AI decoding of ancient scripts must confront colonial legacies, funding inequities, and indigenous knowledge gaps
Original framing: “Cracking the code: Can AI help us decipher ancient languages?” — Phys.org
Recent transformer models have shown promise in pattern recognition across large corpora, yet their performance on sparse, noisy epigraphic data remains limited. Rigorous validation against known corpora and transparent error analysis are essential to avoid over‑claiming success. Interdisciplinary collaboration with epigraphers ensures scientific robustness.
AI's capacity to detect patterns offers a powerful tool for unlocking Linear A, yet without confronting the colonial histories, funding inequities, and epistemic exclusions that have shaped script research, such breakthroughs risk reproducing past injustices.
By embedding indigenous knowledge, historical awareness, and cross‑cultural methodologies into model design, scholars can generate interpretations that honor both scientific rigor and cultural integrity. Collaborative data partnerships, multimodal modelling, and equitable funding structures provide concrete pathways to democratise decipherment. Ultimately, a disciplined trickster perspective reminds us that true progress lies not in the illusion of neutral technology, but in the intentional redistribution of interpretive power across all stakeholders.