart of this system lies the Anti-Dumping Agreement (ADA), a framework intended to ensure a level playing field by penalizing price discrimination that causes material injury to domestic industries. However, the rapid acceleration of the global technology exchange has introduced a paradigm shift that threatens the foundational assumptions of these rules. In the contemporary global economy, value is increasingly driven by intangible assets, data flows, and, most crucially, the algorithms that govern pricing and distribution.
The emergence of “algorithmic competition” or “robo-selling” has fundamentally complicated the comparison between export prices and normal value. Firms now utilize mass data collection, supercharged connectivity, and automated processing to adjust prices in real-time, often tailored to individual consumer behavior or fluctuating market conditions. This technological surge challenges the foundational definitions of the “ordinary course of trade” and the “comparable price” upon which anti-dumping investigations depend. As artificial intelligence becomes a central pillar of commerce, the legal community faces a “pacing problem,” where the law struggles to keep up with the exponential speed of development, leading to a disconnect between static trade rules and dynamic market realities.
The global trading system is currently navigating a “classification paradox,” where digital products may be treated as goods or services depending on their delivery method, yet their underlying value is derived from the same algorithmic logic. This report argues that the necessity of algorithmic transparency in the determination of normal value is no longer a peripheral concern but a central requirement for the survival of the rules-based trading system. Without access to the logic of pricing models, investigating authorities cannot ensure a fair comparison, thus undermining the integrity of the ADA.
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The Doctrinal Framework of Normal Value Determination under Article 2
The Selection of Comparison Methodologies
Under the WTO Anti-Dumping Agreement, the determination of dumping is essentially an exercise in price comparison. Article 2.1 establishes that a product is dumped if the export price is less than the comparable price, in the ordinary course of trade, for the like product when destined for consumption in the exporting country. This “normal value” is the benchmark for fairness. The Agreement provides a hierarchy of methods for identifying and measuring dumping, preferring the comparison of home-market sales to export sales. Consistent with Article 2.2, if home-market sales do not exist or are not usable, authorities may use either sales to a third country or a “constructed value” based on the cost of production in the country of origin plus a reasonable amount for administrative, selling, and general (SG&A) costs and profit.
| Methodology | Legal Basis | Application Conditions |
| Home Market Price | ADA Article 2.1 | Sales must be in the “ordinary course of trade” and of sufficient volume. |
| Third Country Price | ADA Article 2.2 | Used when domestic sales are absent or the market is in a “particular situation.” |
| Constructed Value (CV) | ADA Article 2.2 | Based on cost of production + SG&A + Profit; used when price data is unreliable. |
| Non-Market Economy (NME) | Accession Protocols | Uses surrogate costs from a third country (e.g., historical practice for China). |
The requirement for a “fair comparison” under Article 2.4 mandates that the normal value be adjusted for differences that affect price comparability, such as differences in quantities, physical characteristics, and circumstances of sale. In the digital era, these adjustments are increasingly difficult to quantify. For example, the physical differences between two software-embedded devices may be negligible, while their “intangible” value, dictated by software versions or algorithmic capabilities, may be vastly different.
The Ordinary Course of Trade and Below-Cost Sales
The concept of the “ordinary course of trade” (OCOT) is central to ensuring that the normal value is not distorted by unusual market conditions. Article 2.2.1 indicates that sales at prices below per-unit costs of production may be treated as not being in the ordinary course of trade and can be disregarded. This is particularly relevant in the technology sector, where “predatory pricing” or “input dumping” strategies often involve selling goods below cost to gain market dominance or data access. The investigation of such practices requires a granular analysis of the exporter’s cost structure.
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In cases involving “constructed value,” the calculation of profit and SG&A must be based on actual data pertaining to production and sales in the ordinary course of trade. The Appellate Body and various WTO panels have clarified that investigating authorities have discretion to choose among alternative calculation methods under Article 2.2.2, including the use of data from other producers or exporters in the same category. However, the surge in global technology exchange complicates this “averaging” process, as firm-specific algorithms create unique cost-revenue profiles that are not easily comparable across the industry.
Algorithmic Competition and the Disruption of Pricing Norms
The Rise of Robo-Selling and Dynamic Market Structures
Technological change has brought forward “algorithmic competition,” where firms draw on supercharged connectivity and mass data collection to engage in automated pricing. These algorithms do not follow pre-set rules; they react to changing market conditions in seconds, a feat that would take human retailers weeks or months to achieve. This shift has profound implications for anti-dumping law, as the “weighted average” of prices traditionally used in dumping margin calculations may no longer reflect a stable “normal value”.
