A talent acquisition leader's guide to AI-enabled work assessment

Place the leader in the right decision system

Classify the talent acquisition leader as an owner, influencer, reviewer, or collaborator for each decision. They may own participant communications and process design while legal, accessibility, security, the relevant functional leader, and an accountable human decision-maker own other judgments. Record who can approve the assessment purpose, scenario, accommodations route, data handling, scoring interpretation, and any change in use.

Bound the exercise to a realistic task and a defined population. Specify what the simulation gives participants, what it observes, what the report may say, and what it cannot establish. Separate evidence about performance inside the task from claims about future workplace performance. Also separate development or onboarding uses from selection uses; the consequences, review requirements, and acceptable evidence may differ.

The US Department of Labor's AI and Inclusive Hiring Framework announcement describes a framework built around accessibility and disability-inclusive practices when organizations assess, acquire, or deploy AI-enabled hiring technology. It is guidance, not proof that a particular process is fair or compliant.

Sources: [1]

Build the assessment-use charter

The charter should name the purpose, population, task, work context, time limit, permitted assistance, data inputs, captured events, scored evidence, human interpretation, retention, participant notice, accommodation route, appeal or review route, accountable owners, and expiry date. Add explicit prohibited uses, including automated ranking, unsupported prediction, or reusing scores for a materially different purpose.

For each proposed signal, ask what behavior the task creates an opportunity to observe. A polished final document alone cannot show every check, source review, or revision. Workflow events may be incomplete. Missing evidence should reduce confidence, not be converted into a negative inference. Define how reviewers handle interrupted sessions, inaccessible material, technical faults, task ambiguity, and evidence that is not comparable.

The US Equal Employment Opportunity Commission's Artificial Intelligence and the ADA resource page routes employers to its technical assistance on algorithms, assessments, and disability. The relevant lesson for a process owner is to establish an accessible route and review the actual assessment context. The page does not replace jurisdiction-specific legal advice.

Sources: [2]

Worked artifact: a synthetic proposal exercise

Consider a synthetic 30-minute exercise in which participants review an inbox message, a short request document, and a spreadsheet, then produce a compliance note. The assessment-use charter states that the exercise is intended to observe task framing, evidence navigation, AI orchestration, verification, judgment, and communication in that bounded simulation. It says the report is interpreted by trained humans and cannot decide whether someone is hired, rejected, or advanced.

During a pilot, one participant cannot operate a spreadsheet interaction using their assistive setup. The incident log shows that the task no longer created the same opportunity to inspect the evidence. The leader pauses interpretation of that record, provides an approved accessible format, records the variation, and asks the assessment and accessibility owners whether the alternate route preserves the relevant task demands.

A second participant submits a strong deliverable but has incomplete workflow telemetry after a connectivity failure. The report preserves the final-output evidence and marks process-level confidence as limited. The team does not treat absent events as proof that verification did not occur. This example is synthetic and establishes no legal conclusion or validated outcome.

Verify before wider use

Run a small pilot with realistic instructions, the actual interface, representative source materials, and documented review criteria. Test the accommodation route, timer behavior, source access, AI permissions, submission recovery, reviewer consistency, and explanations shown to participants. Use deliberately ambiguous and conflicting evidence to check whether the rubric distinguishes sound judgment from superficial completion.

Review disagreements by tracing them to the task, evidence, rubric, interface, or reviewer interpretation. Do not hide disagreement inside one aggregate score. Reopen the charter when the population, purpose, scenario, tool permissions, evidence capture, scoring method, report audience, retention, or decision consequence changes.

Two Prune can provide task-bounded evidence from a controlled AI-enabled work simulation for human interpretation. It is not an applicant tracker, automated selection system, employment decision-maker, regulatory approval, or proof of future performance.