The project

Algorithmic management, which encompasses the digital direction, evaluation and optimization of work processes, is growing in both new organizational forms emerging from the gig economy and in so-called “traditional” organizations, where artificial intelligence (AI) is used to track, categorize, evaluate and manage workers’ actions and emotions.  

The Workaround project focuses on the circumvention practices implemented by workers using these algorithmic surveillance technologies, through the discourses of the developers of these technologies, the organizations adopting them as well as the workers elaborating such practices, in the context of three distinct technologies : 

  1. Activity-tracking technologies (bossware – field 1) 
  2. Emotion evaluating technologies (AI-powered sentiment analysis – field 2) 
  3. On-demand work platforms (crowdworking – field 3) 

Understanding the phenomenon of algo-surveillance via circumvention practices 

As AI technologies and remote working modes gain in popularity, workers are developing new forms of circumvention practices, which this project aspires to shed light on in order to better understand the phenomenon of algo-surveillance. With these three fields of study, we seek to understand and make visible the development process of circumvention practices elaborated by workers facing these algorithmic surveillance technologies.  

The methodology 

The project mobilizes a qualitative methodological strategy deploying discursive and content analysis of associated promotional and technical documents from the point of view of their design and functionality. It incorporates interviews with developers and managers involved in the development, marketing and/or use of these technologies, as well as an online ethnographic study of the spaces where workaround strategies are shared. It also mobilizes participatory “reverse-engineering” workshops with groups of workers who will develop workaround practices in an attempt to understand and influence the scores assigned to them by the technologies studied.  

This research is funded by SSRHC (2023-2026) and certified by the UQAM Institutional Committee on Ethics in Research Involving Humans.