Designing for AI/ML Human Interactions in Industrial Condition Monitoring

One challenge to industry adoption of products and services based on Machine Learning and Artificial Intelligence is that their inner workings are often not discernable to human operators. When operators can’t reason about theit tools they tend to make poor decisions about how and when to rely on them. This ultimately limkitsthe effectiveness and efficiency of these technologies in industrial applications. The objective of the proposed internship projects is to develop evidence-based design recommendations to make automated decision aids more transparent to operators, with the ultimate goal of promoting adoption of, reliance on, and effective interaction with these tools. We will explore the effects of a variety of manipulations to the work context and information provided to operators. Each of these projects is paired with an intern with prior academic and/or professional experience in AI/ML technologies. Their proposed MITACS Accelerate internships present an excellent opportunity to develop both their technical skills and their appreciation for the promises for, and challenges facing, AI/ML-based aids in industry.

Faculty Supervisor:

Greg Jamieson;Ray Gosine


David Quispe;Trung Nguyen


Ericsson Canada


Engineering - mechanical


Information and cultural industries


University of Toronto



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