The use of AI in hiring has become increasingly commonplace, with 98% of FTSE 100 companies now using it. As we have documented, a range of algorithmic functionalities that use varying sources of data and approaches to processing, can underpin these systems. Our particular focus is that of LLMs, that may be able to provide a more holistic view of a candidate’s suitability for a role, compared to current keyword matching approaches used in applicant screening. The specific use of LLMs in this context presents challenges for governance regarding equality and discrimination, the effectiveness of audit tools, and prompts critical reflection on their effectiveness in the workplace.

Detecting work methods is key to future automation, and advancements in technologies such as Edge Computing and Digital Twins allow for real-time guidance to workers and restructure the tasks they carry out within the workplace. If done correctly, these technologies can be used to redesign jobs and their corresponding tasks, in ways that improve job quality and wellbeing. However, there are various approaches to eliciting knowledge, which may infringe on individuals’ rights and freedoms. Additionally, systems have varying degrees of robustness and can change firm capabilities where procurement of SaaS solutions is not critical. This presents questions for good work, and firm sustainability.

Advancements in computer vision and machine learning have led to the use of affective computing in the workplace to identify and interpret worker behaviors and sentiments. Whilst workplace monitoring is not a new concept, these practices have intensified post-pandemic. We aim to better understand the ways in which increases in workplace surveillance and opacity can impact fundamental rights and worker wellbeing as well as how this use of AI can generate new insights that may help support workers, and identify excessive workloads.

The use of AI within hiring practices has been well documented. However, AI is also commonly used to allocate tasks to workers within jobs, based on perceived competencies. Further, it may do this based on different sources of data and derived analyses of the skills of individual workers. This technological innovation may allow firms to allocate jobs more effectively across their workforce, potentially increasing productivity but has associated risks. We are interested in understanding the potential impacts of the objectification of skills within the workplace, including the possibility of discrimination as well as how this technology benefits the workplace.