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With a rich background of experience in automotive, Mary spent 10 years as a mechanical engineer in the industry, with roles in design, manufacturing, and quality. Her entry point to the user experience began there, in design validation testing. She has conducted qualitative and quantitative research in human factors and UX, on projects involving attentional, perceptual, cognitive load, and cognitive bias considerations. She is particularly interested in behavior change design, with training and experience in designing and testing behavioral interventions. Mary has a MS in Engineering Management from the University of Detroit-Mercy, and a MS in Human Factors Psychology from the University of Idaho.
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A quick follow-up to our blog posts about AI… The name of the game is no longer Moore's Law where we see processors getting exponentially faster. AI technology is driven not by computing processes of the past, but from an evolution beyond central processing unit (CPU)...
The first step when developing AI is to understand the user need; but just as critical, is knowing the context in which the data is being collected.
To truly deliver on the promise of AI, developers need to keep the end users in mind. By integrating three components of context, interaction, and trust, AI can be the runaway success that futurists predict it will be.
A stronger recruiting strategy that includes relationships with patient support groups and clinical treatment centers can provide better access to difficult-to-reach patient populations. Being intentional about how you plan the logistics of your human factors and user research can mitigate risks to validity introduced by biases.