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The foundation is laid: your team knows the AI Act, the logs are running, and colleagues now understand what a ranking model does. Yet the question remains whether the system will still behave as exemplary tomorrow. AI tools evolve; vendors quietly implement model updates, the labor market shifts, and job descriptions change in tone. Without a routine to track that changing landscape, a neatly organized audit folder can become outdated in just a few weeks. That's where monitoring comes in. It's not an extra layer of spreadsheets but a working agreement: keep watching together whether the technology still contributes to a fair, transparent, and effective recruitment process.
Dashboards serve excellently as a starting point, but the real work happens in the dialogue between recruiter, data analyst, HR lead, and legal counsel. They discuss whether the rankings make sense, why certain candidates drop out, and if the feedback to applicants remains clear enough. The numbers are their agenda, not their goal. This distinction makes monitoring manageable for smaller HR teams without a dedicated data department.
Measuring everything ultimately means seeing nothing. In practice, five key indicators are sufficient to spot most risks early.
| Indicator | Meaning | Signal value |
|---|---|---|
| Overrule percentage | How often does a recruiter adjust the model shortlist? | A rising line points to missing context or incorrect weightings. |
| Bias difference | Ratio between demographic intake and final selection | Sudden outliers reveal underlying bias. |
| Model drift | Deviation of predictions compared to three months ago | Indicates whether new data is steering the model in unwanted directions. |
| Candidate NPS | Experience of applicants, regardless of outcome | Rapidly declining NPS often indicates non-transparent rejections. |
| Incident response time | Time between first suspicion of bias and concluding analysis | Keeps the team focused on follow-through and knowledge sharing. |
These indicators can live in a simple Supabase view or even in a shared spreadsheet. As long as everyone is looking at them, they do their job.
On Monday morning, Rima, recruitment manager at a logistics scale-up, notices that forty percent of the shortlists have been manually revised. It turns out that a recent vendor update has heavily upgraded short online courses, causing junior IT candidates with a single evening course to come out on top. The data analyst reverts the weight factor and links the change to a short log number. Two hours later, the indicator is green again.
Midweek, the bias graph shows that women often drop out in the final round for physical warehouse positions. A recruiter remembers that the job description explicitly mentions "heavy lifting." HR adjusts the text, runs an A/B test, and within two weeks, the gap narrows. This is monitoring in action: first observe, then improve.
On Friday, Legal looks at the average response time to incidents. It's at eight days; the internal standard is ten. The number goes into the management report, not because it's perfect, but because everyone now knows how quickly the team can solve problems.
Start with the five indicators and assign one owner per indicator. The recruiter records overrules in the ATS; the data analyst monitors drift; HR presents the complete picture on the first Tuesday of the month. Only log what will truly have value later: who changed what, why, which date, for which job posting. Fewer fields means quicker entry AND faster retrieval.
For reporting issues, a simple workflow is sufficient. In many teams, a Slack command "/bias <description>" automatically opens a ticket. This way, recruiters don't have to doubt whether something is worth reporting; reporting takes them less than ten seconds.
Since most AI tools are purchased externally, monitoring belongs in the contract. Agree that the vendor sends a monthly drift report, immediately warns of major weight shifts, and helps trace deviations back to data or code. Some organizations establish this as a Fairness Service Level Agreement: alongside uptime and support, threshold values for bias and response time are specified in black and white. This way, everyone knows what "green" means.
A red indicator is only valuable if something is done about it. Make every discovery public on the team channel, including cause and fix. Candidates appreciate when you explain how their feedback improves the process; internal stakeholders see that monitoring isn't bureaucracy but quality control. This is how trust grows—not through perfect numbers, but through visible corrections.
HR teams often fear that monitoring creates extra work. Experience shows the opposite: an early detected error prevents piles of manual checks, angry candidates, and expensive remedial actions. A small dashboard keeps the inbox quiet and the audit day short. This far outweighs the time you spend weekly checking five indicators.
| Monitoring approach | Traditional | Agile | Impact on team |
|---|---|---|---|
| Frequency | Monthly large audit | Daily micro-checks | Fewer work interruptions; problems stay small |
| Reporting culture | Formal forms | Low-threshold tools (Slack) | More reports; faster correction of small issues |
| Ownership | Central responsibility | Distributed across various roles | Higher engagement; better knowledge distribution |
| Documentation | Exhaustive reports | Just-enough logging | Less paperwork; more action on insights |
With monitoring, the circle is almost complete: you know the rules, master the skills, and continuously keep an eye on the system. Next week, we'll take one more step back in the chain. In part 4, we'll show how fairness-by-design already begins with the job description, long before an algorithm comes into the picture.
Embed AI helps HR teams with plug-and-play dashboards, log templates, and workshops where your people learn to recognize AND solve incidents in one day. Want to know more? Send me a message.