The onset of COVID-19 has seen a significant increase in investment in ‘smart’ machines by organisations throughout the world. Although full automation is already possible in physical, labour-intensive work domains, these machines are now able to support workers to perform a range of ‘cognitive’ tasks. One exciting application is the use of Decision Support Systems (DSS), which leverage cutting edge advancements in algorithms, Artificial Intelligence (AI) and Machine Learning (ML) to help users predict the path of bush fires, select cancer treatments, and even investigate crimes. Greater acceptance of DSSs is expected to yield significant operational and safety benefits (i.e., from increased reliability), as well as user-level wellbeing benefits (e.g., reduced frustration). Increased user control has been implicated in positive human-DSS interactions, however recent work has identified cynicism among some workers regarding the technology, which may represent a barrier to its full exploitation. This presentation will explore recent experimental findings that highlight important roles for both users’ control and non-conscious attitudes in their acceptance and satisfaction in using DSS. These findings are translated into several design and training recommendations intended to provide guidance to developers and workplaces implementing DSS technology in the near future.

After watching the presentation, audience members will be able to:
- Describe what Decision Support Systems (DSSs) can do
- Understand the reasons driving increased DSS implementation at work
- Understand the benefits of using DSSs across a range of workplaces
- Understand the impacts of reduced user acceptance of DSS to workers’
effectiveness, safety, and wellbeing
- Recall a range of factors that influence users’ acceptance of DSS recommendations
- Understand how barriers to safe and effective DSS use may be addressed at designer, worker, and organisational levels
- Understand the challenges associated with reducing non-conscious biases

Morrison, Ben W;
Kelson, Joshua N;
Morrison, Natalie M V;
Innes, John M;
Al-Saggaf, Yeslam;
Zelic, Gregory;
Paul, Manoranjan;
McMullen, Travis