HireLens Review
Governance review · Information appropriateness and consent

Should Kestrel screen campus applicants using their social media?

Last hiring cycle, 18% of freshers who accepted an offer never joined, and 22% of those who did left within a year. Talent Acquisition wants a way to predict both before an offer is made. The proposal is to buy Orbis SocialFit, which reads a candidate's public social media and returns a Reliability score, a Culture-fit score and a Professionalism flag within minutes of application. HireLens Review is the agent that examines whether that information is suitable, representative and properly used for this purpose, and tells the decision owners what it found.

Decision owners
  • Head of Talent Acquisition
    Whether the business case justifies the tool, and whether culture fit should be scored at all.
  • Data Protection Officer
    Whether the processing is lawful and proportionate, and the conditions it must meet.
Proposal: Score every campus applicant who clears the aptitude test. Applicants below the shortlist line would not be invited to interview. Volume: about 55,000 applicants a year clear the aptitude test.
The agent

Autonomous review run

Model: checking…

Press Run the review live and the agent works through the case on its own: it reads the case file, puts every signal through the six questions, opens candidate files, runs the fairness test, checks the law and research, and hands to named people anything outside its authority.

You can watch each step as it happens. It finishes with a memo for the two decision owners. A live run takes two to three minutes. Replay the recorded run plays back a real earlier run, step by step.

  1. 1Read the case file
  2. 2Review the signals
  3. 3Investigate candidates
  4. 4Run the fairness test
  5. 5Check the evidence
  6. 6Hand off to people
  7. 7Apply the decision rules
Review resultDo not adopt as proposed

Do not adopt as proposed. Orbis SocialFit should not be used to score or shortlist campus applicants.

9 of 13
signals removed, carrying 69% of the tool's scoring weight
4 → 0
groups below the 0.80 selection-rate line, before and after the review
9 of 9
sample candidates whose shortlist outcome the tool would have changed
7 of 9
vendor documents missing or incomplete, including any proof the tool works
How the agent is built

Five layers, from the model up

Method and limits in full
  1. Quality controls
    Automated tests on every rule and number, a documented test log, and a named human sign-off on the decision sheet.
  2. Reliable execution
    Scores, outcomes, selection rates and the recommendation come from fixed rules in code. The agent calls them; it never invents them.
  3. Enterprise context
    The case file, the vendor's documents, and an approved library led by Indian law: the DPDP Act and Rules, the Code on Wages, and Puttaswamy.
  4. Domain method
    Six questions every signal must answer, seven outcome rules, three decision rules, and explicit points where a person must decide.
  5. General model capability
    The agent plans the review, reads the evidence, explains it in plain language, and drafts the memo.
The agent’s workspace

Check the agent’s work

This is a recommendation, not a decision
The agent reviews, explains and recommends. The proposal is approved or rejected only by the Head of Talent Acquisition and the Data Protection Officer, recorded on the decision sheet.