Auditability is the property of a scoring or decision system that lets every output be traced back to the inputs, rules, and evidence that produced it. A reviewer who was not in the room should be able to look at any score later and see exactly why it came out the way it did.
An auditable system keeps three things attached to every score: the rubric that was applied, the raw evidence that was read, and the specific line items that earned or cost points. Nothing gets averaged away or hidden inside a model. If a founder scored a 7 on execution, the record shows which shipped products, prior roles, or references drove that number.
That is different from a system that just outputs a number. A black-box model can be accurate and still fail an audit, because no one, including the people who built it, can reconstruct why a specific candidate or founder got a specific score. Regulators are treating that gap as a real liability, not a theoretical one.
Auditability has moved from best practice to legal requirement. As of early 2026, the EEOC's algorithm auditing requirements mandate that employers using AI-powered recruitment tools "maintain detailed algorithmic impact assessments" and demonstrate "clear, auditable reasoning" for the decisions those tools produce. The enforcement pattern so far is telling: most organizations that failed compliance did so because their systems could not provide "the granular decision-making transparency needed for bias auditing," not because the outcomes themselves were unusually skewed.
For a fund scoring founders or a portfolio company scoring candidates, the same logic applies even without a regulator asking. A partner defending a pass or a hiring manager defending a rejection needs to point to something more concrete than a gut call. That is the whole premise behind evidence-based scoring: every score should come with a receipt. We go deeper on what that looks like in practice in how we tie every score to a line of evidence.
The two get used interchangeably but they answer different questions. Explainability is about the moment a score is produced: can the system say, right then, why it reached that conclusion. Auditability is about later: can a different person, weeks or months afterward, reconstruct the reasoning from a permanent record. A system can be explainable in the moment and still fail an audit if that explanation is never stored anywhere. ScoringFactory treats the two together, an rubric-driven score with the evidence trail saved, so the "why" survives past the moment the decision was made.
No. A confidence score tells you how sure a model is, not why it reached its answer. Auditability requires the underlying evidence and rubric to be retrievable, not just a probability attached to the output.
It changes what gets stored, not how fast a score gets produced. Every score is generated with its evidence trail already attached, so nothing needs to be reconstructed after the fact.
In practice it is whoever needs to defend the decision later: a partner justifying a pass in an investment memo, a hiring manager responding to a candidate dispute, or a compliance team responding to a regulator's request.
See how ScoringFactory keeps the evidence behind every founder and candidate score, ready for any partner or auditor who asks.