What is explainable AI?
Definition
Explainable AI (XAI) describes AI systems whose outputs come with information a person can understand about why they were produced: the evidence, factors or reasoning behind each result.
How it works
Explainability can take several forms: showing which factors contributed to a score, linking a result to the evidence it came from, breaking an overall rating into its parts, or giving a plain-language rationale that can be checked against the source data.
Why it matters
People affected by a decision, and the people accountable for it, need to understand how it was reached. Explanations make it possible to spot errors, answer a candidate's challenge, and audit decisions later.
Example
Instead of a bare "72% match", a candidate report shows which role requirements the candidate met, which assessment evidence supports each score, and where the risks are, so the hiring team can see why the result came out as it did.
AuraSync's approach
AuraSync attaches the evidence behind every score, ranking and recommendation, and AskAura can explain the reasoning behind a conclusion on demand, so results stay transparent and reviewable.
Limitations
An explanation is not proof that a result is correct. Explanations simplify, and AI-written explanations can sound plausible while being imprecise, so they should be checked against the underlying materials.