Inside the Black Box: How algorithms are made – and why it matters From warehouses to recruitment platforms, algorithms are quietly becoming the new managers of modern work. Increasingly, decisions about hiring, scheduling, productivity and even dismissal are shaped by automated systems that analyse vast amounts of data. These technologies promise efficiency and innovation, but they also raise important questions about fairness, transparency and worker rights. Algorithms are often presented as neutral tools, but they reflect the choic­es, assumptions and data used to build them. Past data is likely to reflect inequal­ities – such as gender or racial bias in hiring – and algorithms may reproduce and even amplify such patterns. At the same time, many systems operate as»black boxes«, which makes it difficult for workers, unions or regulators to understand how decisions, which are likely to affect people’s livelihoods, are really made. This paper takes readers inside the process of how algorithms are designed, trained and deployed in workplaces. It highlights the key stages at which bias, ex­clusion or unfair treatment may enter the system, from definition of the problem to selection of data and testing of models. It also examines the growing impor­tance of algorithmic audits and stronger governance to ensure accountability. The message is clear: algorithms are not destiny. With stronger oversight, worker par­ticipation and ethical design, these technologies can support more transparent, equitable and humane workplaces rather than reinforce existing inequalities. Further information on the topic can be found here: ↗ fes.de