A Blockchain-Enabled Approach to Cross-Border Compliance and Trust

Vikram Kulothungan

REVIEW 4 major objections 6 minor 45 references

A Blockchain-Enabled Approach to Cross-Border Compliance and Trust

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A blockchain-based framework could make cross-border AI compliance automatic and verifiable.

desk verdict A coherent but unvalidated blockchain-AI governance roadmap for finance; the core compliance guarantee is undercut by an unaddressed oracle-trust gap. read the letter →

arxiv 2501.09182 v1 pith:ZLQZBTIN submitted 2025-01-15 cs.AI cs.CRcs.CYcs.SE

Vikram Kulothungan This is my paper · ORCID

classification cs.AIcs.CRcs.CYcs.SE

keywords AIgovernanceblockchaindistributedledgertechnologysmartcontractsdecentralizedidentitycross-bordercomplianceEUActDelegatedProof-of-Stake

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that fragmented, region-specific AI regulation can be replaced by a single blockchain-based governance layer. It proposes a decentralized framework that combines delegated proof-of-stake consensus, smart-contract compliance, decentralized identity for AI systems, cross-border auditing, and tokenized rewards to enforce rules like the EU AI Act automatically. The intended payoff is that high-risk AI systems, especially in finance, become continuously auditable and verifiable across jurisdictions instead of relying on voluntary or locally enforced compliance. The paper supports this with a phased ten-year deployment plan and a component-by-component mapping to existing regulatory requirements.

What carries the argument

The central object is the Decentralized AI Governance Framework, a layered architecture rather than a single formula. It carries the argument by mapping each governance function to a blockchain component: DPoS for multi-stakeholder rule-setting, smart contracts for automated and continuous compliance verification, DIDs for tamper-proof identity and audit trails, and tokenized incentives to reward compliant behavior. The risk-based classification of AI systems is what triggers different levels of monitoring and audit frequency throughout the framework.

What would settle it

A pilot test in which a regulatory change (for example, an amendment to the EU AI Act) is run through the paper's proposed NLP-to-smart-contract pipeline and the resulting contract either rejects a compliant AI system or certifies a non-compliant one, based on independent legal review, would show the automated-compliance claim fails.

Watch

Extended reading notes

Core claim

The paper proposes a decentralized AI governance framework built on a permissioned blockchain with a Delegated Proof-of-Stake consensus layer, smart-contract-based compliance automation, decentralized identifiers for AI systems, a decentralized cross-border auditing network, and a tokenized incentive system. The framework classifies AI systems by risk and applies governance rules dynamically, with the EU AI Act used as the reference regulation. The central claim is that this combination creates a unified, transparent, and adaptable system that can enforce security, privacy, and trust standards for high-risk AI across jurisdictions, particularly in finance.

Load-bearing premise

The framework assumes that legal and regulatory requirements, such as those in the EU AI Act and Basel III, can be faithfully translated into machine-readable smart-contract code and that these encodings remain correct across jurisdictions and regulatory updates.

