A sociotechnical evaluation of an algorithmic service allocation tool in Helsinki Employment Services
Paraczky, Anna (2025)
2025
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025060420097
https://urn.fi/URN:NBN:fi:amk-2025060420097
Tiivistelmä
The objective of this thesis was to evaluate an algorithmic tool developed by Helsinki Employment Services to help allocate clients to suitable service categories. The purpose was to evaluate the effectiveness of the tool from the perspective of employment specialists, identify key limitations, and provide practical recommendations for improving its functionality in everyday workflows.
The thesis used Sociotechnical Systems (STS) theory as a framework, highlighting that both technical design and social context are important when implementing digital tools. A survey with both quantitative and qualitative questions was conducted among employment specialists.
Overall, the algorithmic tool represents a promising initiative in digitalising employment services. However, key findings showed that employment specialists were sceptical about the tool’s accuracy in reflecting the labour market situations and working capacities of the clients. Main concerns included misclassification of clients, insufficient training provided, unclear logic behind the tool, and its tendency to oversimplify more complex client cases.
The results show that the algorithmic tool needs clear criteria and more transparency in how results are informed. On the organisational side, better training, clearer communication, and involving employment specialists directly in future improvements are recommended.
The thesis used Sociotechnical Systems (STS) theory as a framework, highlighting that both technical design and social context are important when implementing digital tools. A survey with both quantitative and qualitative questions was conducted among employment specialists.
Overall, the algorithmic tool represents a promising initiative in digitalising employment services. However, key findings showed that employment specialists were sceptical about the tool’s accuracy in reflecting the labour market situations and working capacities of the clients. Main concerns included misclassification of clients, insufficient training provided, unclear logic behind the tool, and its tendency to oversimplify more complex client cases.
The results show that the algorithmic tool needs clear criteria and more transparency in how results are informed. On the organisational side, better training, clearer communication, and involving employment specialists directly in future improvements are recommended.