The Cournot model of oligopoly predicts that as market concentration increases, firms price above the competitive level through their own action. In digital markets, algorithms can foster “tacit collusion” by implementing instantaneous price changes that signal future prices to competitors without any direct communication. This creates “pricing focal points” where market players converge, making it difficult for investigating authorities to determine if a domestic price is the result of fair competition or an algorithmic distortion.
| Feature | Traditional Pricing | Algorithmic Pricing |
| Update Frequency | Weeks to Months | Seconds to Minutes |
| Data Inputs | Historical Sales, Local Demand | Real-time Global Data, Consumer Behavior |
| Collusion Risk | Express (Direct Communication) | Tacit (Algorithmic Signaling) |
| Transparency | High (Public Price Lists) | Low (Black Box Logic) |
This “Digital Eye” requires a stricter stance against algorithmic tacit collusion, as it can inflict the same consumer harm as express cartels but is much harder to detect under current legal frameworks. In Indonesia, for instance, the rapid expansion of the digital trade ecosystem has made predatory pricing increasingly difficult to regulate due to algorithmic pricing and cross-subsidization between different segments of a platform’s business.
The Indonesia Case: Predatory Pricing and the Digital Market
The Indonesian experience highlights the gaps in legal standards for identifying below-cost pricing in digital markets. Enforcement is hindered by limited data access, dynamic promotional models, and cross-border platform operations. When a digital platform uses an algorithm to sell goods below cost to weaken competitors, traditional competition law benchmarks for “exclusionary intent” and “recoupment” become elusive. The Indonesian study suggests that a more adaptive regulatory structure is needed, incorporating algorithmic transparency and structural market effects to strengthen legal certainty.
Also read this page: Reconstructing Dumping Evidence in Cross-Border E-Commerce Transactions: Law Enforcement Challenges in the Era of Market Algorithms in Indonesia
The classification of dumping as a predatory pricing practice is accurate in terms of its impact, but the legal consequences differ significantly. Anti-dumping regulations remain the primary tool for addressing international price discrimination, but their effectiveness in the digital age depends on the ability of authorities to analyze the algorithms driving the price changes. This is especially critical for small and medium-sized enterprises (SMEs), which are most vulnerable to the exclusionary effects of algorithmic competition.
The Transparency Paradox: Trade Secrets vs. Regulatory Oversight
The “Black Box” Challenge in Investigation
A critical aspect of the current debate is the tension between the protection of trade secrets and the need for accountability in AI systems. Algorithmic pricing logic, training datasets, and source code are frequently protected as trade secrets under the TRIPS Agreement. This creates a “black box” environment where the internal workings of pricing models are hidden from external view, including from the investigating authorities responsible for determining normal value.
Expansive trade secret protection can impede the drive for transparency. While trade secrets are intended to prevent misappropriation by competitors, they are increasingly used as a shield against regulatory disclosure. This lack of scrutiny prevents authorities from assessing whether an algorithm is programmed to facilitate dumping or whether it is reacting to legitimate market signals. The legal framework for trade secrets currently lacks the defined boundaries and “public interest exceptions” found in other areas of intellectual property law, such as patents or copyrights.
The Role of Regional Trade Agreements (RTAs)
The legal landscape is further complicated by the inclusion of source code protection provisions in recent Free Trade Agreements (FTAs) and RTAs. Agreements like the USMCA and CPTPP prohibit governments from requiring access to or the transfer of source code as a condition for the import, distribution, or sale of software. These provisions establish “secrecy by default,” creating a new type of legal protection that prioritizes the confidentiality of algorithmic governance tools over the state’s ability to assess their compliance with the law.
| Protection Attribute | Intellectual Property Law (TRIPS) | RTA Source Code Provisions |
| Priority | Priority to the author/owner. | Priority to secrecy as a preventive measure. |
| Durability | Limited lifespan (e.g., patents). | Indefinite secrecy. |
| Enforcement | State vs. Private Actor. | State vs. State (prohibiting the lifting of the veil). |
| Transparency | Encouraged through disclosure. | Hampered through “Default Secrecy.” |
The “Code of Capital” theory suggests that by prohibiting states from requiring access to source code, these agreements protect private capital at the expense of regulatory autonomy. For anti-dumping authorities, this means that even if an algorithm is suspected of facilitating “unfair” pricing, the legal basis for demanding its disclosure may be barred by the very trade agreements designed to facilitate commerce.