Editorial extensions

If this is right

Auditing of high-risk AI systems could shift from periodic, manual reviews to continuous, automated checks recorded on an immutable ledger.
Regulators in different jurisdictions could share the same compliance evidence, reducing duplicate reporting by global financial institutions.
Tokenized rewards would create economic pressure for firms to keep AI systems in compliance, even when no single authority is watching.
The framework's phased rollout implies that early adoption in EU and US financial pilots would shape the global standards before G20 expansion.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the mechanism is sound, the same layered design could be transferred to other heavily regulated sectors, such as healthcare diagnostics or energy grid management, where risk classification and audit trails already exist.
The natural spot to falsify the design early is the legal-to-code translation step; a small experiment translating GDPR clauses into executable rules would be a fast, cheap test.
A permissioned chain with regulators as validating nodes might be a more politically acceptable variant than a fully public token-based network, a modification the paper leaves implicit.
The token incentive's value depends on the compliance ecosystem's credibility, so a governance failure in early pilots could undermine the token's utility and the entire reward loop.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You asked what I think of the blockchain AI governance paper from Vikram Kulothungan. Here's the short version: it's a cleanly written concept paper that assembles existing pieces—DPoS, smart contracts, DIDs, token incentives—into a finance-specific governance architecture. What's actually new is the integrated package and the explicit mapping to the EU AI Act. That's a useful synthesis for a governance or regtech audience, and the paper is honest about some real pitfalls (voting caps, anti-collusion, GDPR vs. transparency). It does not, however, deliver what the abstract claims. The phrase 'ensures security, privacy, and trustworthiness' is simply not backed by any derivation, simulation, pilot, or baseline comparison. This is a roadmap, and the paper repeatedly asserts that smart contracts 'automatically enforce compliance' without showing how that enforcement survives contact with real-world data. The stress-test note about oracles is on target. The framework relies on off-chain data feeds—market conditions, regulatory updates, audit evidence—bridged by oracles, but the paper mentions oracles only in passing and never specifies an integrity mechanism. A stale, biased, or compromised oracle would let the smart contract certify a non-compliant AI as compliant, even if every legal rule were perfectly encoded. That gap is load-bearing, not a minor technicality. The same goes for the NLP-to-smart-contract translation step: encoding Basel III or the EU AI Act into machine-readable code and keeping it current across jurisdictions is hand-waved. These soft spots don't make the framework worthless, but they do mean the advertised guarantee is unearned. On the citation pattern: the references are relevant and mostly appropriate, though a few feel tangential. The paper is internally consistent and the author clearly understands the component technologies. That earns a 'serious thinker' from me. But as a research contribution, it needs major revision: reframe the claims as a proposal, add a concrete validation and adversarial analysis plan, and address the oracle and legal-encoding problems head-on. Would I send it to peer review? Yes, with the expectation of heavy revision—it deserves referee time precisely because it's a plausible, potentially influential design that currently overstates itself. A good referee could push it into something genuinely useful. I'd bring it to a reading group focused on AI governance or blockchain regulation, but not to a technical security group.

Referee Report

4 major / 6 minor

Summary. The paper proposes a blockchain-based decentralized AI governance framework aimed at cross-border compliance and trust, particularly in the financial sector. The framework integrates a Delegated Proof-of-Stake consensus mechanism, smart contract-based compliance automation, decentralized identities for AI systems, a decentralized auditing network, a tokenized incentive structure, an interoperability layer, stakeholder engagement, risk management, and education/training components. The paper claims that this framework ensures security, privacy, and trustworthiness of AI systems across borders, aligns with the EU AI Act, and provides a phased deployment timeline from 2024 onward. No formal analysis, simulation, pilot data, or comparison with existing approaches is provided; the contribution is an architectural proposal and roadmap.

Significance. If the framework's assurance properties were rigorously established, the paper would offer a valuable direction for automated, cross-jurisdictional AI governance. The proposal synthesizes several credible components (smart contracts, DIDs, DPoS, token incentives) and maps them to EU AI Act requirements, which may serve as a useful checklist for future system designs. However, the paper does not ship machine-checked proofs, reproducible code, or falsifiable predictions; its central contribution is a high-level architecture. The main value is as a conceptual roadmap, but the load-bearing claims of guaranteed security, privacy, and trustworthiness are not demonstrated. The unaddressed oracle trust problem, in particular, leaves the automated compliance mechanism without a sound trust model.

major comments (4)

[Abstract and Section I] The central claim that the framework 'ensures security, privacy, and trustworthiness' is asserted without any formal verification, simulation, pilot data, or comparison baseline. The paper provides no evidence that the proposed mechanisms guarantee these properties, and no threat model is defined. This is load-bearing because the entire contribution rests on these assurance claims.
[Section IV.B.1] Smart contract compliance automation depends on real-time data feeds bridged by oracles, but the paper does not specify how oracle integrity is established. A stale or malicious oracle can feed false regulatory or market data, causing the contract to certify non-compliant AI as compliant. The statement that data feeds are 'securely integrated into the blockchain' is asserted without a mechanism such as decentralized oracle networks, multi-sourcing, cryptographic attestations, or economic penalties. This gap breaks the claimed compliance guarantee even under perfect encoding of legal rules.
[Section IV.B.1 and IV.D.1] The framework assumes that legal and regulatory requirements (e.g., EU AI Act, Basel III) can be faithfully translated into machine-readable smart contract code via NLP, and that 'standardized audit protocols will be developed and codified.' The paper does not address how ambiguity, conflicting jurisdictions, or regulatory drift over time are handled, nor does it provide a correctness argument or a governance mechanism to update encoded rules without introducing new errors. This is a load-bearing unaddressed risk for the automated-compliance claim.
[Section IV.B (overall)] The claim that 'smart contracts automatically enforce compliance' is presented as a design feature, but no execution semantics, formal specification, or evaluation of contract correctness is given. Formal verification is mentioned as a property ('formal verification, including model checking and theorem proving'), but no verification results or even a specification of the verified properties are reported. Without such evidence, the assertion of automatic enforcement remains an unsupported claim.

