how can digital technology be used in the field of archaeology

Argument against (concise): AI cannot be relied upon to decipher ancient texts on its own because its strengths—pattern recognition and statistical prediction—fail to capture the deep philological, cultural, and interpretive work that genuine decipherment requires. Key reasons: - Reliance on data that may not exist. Many ancient scripts lack large, well-labeled corpora or bilingual texts; machine-learning models need abundant, representative training data and will produce unreliable or spurious outputs when that data is absent or biased (cf. limited corpora problems in low-resource NLP). - Probabilistic, not explanatory, results. AI provides likelihoods and candidate restorations but does not offer causal or historically grounded explanations for why readings are correct. Decipherment demands argumentation about phonology, morphology, historical contact, and cultural practice that AI cannot supply independently. - Ambiguity and multiple plausible readings. Fragmentary inscriptions and palimpsests often admit many equally plausible reconstructions; statistical completion can favor the most common patterns in the training set rather than the historically accurate but rare forms. - Context and semantics require human judgment. Understanding meaning involves cultural knowledge, syntax, idiom, and semantics tied to material culture and historical context—areas where pattern models lack genuine comprehension and may mislead scholars. - Risk of reinforcing bias and error. Models trained on existing interpretations inherit their biases; using AI outputs uncritically can ossify contested readings, obscure minority hypotheses, or propagate transcription errors. - Ethical and epistemic limits. Decipherment carries responsibilities toward source communities and heritage contexts; automated claims of “decipherment” can be sensationalized without the careful corroboration the discipline demands. Conclusion: AI is a powerful assistive tool for imaging, transcription suggestion, clustering, and hypothesis generation, but it cannot replace the interdisciplinary philological reasoning, contextual interpretation, and evidential standards required for authentic decipherment. Human expertise must remain decisive in validating and explaining any AI-generated proposal. Selected references: - Seales, B., et al., work on Herculaneum scroll imaging and reconstruction. - Recent surveys in digital humanities and historical-document analysis (ICDAR, DH proceedings) on limits of ML for low-resource scripts.