The DEPA AI Chain is central to operationalising data sharing for AI development and runtime use, while preserving privacy and maintaining verifiable provenance across the entire AI lifecycle — spanning dataset creation and licensing through training, release, inference, and content distribution. Risks and returns are managed through contracts and programmable controls; oversight is delivered via transparency logs and lightweight audits by a self-regulatory organisation (SRO), yielding an efficient and effective supervisory mechanism.

1.0 Unpacking Provenance

Provenance, in digital systems, refers to the systematic tracking of the origin of data and the complete history of the transformations and processes it undergoes throughout its lifecycle. It captures metadata about where the data came from, how it was created, and how it has been modified, combined, or interpreted over time.

Data provenance plays a critical role across a wide range of applications and scenarios. It is essential for ensuring the reproducibility of scientific experiments and computational workflows, enabling others to independently validate results. It supports fault diagnosis and fault tolerance by providing a traceable record that helps isolate and correct errors in complex systems. Provenance is also key to explainability (but also vastly different), as it clarifies how specific outcomes or decisions were derived, particularly in contexts such as AI and automated decision-making. In addition, provenance provides vital support for forensic investigations and auditing, where establishing the trustworthiness and integrity of data is crucial for compliance, accountability, and legal defensibility. By making the history of data transparent and verifiable, provenance serves as a foundational element of trustworthy digital systems.

In the context of personal data sharing, consent without provenance is an unauditable promise. There is a need to include a machine-readable trail linking consent or data protection compliance (the promise) to verifiable facts. 

The concept of provenance is increasingly critical in the context of modern AI systems, which are pervasive across numerous domains. In such systems — often characterised by Markovian or black-box behaviours — establishing clear causal relationships between inputs and outputs is inherently challenging. The opacity of many AI models, particularly deep learning models, makes it difficult to trace how specific outcomes arise, raising significant concerns around trust, accountability, and reproducibility.

Although parallel efforts exist under the banners of Explainable AI (XAI) and Trustworthy AI (TAI), provenance offers a complementary and, in many cases, more scalable and cost-effective approach to enhancing transparency. When thoughtfully designed and integrated into AI pipelines, provenance can provide a systematic, audit-friendly mechanism to capture the lineage and transformations of data and models, often with fewer assumptions than model-specific explainability techniques.

At its core, provenance in AI systems addresses concerns such as: (i) authenticity (of data and its origins), (ii) ownership, (iii) traceability, and (iv) (approximate) reproducibility. In contrast, frameworks such as TAI tend to emphasise aspects including (i) accuracy, (ii) fairness, (iii) explainability, and (iv) safety.

Yet, even with these clear distinctions, provenance is sometimes misframed in policy discussions. Treating any and all provenance artefacts as something that inevitably leads to identity disclosure is an error, one that conflates transparency with surveillance or identity tracking. As critics often put it in “Road to Perdition” terms, unfettered access to provenance data may indeed pose risks — but such access is not meant to be unfettered. It must come with safeguards, constrained by law and subject to due oversight. Framing the choice as either no provenance or dystopia ignores both context and the inevitability of provenance as part of the solution. Even references to Puttaswamy’s judgement, frequently invoked in this debate, are incomplete if not situated within its broader framework of proportionality and legitimate state aim. After all, without engaging with principles such as purpose limitation, retention bounds, or penalties for misuse, how else are systems meant to achieve reliability and harm reduction at scale? The answer lies not in abandoning provenance, but in advancing privacy-preserving provenance — mechanisms that preserve accountability and auditability without compromising individual rights.

1.1 Promise and Potential of AI Chain

The AI Chain is fundamentally a mechanism for capturing the lineage and transformations of data and models in a systematic, effective way, offering a complementary approach to XAI. The AI Chain promises to meet the following requirements:

• Lineage: Lineage captures the complete journey of data and AI outputs—from consent and licensing, through training, to distribution—ensuring traceability, authenticity, and near-precise reproducibility of AI outcomes. It provides a granular record by assigning unique IDs to datasets and linking a Data Principal’s ID to their data and consent artefact, documenting how data is introduced, modified, combined, and interpreted. To preserve privacy, lineage can be applied to metadata rather than raw data. Cryptographic mechanisms such as hash chains and Merkle trees secure the integrity of the entire lineage.
  

• Effective Verification and Its Impact on Liability Allocation: Verifiers can check provenance artefacts—including signatures, attestations, and log proofs—at scale. This may assist in liability and accountability allocation, since the responsibilities of Training Data Providers, Training Data Consumers, publishers, and platforms are clearly stated through policies and contracts, and their actions are immutably recorded in provenance artefacts.
  

Finally, this approach has second-order effects on data quality: established provenance artefacts increase the value of well-curated datasets.

1.2 What AI Chain Is Not Intended to Do

• Truthfulness or correctness guarantees: The chain reveals who, what, when, and how a piece of content was created or modified—but it cannot confirm whether the content depicts reality.
  
• Bias/fairness or safety adjudication: The chain records facts; value judgements belong to governance, post-facto audits, and external assessments.
  
• Enforcement on off-chain actors: Entities falling outside the chain are not snapshotted and can ignore the guardrails.
  
• Eliminate the need for legal process: The chain provides strong factual and indisputable evidence, not automatic verdicts.

We welcome feedback and suggestions from all stakeholders at [email protected]

Please note: The blog post is authored by Subodh Sharma, with inputs from Sunu Engineer and Raj Shekhar, all volunteers with iSPIRT.