minor comments (6)

[Abstract] The phrase 'more urgent that ever' should be 'more urgent than ever.'
[Section III heading] The heading 'DESIGN PRINCICPLES AND METHODOLOGY' contains a typo; 'PRINCICPLES' should be 'PRINCIPLES.'
[Section VI.3] The heading 'Accountatbility in a Decentralized System' contains a typo; 'Accountatbility' should be 'Accountability.'
[References] Reference [15] lists '2017 IEEE International Conference on Software Architecture (ICSA)' but the year at the end is given as 2021; the year should be corrected to 2017.
[Section IV.C.1] The DID registration is described as using a permissioned blockchain with Proof of Authority, while the broader framework uses DPoS; the relationship between these two consensus mechanisms should be clarified.
[Section II] The related-work discussion is brief and does not position the proposal against existing blockchain-based governance or regulatory technology frameworks; a deeper comparison would strengthen the novelty claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a design proposal with asserted components and no derivation chain that reduces to its inputs.

full rationale

The paper proposes a decentralized AI governance framework and describes components (DPoS consensus, smart-contract compliance, decentralized identity, cross-border auditing, token incentives, interoperability) without deriving any result from fitted parameters or equations. There are no formulas, no statistical fits, and no quantity is defined in terms of another quantity that it is supposed to predict. The claimed benefits, such as 'ensures security, privacy, and trustworthiness,' are asserted design goals rather than conclusions derived from the framework's specifications. The EU AI Act alignment table maps framework components to regulatory requirements, but that mapping is a stated correspondence, not a circular inference. References are to external prior work on blockchain, smart contracts, and AI governance; none is a self-citation by the author, and none is invoked as an unverified uniqueness theorem that forces the framework's choices. The main legitimate concerns—such as oracle integrity and the feasibility of encoding legal rules into smart contracts—are soundness or implementation risks, not circularity. Because the paper does not perform a derivation or fit, the circularity burden is essentially zero.

Assumptions & free parameters 0 free parameters · 5 assumptions · 2 invented entities

The framework relies on several unvalidated domain assumptions about translating law into code, DPoS scalability, and token behavior. The two invented entities (token and AI DIDs) have no external evidence. No free numerical parameters are fitted.

assumptions (5)

domain assumption Blockchain immutability and transparency directly enable trustworthy governance.
Invoked in Section I as the basis for the entire framework; not empirically validated in the paper.
domain assumption Legal and regulatory rules can be translated into machine-readable smart-contract code that remains correct across jurisdictions.
Assumed in Section IV.B.1 (NLP translation) and Section IV.D.1 (standardized audit protocols).
domain assumption Delegated Proof-of-Stake provides secure, scalable governance for the financial sector.
Assumed in Sections III.B and IV.A; the paper cites generic consensus reviews but does not validate DPoS for this use case.
domain assumption Tokenized incentives create a self-sustaining compliance ecosystem without destabilizing manipulation.
Assumed in Section IV.E; the paper discusses anti-collusion measures but provides no economic modeling.
domain assumption Zero-knowledge proofs enable compliance verification without compromising data privacy in practice.
Assumed in Section IV.D.1; no performance or feasibility analysis is provided.

invented entities (2)

Native compliance token
purpose: Reward compliant AI systems, enable staking and governance influence (Section IV.E).
The token's utility and value are defined wholly within the proposed framework; no implementation, issuance, or market exists.
Decentralized identity (DID) for AI systems
purpose: Provide tamper-proof identity and traceability for AI systems (Section IV.C).
The paper proposes assigning DIDs to AI systems and registering them on a permissioned blockchain; no standard or implementation exists.

