The Algorithm Has Bad Days Too

The algorithm also has bad days

An algorithm trained on historical data also learns the biases baked into that data. If a certain type of person got promoted for years, a model learning from that history will tend to recommend exactly the same pattern, wrapped in the appearance of objectivity that a number provides.

• An algorithm isn't neutral by default, it's only as fair as the data it was trained on.
• The appearance of objectivity in a number can hide a bias that's harder to spot than a person's opinion.
• Auditing an evaluation system for bias isn't optional if it's used to make decisions about people.

Apparent objectivity, inherited bias

More and more organisations are adopting automatic scoring models to evaluate candidates, measure potential or prioritise succession plans. Few have seriously asked whether these models are perpetuating historical patterns they'd rather not repeat.

Algorithmic bias is rarely intentional, and that's exactly what makes it dangerous, nobody actively decides to discriminate, but the system learns from a past that already had its own imbalances and reproduces them with the quiet authority of a data point.

• Potential models trained on promotion histories that were far from balanced.
• Automatic scores that penalise non linear career paths without anyone able to explain why.
• Decisions justified by a number that nobody has audited since the system was implemented.

Auditing the model is part of leadership

We believe technology can help reduce human bias, but only if it's designed and reviewed with that explicit intention. Without auditing, an algorithm doesn't remove bias, it simply hides it behind a number that seems harder to question than a person's opinion.

That's why we insist on treating any evaluation model as something alive that needs constant review, not a black box installed once and trusted forever. Fairness isn't solved at the design stage, it's sustained through regular audits.

How to watch for bias without abandoning data

Reducing algorithmic bias in talent evaluation is achievable with a disciplined process, not a generic promise of impartiality.

1. Audit the training data before the model
   • Check whether the historical data used to train a system reflects patterns you don't want to repeat.
   • Fixing bias at the source is more effective than correcting it later in the scores.
2. Compare outcomes across groups on a regular basis
   • Check whether the system scores comparable profiles differently for reasons that shouldn't matter.
   • Do this on a fixed schedule, not only when a complaint arises.
3. Always keep a human review channel open
   • Anyone affected by an automatic score should be able to ask for a human review.
   • That channel needs to be real, not a symbolic formality.
4. Document and communicate the system's criteria
   • Explain, in understandable language, which variables the model uses and why.
   • Opacity is, in itself, a risk factor for undetected bias.

Questions before trusting a score

Before trusting one more decision to an automated system, it's worth asking:

• What historical data was the model you use for talent evaluation trained on?
• Have you ever compared how the system scores different profiles with comparable merit?
• Is there a real channel for someone to challenge an automatic score that affects them?
• When was the last time you audited your evaluation system for bias?
• Could you explain, without calling the vendor, why the system scores the way it does?

Let's audit what your model recommends

You don't need to distrust all technology to take algorithmic bias seriously. You need to subject it to the same scrutiny you'd apply to any person making decisions about others.

If you'd like to review how well audited your evaluation systems are, let's talk. It's the kind of review that usually brings peace of mind, even when it finds something to fix.