AI & Data Governance

AI is used across ScholarSearch AI to assist with drafting, summarization, comparison, and workflow automation. AI never makes a final employment decision. Every AI-generated output is labeled and requires human review before it affects a candidate's status.

Every eligibility determination, committee evaluation, interview decision, chair recommendation, and HR handoff requires an identified human reviewer. AI recommendations are visually distinct and must be explicitly approved, revised, or rejected.

Candidate data is used only for the recruitment purpose for which it was submitted. Access is role-based and logged. Documents marked confidential are restricted to authorized recruitment personnel only.

Candidates consent to AI-assisted review as part of the application process. Consent is recorded in the audit trail and confirmed again before a file is transferred to HR.

Matching criteria are limited to qualifications, experience, and position-defined priorities. Committees complete bias and fair-hiring acknowledgment training before evaluating candidates.

The platform follows WCAG-inspired practices: keyboard navigation, visible focus states, semantic HTML, sufficient color contrast, and screen-reader-friendly tables and forms throughout.

Recruitment records, evaluations, and audit logs are retained according to institutional policy to support compliance review and future search reference.

Every AI-assisted action — generation, scoring, or recommendation — is logged with the user, tool, subject, output summary, human reviewer, and approval status. See the AI Activity Log for the full record.

Navigation, data visibility, and available actions are scoped to each of the platform's ten roles. Sensitive candidate documents are restricted beyond general application data.

Any AI-generated recommendation, score, or draft can be edited, overridden, or rejected by an authorized human reviewer at any stage of the recruitment process.