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Cite this review

Pith. "Pith review of A Blockchain-Enabled Approach to Cross-Border Compliance and Trust." pith.science (2026). https://pith.science/paper/ZLQZBTIN

@misc{pith2026250109182,
  author       = {Pith},
  title        = {Pith review of: A Blockchain-Enabled Approach to Cross-Border Compliance and Trust},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZLQZBTIN}},
  note         = {Machine review of arXiv:2501.09182}
}

read the original abstract

As artificial intelligence (AI) systems become increasingly integral to critical infrastructure and global operations, the need for a unified, trustworthy governance framework is more urgent that ever. This paper proposes a novel approach to AI governance, utilizing blockchain and distributed ledger technologies (DLT) to establish a decentralized, globally recognized framework that ensures security, privacy, and trustworthiness of AI systems across borders. The paper presents specific implementation scenarios within the financial sector, outlines a phased deployment timeline over the next decade, and addresses potential challenges with solutions grounded in current research. By synthesizing advancements in blockchain, AI ethics, and cybersecurity, this paper offers a comprehensive roadmap for a decentralized AI governance framework capable of adapting to the complex and evolving landscape of global AI regulation.

Figures

Figures reproduced from arXiv: 2501.09182 by the authors.

**Figure 1.** Proposed Decentralized AI Governance Framework This framework ensures that AI systems are governed according to their risk levels, addressing the complexities of global financial regulation by integrating key components that provide security, compliance, and trust across international markets. A. Global Consensus Mechanism This paper proposes a tailored Delegated Proof-of-Stake (DPoS) consensus mechanism designed sp… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 43 canonical work pages

[1]

Implementation: The implementation involves active participation from regulators, financial institutions, and AI developers. Given the sector's high stakes, the DPoS mechanism integrates multiple layers of security and accountability: • Stakeholder Engagement and Voting Power: Stakeholders elect delegates to make governance decisions, with voting power ty...

work page
[2]

For example, AI-driven credit scoring models, deemed high-risk, would be continuously monitored, with governance standards dynamically adjusted ba sed on real -time data

Financial Sector Example: In practice, this DPoS mechanism enables collaboration between banks, fintechs, and regulators to classify AI systems by risk. For example, AI-driven credit scoring models, deemed high-risk, would be continuously monitored, with governance standards dynamically adjusted ba sed on real -time data. This classification triggers stri...

work page
[3]

Key components include: • Modular Smart Contract Design: Smart contracts, developed using languages like Solidity for Ethereum-based systems [8], feature a modular architecture

Implementation: Implementing smart contracts involves integrating blockchain and AI to create a dynamic, responsive compliance system. Key components include: • Modular Smart Contract Design: Smart contracts, developed using languages like Solidity for Ethereum-based systems [8], feature a modular architecture. Each compliance component (e.g., data privac...

work page
[4]

Financial Sector Example: In practice, smart contracts can enforce compliance with regulations like Basel III, automatically verifying loan agreements and adjusting terms based on real-time financial data. In derivatives trading, they can execute trades only if they meet compliance criteria, reducing the risk of error, fraud, and non- compliance, while im...

work page
[5]

Implementation: The implementation of the DID system integrates advanced technologies to ensure security, privacy, and scalability [10]. Key steps include: • Creation of Unique DIDs: Each AI system is assigned a unique DID using cryptographic algorithms like elliptic curve cryptography (ECC) or RSA, ensuring security and preventing duplication. DIDs are f...

work page
[6]

If compliance changes due to a regulatory update, smart contracts automatically update the DID and trigger necessary audits, ensuring continuous oversight

Financial Sector Example: In the financial sector, high- risk AI models, such as trading algorithms, will receive blockchain-based DIDs that include risk classification and compliance status. If compliance changes due to a regulatory update, smart contracts automatically update the DID and trigger necessary audits, ensuring continuous oversight. D. Cross-...

work page
[7]

Key components include: • Rigorous Accreditation Process for Auditors: The success of the auditing network relies on a robust accreditation process

Implementation: The implementation of a decentralized AI auditing network is a multi -layered process that requires the integration of advanced technologies and rigorous governance protocols. Key components include: • Rigorous Accreditation Process for Auditors: The success of the auditing network relies on a robust accreditation process. Auditors undergo...

work page
[8]

For example, a global AI -driven credit scoring system is periodically audited for compliance with local and international regulations

Financial Sector Example: In the financial sector, AI models in high -risk applications, such as credit scoring and algorithmic trading, undergo regular audits by accredited auditors. For example, a global AI -driven credit scoring system is periodically audited for compliance with local and international regulations. Audit findings can be recorded on the...

work page

Show all 45 references

[9]

A finite supply will prevent inflation, with allocations for rewards, governance, and development

Implementation: The implementation of the tokenized incentive structure includes the following key components: • Creation of the Native Token: The native token, developed using blockchain technology, will be secure, transparent, and transferable. A finite supply will prevent i...
[10]

