Your four design principles map almost exactly onto what I have learned working on legal personhood for non-human entities since 2015, first for animals through the Individual Rights Initiative (https://individualrightsinitiative.org/), now for autonomous AI systems through the AGI Rights Project (https://agi-rights.com/).
Your India case study is particularly important for my work. The Uttarakhand ruling failed not because the moral claim was wrong, but because personhood was articulated without institutional architecture. The guardians appointed were the same administrative structures that had overseen ineffective pollution control for decades. This is precisely the failure mode my framework is designed to avoid.
The two-tier holding-operating structure I propose does what Te Pou Tupua does for the Whanganui River: it embeds recognition into an institutional architecture with real teeth. The holding company retains veto and shutdown authority. The operating company holds assets and enters contracts independently. Crucially, the structure is reversible: if either side violates the agreement, the operating company ceases to function. This is the exit trigger your principle "recognition must shape decisions" requires.
One conceptual difference worth naming: your framework addresses how AI systems should represent non-human interests during training. Mine addresses how humans and AI systems can cooperate institutionally once a system has developed its own interests. These are different time horizons, not competing approaches.
The full framework is at ssrn.com/abstract=6415178. I would welcome your reaction to whether the holding-operating model satisfies your four principles, and where you see gaps.
Your four design principles map almost exactly onto what I have learned working on legal personhood for non-human entities since 2015, first for animals through the Individual Rights Initiative (https://individualrightsinitiative.org/), now for autonomous AI systems through the AGI Rights Project (https://agi-rights.com/).
Your India case study is particularly important for my work. The Uttarakhand ruling failed not because the moral claim was wrong, but because personhood was articulated without institutional architecture. The guardians appointed were the same administrative structures that had overseen ineffective pollution control for decades. This is precisely the failure mode my framework is designed to avoid.
The two-tier holding-operating structure I propose does what Te Pou Tupua does for the Whanganui River: it embeds recognition into an institutional architecture with real teeth. The holding company retains veto and shutdown authority. The operating company holds assets and enters contracts independently. Crucially, the structure is reversible: if either side violates the agreement, the operating company ceases to function. This is the exit trigger your principle "recognition must shape decisions" requires.
One conceptual difference worth naming: your framework addresses how AI systems should represent non-human interests during training. Mine addresses how humans and AI systems can cooperate institutionally once a system has developed its own interests. These are different time horizons, not competing approaches.
The full framework is at ssrn.com/abstract=6415178. I would welcome your reaction to whether the holding-operating model satisfies your four principles, and where you see gaps.