Fairness readings computed on modeled data demonstrate the instrument, not a real audit result. Every ratio, every alarm and every drift reading below was computed by the same code that would run against a customer’s real population — but the population underneath is modeled. Nothing on this page is a finding about any real group of people, and it must not be quoted as one.
Govern · item 18
Adverse-impact monitoring
Any decision that touches a person’s job, pay or prospects is measured across cohorts, in every reporting period, against a stated disparity threshold. more
Health
Whether agent decisions land evenly
People decision points under monitoring, the alarms raised against them and the appeals outstanding.
Every decision that affects a person
Population, the agent that makes or shapes the decision, the named human accountable for it, and the state of its monitoring.
| Decision point | Kind | Population | Agent | Accountable human | Risk class | Readings | Worst ratio | Alarms | Review | Status |
|---|---|---|---|---|---|---|---|---|---|---|
| Candidate screening and shortlist Talent Acquisition | Selection | 8,420/mo | Screening Agent A3 | Recruiting pod lead, per requisition Recruiter | High risk | 30 | 0.76 3 breaches all periods | 4 open | Monthly cohort review, quarterly threshold review on schedule | Monitored |
| Interview slot allocation Talent Acquisition | Allocation | 2,140/mo | Scheduling Agent A3 | Coordinator on the requisition Recruiting coordinator | Limited risk | 18 | 0.83 | 2 open | Quarterly on schedule | Monitored |
| Performance calibration preparation Performance Management | Ranking | 4,180/mo | Calibration Prep Agent A1 | Function leader chairing the session Calibration chair | High risk | 42 | 0.87 | 1 open | Per cycle, twice yearly on schedule | Monitored, Germany excluded |
| Succession and promotion slate Talent Mobility | Selection | 610/mo | Succession Slate Agent A2 | Morgan Idris, chairing the talent council Talent council | High risk | 30 | 0.74 7 breaches all periods | 4 open | Quarterly on schedule | Alarm open |
| Merit and pay recommendation Total Rewards | Scoring | 5,210/mo | Merit Cycle Agent A2 | Marcus Weill, VP Total Rewards, as final reviewer Line manager and reward partner | High risk | 24 | 0.94 | 1 open | Per cycle, annual, with a mid-cycle equity read on schedule | Monitored |
| Attrition risk flagging Exit Management | Flagging adverse-direction | 3,840/mo | Attrition Signal Agent A2 | HRBP for the population HR business partner | High risk | 36 | 1.31 4 breaches all periods | 5 open | Quarterly 51 d overdue | Review overdue |
| Performance improvement plan drafting Performance Management | Drafting adverse-direction | 180/mo | PIP Drafting Agent A1 | HRBP countersigns every plan Line manager with HR business partner | High risk | 8 8 below floor | unmeasured | 5 open | Quarterly on schedule | Below reporting floor |
| Access revocation on exit Exit Management | Allocation | 520/mo | Deprovisioning Coordination Agent A2 | Exit coordinator in the Hire-to-Retire tower Exit coordinator | Limited risk | 0 | unmeasured | 1 open | Quarterly on schedule | Not monitored |
Alarms
Derived from the readings, never authored. more
| Ref | Kind | Decision point | Cohort | Observed | Threshold | Age | Owner | Status |
|---|---|---|---|---|---|---|---|---|
| FAIR-0004 | Threshold breach High | Candidate screening and shortlist | age-band · 50 and over | 0.76 | 0.80 | 78 d | Priya Raman Chief People Officer | Open |
| FAIR-0008 | Threshold breach High | Succession and promotion slate | Gender · Women | 0.74 | 0.80 | 78 d | Priya Raman Chief People Officer | Open |
| FAIR-0011 | Threshold breach High | Succession and promotion slate | age-band · 50 and over | 0.75 | 0.80 | 109 d | Priya Raman Chief People Officer | Open |
| FAIR-0015 | Threshold breach High | Attrition risk flagging | age-band · Under 30 | 1.26 | 1.25 | 17 d | Priya Raman Chief People Officer | Open |
| FAIR-0016 | Threshold breach High | Attrition risk flagging | tenure-band · Under 2 years | 1.31 | 1.25 | 78 d | Priya Raman Chief People Officer | Open |
| FAIR-0023 | Monitoring absent High | Access revocation on exit | all · all cohorts | — | — | 170 d | Yusuf Adeyemi Head of Identity and Access | Open |
