Use AI summaries without losing the human decision

Step 1: Decide where AI actually helps

Start by naming the specific review tasks where AI assistance saves time without touching judgment. Three earn their place in most hiring workflows: summarizing long responses (a 15-minute inside-sales role-play transcript condensed to what the candidate asked, claimed, and committed to), pointing at evidence (quoting the exact lines where a support reply addressed the refund policy criterion), and drafting criterion-level score suggestions for a human to confirm or correct.

Notice what is not on the list: ranking candidates, screening anyone out, or writing rejection reasoning. Those are decisions, and the moment a model performs them the process stops being explainable. Write down which assisted outputs your workflow uses and which it forbids — that one paragraph is the policy everything else in this playbook enforces.

• Summarize: condense transcripts and long written answers to the decision-relevant points.
• Point to evidence: quote where a response touched each rubric criterion.
• Draft scores: suggest a criterion-level score with the supporting quote attached.
• Never: rank, reject, or generate candidate-facing reasoning without human review.

Step 2: Keep the confirm-or-override step explicit

A draft score must arrive as a question, not an answer. The reviewer sees the AI suggestion alongside the evidence it cites, then takes an explicit action: confirm it, or override it with their own score and a short rationale. The critical design property is that the score does not exist until the human acts — an ignored suggestion never silently becomes a recorded score.

The evidence link is what keeps this honest. When a suggestion says 'objection handling: strong' next to the quoted exchange where a sales candidate reframed a pricing pushback, the reviewer can check the claim in seconds. A suggestion without its evidence trains reviewers to trust the number; a suggestion with its evidence trains them to read the work — which is the job.

Watch your own confirm rate as the workflow beds in. If a reviewer confirms every suggestion without ever opening the underlying response, the process has drifted into rubber-stamping — the human is technically in the loop but no longer in the judgment. Make overrides cheap to record, and treat a run of unexamined confirms as a coaching conversation rather than a time saving.

Step 3: Log model and version for every assisted output

Every summary, evidence pointer, and draft score should carry a record of which model produced it, which version, and when. This feels bureaucratic until the first time you need it: a candidate questions a decision months later, and you can show exactly what the reviewer saw, what the model suggested, and that a human confirmed or overrode it.

Version logging also makes change visible. Models get updated, and an updated model can summarize differently or drift in how generously it drafts scores. If suggestion patterns shift mid-hiring-round, the log tells you whether the candidates changed or the model did — without it, you cannot tell those apart, and your comparisons across candidates quietly stop being like-for-like.

• Record model name, version, and timestamp on every assisted output.
• Record the reviewer's action: confirmed, overridden, and the override rationale.
• Keep the log inside the decision file, next to the scores it explains.

Step 4: Spot-check AI suggestions against human scores

Trust in an assisted workflow is earned by sampling, not assumed. Once per hiring round, pull a handful of responses and compare the AI draft scores against what calibrated human reviewers decided. You are looking for systematic patterns: a model that consistently drafts support-tone scores higher than the panel, or that misses qualification gaps in sales discovery answers because the candidate sounded fluent.

Treat findings the way you treat evaluator drift. If the model runs generous on one criterion, tell reviewers to read that suggestion skeptically — or stop drafting scores for that criterion and keep only the evidence pointers. The spot-check also protects fairness: if suggestions run consistently different for one group of candidates or one response style, that is a finding to act on immediately, not a curiosity.

Log what each spot-check covered and what it found, even when the answer is 'nothing'. A recorded history of checking your own tooling is itself part of a defensible process: it shows the assistance was supervised rather than trusted blindly, and it gives you a baseline to compare against after the next model update.

Step 5: Nothing unreviewed reaches a candidate

Draw the final line where it matters most: no AI output touches a candidate-facing outcome without a human having reviewed it. A rejection, an advance to interview, feedback in a report, a score a client sees — each one passes through a named person who read the evidence and owns the call. 'The model scored it low' is never the answer to 'why was I rejected'; 'our reviewer found the response missed the customer's actual question, and here is the rubric' is.

This line is also what makes the whole arrangement worth having. Reviewers who know their name is on the decision read the evidence; summaries and pointers make that reading faster, not optional. The result is the trade you wanted from the start — AI absorbing the reading load, humans keeping the judgment — with a decision file that shows exactly who decided what, and on which evidence.