Explainable AI
Explainable AI refers to methods and design choices that make it possible for people to understand why an AI system produced a particular result, so they can check, question and trust it appropriately.
Definition
Explainable AI, sometimes shortened to XAI, is about making an AI result understandable. For a simple model, such as a scoring grid, the explanation is the grid itself. For complex models, such as neural networks, additional techniques show which inputs had the most influence on a result, or the system is designed to give reasons alongside its output.
A good explanation is judged by who reads it: it should be understandable to the person who needs to act on it, faithful to how the result was really produced, and specific enough to be checked. In hiring or evaluation, that can mean showing which answers and which criteria led to a score, with the supporting extract from the conversation, so that a reviewer can agree or disagree.
Without explanations, people either trust an AI blindly or reject it wholesale. Explanations support human oversight, help detect bias, and answer candidates or employees who ask how a decision was reached. A common pitfall is a plausible explanation written after the fact that does not reflect how the system actually worked, so ask providers how their explanations are produced.
Key points
- It helps people understand why an AI produced a result.
- Simple, criteria-based scoring is easier to explain than a black-box model.
- A useful explanation links a result to specific inputs that a person can check.
- It supports human oversight, bias detection and transparency to the people concerned.
- Beware explanations that sound plausible but do not reflect how the result was produced.
Example
A hiring manager sees that a candidate scored 2 out of 5 on “customer orientation”. The report shows the criterion, the two answers considered and the extract in which the candidate described an unresolved complaint. The manager listens to the extract and decides that the score is fair for this role.
Frequently asked questions
Is explainable AI the same as transparent AI?
They are related. Transparency is about openness on how a system is built and used, while explainability is about understanding a particular result.
Are all AI systems explainable?
Not equally. Some approaches are easy to explain, while others need extra techniques that give only partial insight.
Why does explainability matter in recruitment?
Because decisions affect people, who can ask how they were reached. Reviewers also need to see the reasons for a score before they rely on it.
Related terms
AI conversations, human decisions
Passisto holds structured voice conversations with candidates, employees, clients and students, and turns them into reports your team can act on.