When we talk about 'algorithmic management' we think of platforms, but the reality is broader: automatic shift assignment, punctuality scoring, prioritisation of requests, or incentives based on metrics. All of these are decisions that impact people's lives, and that is why Europe is focusing on transparency and oversight.
1) What algorithmic management is (beyond gig workers)
Any system that makes decisions or recommendations about work using data can become algorithmic management: who works when, who receives overtime, who gets a supplement, or whose change request is approved. If the team does not understand the rules, they perceive arbitrariness.
An example: a system that automatically assigns the best shifts to those with the best attendance 'score'. If it is not explained and there is no review process, it will generate resistance, even if the goal is to improve coverage.
2) Transparency: explain assignment rules and avoid black boxes
Transparency does not mean publishing source code. It means that the business rules are clear: what variables are used, how they are weighted, and what behaviours are incentivised or penalised. In shift management, this is critical for people to trust the schedule.
A good practice is to document the 'why' behind the assignment: required coverage, skills, legal rest periods, preferences, and equity. The more explicit the model, the less conflict there will be when someone does not get the shift they wanted.
3) Human oversight and the right to review
Automating is not abandoning. If an automated decision harms a person, there must be a human review channel and a correction process. This is not just compliance: it is operational quality. Systems make mistakes and data can be incomplete.
For example, if an employee appears as 'unavailable' due to a recording error, a supervisor should be able to correct it with traceability. That traceability protects the company and the worker.
4) Data quality: if the input is bad, automation is unfair
Many biases do not come from the algorithm, but from the data: incomplete records, corrections without a reason, incidents without documentation. When data is missing, the system decides 'blindly' and that can always disadvantage the same groups.
The foundation is data discipline: reliable time records, an up-to-date skills catalogue, rest period and shift rules, and a clean incident history. Without that, any automation amplifies the chaos.
5) Win-win: efficiency with trust
Well applied, automation reduces manual work, speeds up changes, and improves coverage. For it to be win-win, it must be explainable: the team needs to understand the rules to feel the system is fair.
When transparency and oversight are integrated from the design phase, technology stops being 'an imposition' and becomes a tool that makes life easier for everyone: employees, managers, and HR.