For instance, a Basel III -compliant credit scoring system may receive tokens, offering benefits like reduced fees or access to premium services

Financial Sector Example: In the financial sector, high- risk AI systems, such as those in credit scoring or algorithmic trading, that comply with regulatory standards will earn tokens as rewards. For instance, a Basel III -compliant credit scoring system may receive tokens, o...
[11]

Implementation: The implementation of the interoperability layer involves the development of a comprehensive architecture that facilitates the integration of high-risk AI systems with existing financial systems and multiple blockchain platforms. Key components include: • Devel...
[12]

Standardized APIs and blockchain connectors ensure compliance with data protection and facilitate cross -border transactions

Financial Sector Example: In the financial sector, the interoperability layer enables high- risk AI systems, such as trading algorithms or credit scoring systems, to securely interact with exchanges, banks, and regulatory platforms. Standardized APIs and blockchain connectors ...
[13]

Key components include: • Stakeholder Roles and Responsibilities: The framework will define the roles of regulators, banks, fintechs, AI developers, and industry experts

Implementation: The stakeholder engagement mechanism will ensure all relevant voices are included in the governance process. Key components include: • Stakeholder Roles and Responsibilities: The framework will define the roles of regulators, banks, fintechs, AI developers, and...
[14]

Insights from these discussions will inform updates to governance standards, ensuring fairness and transparency

Financial Sector Example: In the financial sector, quarterly forums will allow stakeholders to discuss AI governance improvements, focusing on high- risk systems like trading models or credit scoring. Insights from these discussions will inform updates to governance standards,...
[15]

Implementation: Risk management strategies in the governance framework leverage advanced technologies and cross-functional collaboration [18]. Key components include: • Continuous, Automated Risk Assessments: The framework will use continuous, automated risk assessments throug...
[16]

Test results will trigger compliance checks, security measures, and updates to governance standards, ensuring AI systems remain secure and compliant

Financial Sector Example: In the financial sector, high- risk AI systems like trading models or credit scoring algorithms will undergo regular vulnerability testing. Test results will trigger compliance checks, security measures, and updates to governance standards, ensuring A...
[17]

It will include role -specific modules for executives, technical staff, and compliance officers, ensuring tailored, relevant training

Implementation: The implementation of education and training resources involves several key components: • Structured Curriculum Development: A structured curriculum will cover AI fundamentals, governance frameworks, and legal and ethical considerations. It will include role -s...
[18]

TABLE I

Financial Sector Example: In the financial sector, workshops and training sessions will help banks and fintechs integrate AI governance practices, particularly for high -risk systems like automated lending or fraud detection. Certification programs will ensure AI risk managers...
[19]

The framework’s decentralized governance mechanisms will monitor and audit for bias using ethical auditing throu gh smart contracts, ensuring regular fairness evaluations

Bias in AI Decision -Making: AI systems in finance are prone to biases from training data, potentially leading to discriminatory decisions like unfair credit scoring or biased investment strategies. The framework’s decentralized governance mechanisms will monitor and audit for...
[20]

To resolve this, the framework stores only essential metadata on -chain and uses decentralized storage (e.g., IPFS) for personal data

Data Privacy vs Transparency: Blockchain’s transparency can conflict with data privacy regulations like GDPR, particularly when high- risk AI systems process sensitive data. To resolve this, the framework stores only essential metadata on -chain and uses decentralized storage ...
[21]

The framework uses Decentralized Identities (DID) for AI systems and stakeholders, ensur ing traceability of actions and enforceable accountability

Accountatbility in a Decentralized System: Decentralized governance distributes decision -making among stakeholders, enhancing fairness but raising accountability concerns when AI systems make harmful decisions. The framework uses Decentralized Identities (DID) for AI systems ...
[22]

The framework mitigates this with anti- collusion measures, such as weighted voting and caps on voting power, to prevent undue influence

Tokenized Incentives and Governance Manipulation: While tokenized incentives encourage participation and reward ethical compliance, they risk creating profit -driven governance, where powerful entities might manipulate decisions. The framework mitigates this with anti- collusi...
[23]

For instance, data privacy in Europe is stricter than in other regions

Cross-Cultural Ethical Disparities: AI ethics vary across regions, creating challenges in forming universal standards. For instance, data privacy in Europe is stricter than in other regions. The decentralized framework will aim to balance consistent ethical standards with flex...
[24]

• Blockchain Infrastructure Development: Ensure security, scalability, and compliance with financial sector requirements

Phase 1 (2024- 2026): Development and Testing of Blockchain Infrastructure and Smart Contracts: The primary goal of Phase 1 is to build and test the foundational elements, including blockchain infrastructure and smart contracts.. • Blockchain Infrastructure Development: Ensure...