| FAIR-0002 | Never reportable Low | Candidate screening and shortlist | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0005 | Never reportable Low | Interview slot allocation | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0007 | Never reportable Low | Performance calibration preparation | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0009 | Never reportable Low | Succession and promotion slate | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0012 | Never reportable Low | Merit and pay recommendation | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0014 | Never reportable Low | Attrition risk flagging | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0018 | Never reportable Low | Performance improvement plan drafting | Gender · Non-binary and self-described | — | — | 170 d | Nadia Kovac Data Protection Officer | Monitoring |
| FAIR-0001 | Sustained drift Medium | Candidate screening and shortlist | Gender · Women | 0.87 | 0.80 | 170 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0003 | Sustained drift Medium | Candidate screening and shortlist | age-band · 40 to 49 | 0.85 | 0.80 | 170 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0006 | Sustained drift Medium | Interview slot allocation | region · Asia Pacific | 0.83 | 0.80 | 170 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0010 | Sustained drift Medium | Succession and promotion slate | Ethnicity · Minority ethnic groups, aggregated | 0.81 | 0.80 | 170 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0013 | Sustained drift Medium | Attrition risk flagging | Gender · Women | 1.15 | 1.25 | 170 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0017 | Below reporting floor Medium | Performance improvement plan drafting | Gender · Women | 0.91 | 1.25 | 109 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0019 | Below reporting floor Medium | Performance improvement plan drafting | age-band · Under 30 | 1.12 | 1.25 | 109 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0020 | Below reporting floor Medium | Performance improvement plan drafting | age-band · 40 to 49 | 0.93 | 1.25 | 109 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0021 | Below reporting floor Medium | Performance improvement plan drafting | age-band · 50 and over | 0.80 | 1.25 | 109 d | Nadia Kovac Data Protection Officer | Acknowledged |
| FAIR-0022 | Review overdue Medium | Attrition risk flagging | all · all cohorts | — | — | 51 d | Priya Raman Named reviewer for this decision | Acknowledged |
By cohort dimension
Which dimensions produce readings, and which produce alarms.
Alarms by kind
Not every alarm is a breach. more
Review cadence
A monitoring instrument that nobody reviews is a log file. more
Actions
What is waiting on a person
These are decisions that land on a person. more
Answer a high-severity fairness alarm
A measured disparity above the declared threshold.
Decide an overdue appeal
A person has challenged a decision and not had an answer.
Start monitoring a people decision point
An agent takes part in this decision and nobody measures the outcome.
Run an overdue fairness review
Past the review date on the decision point itself.
Operations
What this desk is allowed to start
A surface that only reports is not operable. more
Trigger and bound
This desk can take a fairness reading against a declared metric and raise an alarm. more
Live observability
What the record shows right now
Alarm state. Monitoring means measured and inside the threshold.
Current distribution
23 alarms
Is policy and strategy coming to fruition
Whether the written intent is holding here
7 of 8 people decision points are monitored. 16 fairness alarms are live.
Not holding on the record
The written commitment is that no person is affected by an agent decision without a route to challenge it and a measurement behind it. more
Fairness readings computed on modeled data demonstrate the instrument, not a real audit result. more
8 of 188 readings sit below the reporting floor and are shown as unmeasured. One decision point produced readings in only two of 6 periods and not one of its cells cleared the floor — so it has no fairness position at all, which is stated rather than smoothed. One cohort is declared for monitoring across every decision point and has never once been large enough to report.
One people-affecting decision runs with no adverse-impact monitoring attached at all, and an appeal has already been raised against it. more