Modernizing Constructed Value Determination
The Complexity of Cost Allocation in Technology Exchange
When home market prices are unavailable or unreliable, Article 2.2 requires the calculation of a “constructed value.” This formula is traditionally represented as:
CV = Cost of Production + (Selling, General & Administrative Expenses (SG&A)) + Profit
In the surge of global technology exchange, the “Cost of Production” for software-embedded products or digital services is often front-loaded in R&D and training data, while the marginal cost of producing an additional unit is near zero. Traditional accounting systems like the Integrated Financial Management System (IFMS) were designed for tangible inputs and struggle to accurately allocate these intangible costs.
Furthermore, the “particular market situation” (PMS) provision is increasingly invoked against “input dumping,” where the cost of an input is artificially lowered by state intervention or algorithmic optimization. To determine if a PMS exists, authorities need to know if the domestic market prices are appropriate for comparison. If the input pricing is governed by an opaque algorithm, the triggering condition for PMS becomes elusive, leading authorities to inflate dumping margins based on “surrogate costs”.
The Impact of Automated Accounting Systems
The use of automated accounting systems can facilitate the collection of data for trade investigations, but it also introduces new risks. Systems like CAS (Cost Allocation System) or FARS provide detailed status reports on appropriations and expenditures. However, if these systems lack adequate audit trails or if their data integrity is compromised, the “verification” stage of an anti-dumping investigation becomes unreliable.
Customs valuation, which serves as a baseline for anti-dumping duties, utilizes a hierarchy of six methods, starting with “transaction value”. When valuing intangible goods like software, the “transaction value” method faces challenges such as undervaluation fraud and the complexities of related-party transactions. These challenges are compounded when pricing is dynamic and determined by AI, making it difficult to establish a “stable” transaction value for duty calculation.
Technological Solutions for Data Integrity and Verification
Blockchain as an Immutable Audit Trail
To combat the risk of data manipulation and ensure the authenticity of declarations, blockchain technology has emerged as a promising tool. By providing a decentralized, immutable ledger, blockchain can verify the integrity of pricing data from the moment it leaves the exporter’s system to its submission to the investigating authority. In supply chain management, blockchain ensures traceability and provenance assurance, preventing the manipulation of records in sectors like pharmaceuticals or high-tech electronics.
For anti-dumping investigations, a blockchain-based forensic framework could allow authorities to:
- Verify the accuracy of per-unit costs and transaction prices through synchronized ledgers.
- Detect attempts to tamper with or falsify accounting data in real-time.
- Reduce the need for intrusive on-site verifications by providing a “trustless” source of data integrity.
| Feature | Traditional Audit | Blockchain-Driven Audit |
| Verification | Manual, Sample-based | Automated, 100% of Transactions |
| Tamper Resistance | Vulnerable to retroactive changes | Immutable Records |
| Resource Use | High (On-site visits, paper files) | Low (Database queries, real-time access) |
| Transparency | Limited by data access | Continuous and Unalterable |
This shift would allow customs to move from transaction-based checks to a more holistic, company-oriented compliance review, effectively managing the high transaction volumes common in digital trade.
AI-Driven Transaction Audits and Predictive Risk Management
Paradoxically, while AI creates the transparency challenge, it also provides the tools to solve it. Investigating authorities can use “AI-driven transaction-based audits” to compare purchase orders, invoices, and certificates to detect mismatches or irregularities. Machine learning algorithms can analyze vast datasets to identify non-compliance patterns and conduct predictive risk assessments.
However, the efficacy of these tools depends on “explainability.” If an authority uses a “black box” AI to catch an exporter using a “black box” pricing algorithm, the legal defense of the resulting audit findings becomes problematic. The “white box” approach—explainable AI (xAI)—is necessary to provide transparency to the audit process itself, ensuring that the results are justifiable in a WTO dispute settlement proceeding.
