Biopiracy 2.0 raguramsa 6208

Biopiracy 2.0: AI Driven Ethnobotanical Mining and the Structural Limits of India's Traditional Knowledge Digital Library Subtheme: Traditional Knowledge, Biodiversity,and Cultural Rights

Abstract: Indian Traditional Knowledge Digital Library (TKDL) embodies a structural irony of the digital age: such a carefully-crafted legal safeguard that it has the potential to promote the very evils it was meant to avert. The TKDL was designed to standardise the indigenous medicine knowledge as prior art and invalidate bad faith patent claims, but now the digitised and systematised architecture acts as a machine-readable corpus, which can be used by ethnobotanical and molecular discovery systems powered by AI. The resulting products of these systems are structurally proximate, yet formally novel compounds that meet the doctrinal criteria of novelty and inventive step in patent law, but are functionally grounded on traditional knowledge- a technologically mediated, and legally opaque kind of biopiracy.

The paper is an argument that the architecture of the TKDL, based on limiting malicious human patent examiners, is structurally incapable of dealing with AI-mediated extraction, recombination, and abstraction of guarded knowledge. This is further curtailed by the international structures that are in existence. The consent-and-benefit-sharing framework of the Nagoya Protocol, which is based on the physical transfer of biological materials, is ill-equipped to address non material forms of utilisation, which are data-driven and do not necessitate any contact with source populations. The ensuing legal vacuum is at a relatively untapped convergence between the intellectual property law, biodiversity governance, and artificial intelligence.

As the current scope of traceability in trained AI systems is limited, this paper redefines the regulation to be on upstream accountability as enforced. It suggests a three level reform agenda: first, a doctrine of computational prior art that extends TKDL defensive coverage to analogues generated by algorithms; second, a calibrated burden-shifting model ensuring non-use: obligatory training-data provenance disclosure; third, a reformed TKDL 2.0: controlled access, traceability layers, and AI resistant governance design. Taken together, these reforms aim to recalibrate the former knowledge protection in the algorithmic age and reduce the possibility of strategic circumvention.

Keywords: TKDL; AI-driven biopiracy; computational prior art; Nagoya Protocol; algorithmic governance