David Ducheyne Algorithms are quietly reshaping how organisations manage performance. From real-time dashboards and predictive analytics to automated feedback and rankings, algorithmic performance management is becoming reality. The promise is compelling: more objectivity, greater efficiency, and decisions based on data rather than gut feeling. But there is a catch. Recent research shows that the success of algorithmic performance management has far less to do with the quality of the algorithm than with the quality of leadership around it. The Myth of the Neutral Algorithm Algorithmic systems are often introduced as neutral and objective tools. By analysing large volumes of data (KPIs, behavioural patterns, productivity metrics) they are supposed to remove bias and standardise decision-making. Yet employees rarely experience these systems as neutral. Algorithms are typically not very transparent. People do not know exactly how decisions are made, which data points matter most, or how m

Eurofound Research Pay gaps between men and women – that is, when women earn less than men for doing the same work or work of equal value, or when the set-up of pay structures and career progression disadvantages one gender over another ‒ can arise for a variety of reasons, including direct and indirect discrimination. They can also stem from an undervaluation of work traditionally carried out by women. The Pay Transparency Directive goes some way towards addressing this by requiring companies to ensure that their pay structures are based on objective, gender-neutral job evaluation that supports the implementation of the work of equal value principle. It also encourages the social partners to take pay equity for the same or equivalent jobs into account in collective bargaining. This article reflects on this principle and how social partners can support its implementation. Introduction The principle that workers shall – irrespective of their gender – receive the same pay not only for th