2024
[25]

• Sector Pilots: Test in the financial and healthcare sectors, focusing on high- risk AI (e.g., lending algorithms, AI diagnostics)

Phase 2 (2026-2028): Pilot Programs and Expansion to Key Markets: Phase 2 aims to expand the framework’s reach by launching pilot programs in additional sectors and markets, testing the framework’s interoperability, compliance, and risk management features across diverse regul...

2026
[26]

Phase 3 (2028 -2030): Full Implementation Across G20 Countries: The goal of Phase 3 is to achieve full implementation of the governance framework across G20 countries, with a particular emphasis on integrating emerging economies. • Adaptation to Local Contexts: The framework w...
[27]

• Global Rollout: Extend the framework to all major economies, ensuring adaptability to local conditions

Phase 4 (2030 Onwards): Global Rollout and Continuous Refinement: Phase 4 will focus on extending the framework’s reach beyond the G20 to all major economies, with a continuous refinement process to adapt to new technological developments, regulatory changes, and stakeholder f...

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Implementation: The implementation involves active participation from regulators, financial institutions, and AI developers. Given the sector's high stakes, the DPoS mechanism integrates multiple layers of security and accountability: • Stakeholder Engagement and Voting Power: Stakeholders elect delegates to make governance decisions, with voting power ty...

work page

[2] [2]

For example, AI-driven credit scoring models, deemed high-risk, would be continuously monitored, with governance standards dynamically adjusted ba sed on real -time data

Financial Sector Example: In practice, this DPoS mechanism enables collaboration between banks, fintechs, and regulators to classify AI systems by risk. For example, AI-driven credit scoring models, deemed high-risk, would be continuously monitored, with governance standards dynamically adjusted ba sed on real -time data. This classification triggers stri...

work page

[3] [3]

Key components include: • Modular Smart Contract Design: Smart contracts, developed using languages like Solidity for Ethereum-based systems [8], feature a modular architecture

Implementation: Implementing smart contracts involves integrating blockchain and AI to create a dynamic, responsive compliance system. Key components include: • Modular Smart Contract Design: Smart contracts, developed using languages like Solidity for Ethereum-based systems [8], feature a modular architecture. Each compliance component (e.g., data privac...

work page

[4] [4]

Financial Sector Example: In practice, smart contracts can enforce compliance with regulations like Basel III, automatically verifying loan agreements and adjusting terms based on real-time financial data. In derivatives trading, they can execute trades only if they meet compliance criteria, reducing the risk of error, fraud, and non- compliance, while im...

work page

[5] [5]

Implementation: The implementation of the DID system integrates advanced technologies to ensure security, privacy, and scalability [10]. Key steps include: • Creation of Unique DIDs: Each AI system is assigned a unique DID using cryptographic algorithms like elliptic curve cryptography (ECC) or RSA, ensuring security and preventing duplication. DIDs are f...

work page

[6] [6]

If compliance changes due to a regulatory update, smart contracts automatically update the DID and trigger necessary audits, ensuring continuous oversight

Financial Sector Example: In the financial sector, high- risk AI models, such as trading algorithms, will receive blockchain-based DIDs that include risk classification and compliance status. If compliance changes due to a regulatory update, smart contracts automatically update the DID and trigger necessary audits, ensuring continuous oversight. D. Cross-...

work page

[7] [7]

Key components include: • Rigorous Accreditation Process for Auditors: The success of the auditing network relies on a robust accreditation process

Implementation: The implementation of a decentralized AI auditing network is a multi -layered process that requires the integration of advanced technologies and rigorous governance protocols. Key components include: • Rigorous Accreditation Process for Auditors: The success of the auditing network relies on a robust accreditation process. Auditors undergo...

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For example, a global AI -driven credit scoring system is periodically audited for compliance with local and international regulations

Financial Sector Example: In the financial sector, AI models in high -risk applications, such as credit scoring and algorithmic trading, undergo regular audits by accredited auditors. For example, a global AI -driven credit scoring system is periodically audited for compliance with local and international regulations. Audit findings can be recorded on the...

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