Artificial intelligence now writes essays, predicts diseases, and approves bank loans. That’s amazing—until a model hallucinates or someone tweaks data to game the result. When we can’t audit how an AI reached its answer, trust evaporates. Verifiable & Responsible AI flips that script: every prediction ships with cryptographic proof that it was produced by the approved model, on authentic data, in a tamper‑proof environment.
Everyday Problems It Can Fix
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Healthcare – A radiology AI says “no tumor.” Verifiable AI lets doctors (and regulators) check a cryptographic receipt showing the model, version, and untouched scan used.
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Education – Exam scores generated by an AI grader come with verifiable proof of fair scoring criteria, ending “black‑box” complaints.
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Data Integrity – Suppliers submit metrics to a procurement AI; buyers get a proof that no one swapped numbers after the fact.
How NEAR Makes It Work
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Private ML SDK – NEAR’s open‑source toolkit runs models inside Intel TDX CPUs and NVIDIA TEEs, shielding raw data from prying eyes.
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On‑Chain Proofs – After inference, the enclave issues a cryptographic attestation. That proof—and only the minimal proof—can be anchored on NEAR’s blockchain so anyone can audit integrity later.
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NEAR Account System – Every action (uploading data, calling the model, publishing a result) is signed by a human‑readable NEAR account like
alice.near. These signatures, plus access‑key permissions, create an immutable provenance trail tying who did what to every AI decision.
Why “Responsible” Too?
Privacy and accountability are two sides of the same coin. By locking data in TEEs, users keep ownership. By publishing proofs on-chain, society gets transparency. And by linking actions to NEAR accounts, we know which human or service is responsible if something goes wrong.
The Takeaway
Verifiable & Responsible AI doesn’t ask you to “just trust the algorithm.” It proves its work—privately, cryptographically, and immutably—thanks to NEAR’s Private ML SDK and account‑based provenance. That’s the foundation we need for safe AI in medicine, learning, and every data‑driven decision that really matters.