Rethinking the Role of the WTO and Global Governance
The “Pacing Problem” and Institutional Flexibility
The current WTO framework, while providing a rules-based order for trade stability, lacks the coherence and flexibility to address AI-induced labor disruption and pricing volatility. The “pacing problem”—the gap between technological advancement and legal response—is exacerbated by the WTO’s procedural rules, which allow a single member to block plurilateral progress. There is no global enforcement mechanism or agreed-upon definitions for high-risk AI systems in trade.
Recommendations for a modernized framework emphasize institutional flexibility and cross-sectoral dialogue. Instead of focusing on rigid, static rules, the WTO should become a platform for “softer, informal, and constructive dialogues”. This would allow the global trading system to be more deferential to local values and cultural contexts while still promoting interoperability between different regulatory regimes.
Free Trade Agreements (FTAs) as Laboratories of AI Governance
In the absence of a multilateral consensus, FTAs have become the “new laboratories” of AI governance. These agreements can embed interoperability and accountability into the fabric of trade through:
- Precise Definitions: Standardizing terms like “foundational model,” “training data,” and “algorithmic transparency”.
- Shared Audit Frameworks: Creating standing committees on “AI and Digital Trust” to conduct joint audits and ensure compliance with global norms.
- Transparency for Public-Impact Systems: Requiring cross-border access to explainability artifacts for high-risk systems, while still protecting proprietary source code for low-risk applications.
The Digital Economy Partnership Agreement (DEPA) offers a “modular” approach that focuses on digital inclusion and capacity building, particularly for developing countries that might otherwise be left behind in the “new North-South divide”.
Economic Implications: The Cost of Secrecy in Global Trade
The “overly broad” protection of trade secrets in AI technologies not only hinders transparency but also creates significant economic risks. When algorithms are protected from audit, they can reinforce systemic biases and inequalities in the market without any possibility of external correction. For businesses, the “patchwork compliance landscape” where a product is lawful in one jurisdiction but non-compliant in another discourages cross-border innovation.
For the consumer, anti-dumping duties themselves can lead to price increases and reduced choice, especially if domestic industries cannot meet demand. If these duties are calculated based on flawed “black box” methodologies, the resulting trade retaliation can trigger a downward spiral of economic volatility.
| Stakeholder | Risk of Opaque Algorithms | Benefit of Transparency |
| Exporters | Retaliatory duties based on “Facts Available.” | Fairer investigations and market access. |
| Domestic Industry | Undetected predatory pricing/input dumping. | Level playing field and protection from “unfair” trade. |
| Regulators | Inability to verify compliance with trade laws. | Efficient auditing and data-driven policy making. |
| Consumers | Higher prices due to trade disputes/reduced choice. | Sustainable competition and price stability. |
The objective of WTO negotiations should not be to weaken national anti-dumping laws but to improve them by curtailing abuses and ensuring that remedies target “unfair” practices that reflect actual market distortions.
Synthesis and Nuanced Conclusions
The surge of global technology exchange has fundamentally altered the landscape of international trade, rendering traditional price-comparison methodologies increasingly inadequate. The determination of “normal value” in an era of algorithmic pricing requires a new perspective that balances the imperative of trade liberalization with the necessity of regulatory transparency.
The findings of this research indicate that:
- Algorithmic Transparency is a Functional Necessity: Without the ability to scrutinize the logic of pricing models, the “fair comparison” requirement of the ADA is undermined. The “ordinary course of trade” cannot be verified in a “black box” environment.
- The “Secrecy by Default” Trend is Counterproductive: While source code protection in FTAs aims to foster innovation, its current application limits the state’s ability to enforce trade remedies and protect domestic markets from algorithmic distortions.
- Technological Interventions Offer a Path Forward: Blockchain and explainable AI (xAI) provide the technical means to ensure data integrity and facilitate audits without compromising the core of a firm’s intellectual property.
- Global Governance Must Become Adaptive: The WTO and RTA frameworks must evolve from rigid structures into flexible “laboratories” that prioritize interoperability, shared audit frameworks, and a commitment to digital inclusion.
Ultimately, the future of anti-dumping law amidst the surge of global technology exchange will depend on the ability of the international community to move beyond the “transparency paradox.” By incorporating public interest exceptions into trade secret protections and mandating algorithmic accountability for high-risk systems, policymakers can ensure that the digital trade frontier remains fair, predictable, and sustainable. The pursuit of a “level playing field” in the 21st century requires nothing less than a “glass box” approach to the algorithms that now define the value of global commerce.