Research

The observations presented on this site are not offered as settled conclusions. They describe structural pressures emerging under acceleration and invite rigorous examination.

Areas of Inquiry

AI acceleration intersects with language, governance, cognition, institutional design, and ethics. The following questions remain open:

Related Work

Questions of accountability in automated systems are being examined across multiple domains. One notable line of inquiry is the concept of decision provenance.

The 2018 paper “Decision Provenance: Harnessing Data Flow for Accountable Systems” proposes capturing decision pipelines and data flows in order to make complex automated systems reviewable and auditable.

The associated initiative, decisionprovenance.org, defines decision provenance as preserving records of context, judgement, and outcome “as they existed at the time” for explanation and accountability.

This line of work shares concern for traceability and review. The framing presented here extends the discussion toward continuity of meaning, semantic drift, and the preservation of agency under acceleration.

Invitation to Dialogue

The goal of this project is not to assert a proprietary solution. It is to clarify structural tensions and invite critique, comparison, and refinement.

If related work strengthens these ideas, it should be integrated. If it challenges them, the challenge should be examined.

Continuity requires openness to revision.

References

1. Pasquale, F., et al. (2018). Decision Provenance: Harnessing Data Flow for Accountable Systems. arXiv:1804.05741. Available at: https://arxiv.org/abs/1804.05741

2. Decision Provenance Initiative. Decision Provenance — Preserving context, judgement, and outcome for accountability. Available at: https://decisionprovenance.org