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.
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Scott Siebert
Partner
Scott’s background is in qualitative research and human behavior. He has been working in research for eight years, with a particular interest in human factors research. He has conducted research in a variety of industries, including health care, automotive, and financial services, with experience in focus groups, in-lab usability testing, summative medical testing, and in-car research. Scott has an MA in global politics from Illinois State University.



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Read our team’s latest bold insights
Three things to improve acceptance of AI
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.
Recruiting methods and study logistics for human factors and user research
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.
Singularity and the potential impact on UX design principles
If we are approaching a rapid technology shift as some experts predict, core UX design principles will have to be redefined to adapt to radically different interaction models.