Dmitry Nikolaev – Fotolia
Dutch organisations must engage in open discussions with employees about the use of algorithms and data in managing staff
By
Kim Loohuis
Published: 22 May 2024 15:42
Organisations are increasingly turning to algorithms to manage and evaluate various aspects of work. This form of algorithmic management can significantly affect employee autonomy, as highlighted in the comprehensive Own rhythm or algorithm report by Dutch research institutes TNO and Rathenau Institute.
The report’s findings suggest that using automated analyses to distribute tasks, measure performances and allocate rewards can potentially erode employee control and hinder their ability to make independent decisions.
Moreover, the researchers highlight various risks and challenges associated with algorithmic management. One of the main risks identified in the report is the risk of discrimination and bias in algorithms.
Because algorithms are trained on historical data, they can contain inherent biases that lead to discriminatory decisions, and these biases can take various forms such as gender, race or socio-economic.
As a result, algorithms can make decisions that amplify inequality and injustice, particularly for minority groups. This can lead to workplace and broader societal discrimination, which can have severe consequences for those involved and the image of organisations.
Privacy concerns
Another significant risk highlighted in the report concerns privacy issues that may arise. Since algorithms often process large amounts of sensitive information, there is a risk that this information may be misused or unlawfully used. For example, employees may worry about privacy if algorithms collect and analyse personal data without their consent or knowledge.
This can lead to breaches of trust between employees and employers, which can disrupt the work environment and affect productivity. Additionally, the researchers see risks in terms of transparency and accountability.
Algorithms are often complex and operate on large datasets, making it difficult to understand their functioning fully. This lack of transparency can make it challenging to account for the decisions made by algorithms, wh