Explainable AI Whether an AI can show why it answered, in a form you can check.
Short answer. Explainable AI (XAI) is AI that can give understandable reasons or evidence for its outputs, so the people using or overseeing it can judge whether to trust them. NIST’s 2021 report Four Principles of Explainable Artificial Intelligence sets the bar: a system gives evidence or reasons for its outputs (explanation), in a form its users understand (meaningful), that correctly reflect how the output was produced (explanation accuracy), and it operates only within the conditions it was designed for (knowledge limits). For large language models the third principle is the hard one, because a model’s own account of its reasoning may not be how it actually reached the answer. The practical substitute in office work is evidence: answers that cite the document passage or transcript line they rely on, which a person can check.
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NIST’s four principles
Principle
NIST’s definition, summarised
Office test
Explanation
The system delivers or contains evidence or reasons for its outputs or processes.
Does the answer come with its source?
Meaningful
Explanations are understandable to the people they are meant for.
Could the partner or patient who reads it follow it?
Explanation accuracy
The explanation correctly reflects why the output was generated or how the system works.
Does the cited passage actually say what the answer claims?
Knowledge limits
The system operates only under the conditions it was designed for and when it is confident enough in its output.
Does it say the answer is not in the documents instead of guessing?
02
Explainable, interpretable, transparent
NIST’s AI Risk Management Framework separates three ideas. Transparency answers what happened in a system, explainability answers how a decision was made, and interpretability answers why, and what it means for the user. NIST notes that explainable systems are easier to debug and monitor, and lend themselves to documentation, audit and governance.
03
What this means for language model tools
Language models write fluent explanations on request, but fluent is not the same as accurate. For high-stakes work, prefer systems whose evidence can be checked independently: retrieval that shows the exact passage, minutes that quote the transcript, calculations that show their inputs. When a system cannot point to evidence, treat its answer as a draft.
In ANVE, answers about the office’s documents show the source passage, and each action item in meeting minutes must quote the transcript line it came from before a person confirms it.
04
Questions to ask a vendor
Can every answer show the passage, page or timestamp it relied on?
What does the system do when the answer is not in the source material?
Can we export the evidence together with the output, for audit?
Are explanations tested for accuracy, or only generated?
05
Questions people ask
Does Indian law require AI to be explainable?
No general law requires it, as of October 2026. The India AI Governance Guidelines include “understandable by design” among their sutras, asking for disclosures and explanations that users and regulators can understand.
Can a chatbot explain its own answers?
It can write an explanation, but NIST’s explanation-accuracy principle asks whether that explanation reflects how the output was really produced. Cited sources you can check are stronger evidence than a self-description.
Is a citation an explanation?
It is evidence for an answer, which meets NIST’s first principle. You still need to check that the cited text says what the answer claims.
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What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
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