Evidence and Responsibility in the Age of AI

Research record

Full chain of AI proof-responsibility Claim → Source → Origin-Chain → Version/Model → Process → Human Check → Decision → Impact → audit trail → Correction/Appeal → Liability Fluent Answer ≠ Proof ; automation ≠ responsibility-deletion ; “The model said” ≠ reasonable cause ; audit trail without decision = retest hard ; Admit error + correction + appeal = responsive system The basic assertion of this chapter is: in the AI ​​age, understand evidence as a “chain of origin”, not the “content of an answer”; And construct responsibility as “role-specific duties and ultimate accountability” rather than “blame on one person”. Verifiable sources, versioned processes, human authorization, audit trail, correction and appeal—they are elements of the same responsive knowledge system. First principle—AI output can be prima facie information, not self-evident evidence. In general brainstorming, verification can be light; high-stakes Have a mandatory level of independent scrutiny of original sources in medicine, law, finance, historical citations, academic references or public policy. Second principle—the burden of proof claim Change accordingly. “This book is famous” and “This sentence is on such and such a page” would not ask for the same verification. Numbers, dates, citations, legal provisions, pharmaceutical quantities, scientific results, attribution and archival claim demand high source-specificity.

Structured debate

Pūrvapakṣa

First premise: “AI is more accurate on average than humans, so humans everywhere should be removed.” Average performance would not show context, subgroup, rare case and accountability cost. More accurate system can be useful, but failure mode, appeal, domain shift and harm severity checks are required for authority transfer. The second premise: “AI is only a tool; the responsibility belongs to the entire user.” Tool design, defaults, warnings, capability claims, deployment constraints and foreseeable misuse can generate developer/deployer duty. Hammer-simple tool analogies not always applicable to adaptive opaque systems. Third premise: “The model made the decision automatically, so no human is responsible.” The environment, objective, data access, threshold, deployment and authority of automatic decisions are determined by the human/organization. Autonomy can be an operational description, not an argument for an accountability vacuum. Fourth premise: “The source is given, so the answer is authentic.” source existence Only the first stage. relevant passage, exact quotation, source authority, date, context, conflicting sources and inference validity to be checked. Fifth premise: “explainable AI solves all problems.” explanation fidelity, comprehensibility, audience, manipulability and causal relevance questioned. Beautiful explanation would not legitimize unjust policy.

Uttarapakṣa

First rule of the answer—classify the claim. General interpretation, exact fact, number, quotation, diagnosis, legal interpretation, prediction, recommendation, ranking, eligibility decision—all have different evidence thresholds. the system interface should show the user this distinction. Second rule—create source ladder. primary document/original data high priority; reputable secondary analysis Next; tertiary summary auxiliary; uncited AI prose only navigation/brainstorming. The more important the claim, the higher the source in the ladder. Third rule—citation verification Both automatic + human. Can check URL/DOI existence check, quoted string match, publication metadata, date freshness machine; Check semantic relevance, context and controversial interpretation human/expert. Fourth rule—keep provenance log minimum dataset: timestamp, model/version, system configuration, user prompt or case identifier, retrieved source IDs, tool actions, decision rule, human reviewer, final action. privacy-sensitive field remain access-controlled. Fifth Rule—high-stakes decisions should have a “reason for action” structured. Who evidence considered, who excluded, key rule/threshold, uncertainty, human reviewer, available appeal. generic “system determined” not because.

Parallel conclusion

In the AI ​​age, proof only means “Where is the link?”. No; How the claims originated, what sources/models/versions were used, what human judgments were involved, what uncertainties remained, and how revisions were possible—this entire series forms the modern form of proof. Responsibility is also not just blame. Without role-specific duty, competent oversight, traceability, monitoring, incident response, correction and appeal, the words “human in the loop” or “ethical AI” can remain empty. The most dangerous sentence can be—“the system said so.” This sentence takes the place of the cause, but not the cause; takes the form of authority, but not legitimacy. The proper questions are: why is the system configured like this, what is the evidence, who is the reviewer, who is the decision authority, who will correct if there is harm? The Indian Pramanamimansa and the Nyaya-Navyanyaya tradition of Mithila teach modern AI governance an important lesson: go through the scrutiny of knowledge claims for means, faults, doubts, scope, titles, counterparts and rebuttals. Modern audit is not a mechanical copy of this ancient discipline, but a parallel reworking.

Section index

1. Problem and scope · 13 source passages
2. Central thesis · 13 source passages
3. Major arguments · 18 source passages
4. Pūrvapakṣa · 12 source passages
5. Uttarapakṣa · 14 source passages
6. Indian philosophical dialogue · 12 source passages
7. Mithila’s parallel perspective · 8 source passages
8. Contemporary applications · 24 source passages
9. Chapter conclusion · 6 source passages
10. Chapter bibliography · 52 source passages

Cite this record

Gajendra Thakur. “Evidence and Responsibility in the Age of AI.” Videha Digital Research Archive: Mithila–Vajji–Anga. Videha — https://www.videha.co.in/ · ISSN 2229-547X · GitHub mirror: https://videha-ejournal.github.io/videha/ · Digital Research Archives on GitHub: https://github.com/videha-ejournal. https://videha-ejournal.github.io/mithila-vajji-anga/records/idea/philosophy-v2-89/