AI needs UX
For machine-learning (ML) scientists to train artificial intelligence (AI) systems and algorithms, they need data. They collect many of the datasets they use to build these AI systems from human behaviors and people’s interactions with the technology they use in their everyday lives.
Obtaining good training data is the Achilles heel of many ML scientists. Where does one get this type of data? Surprisingly, there are many sources that provide access to thousands of free data sets. Recently, Google launched a search tool to make publicly available databases for ML applications easier to find. But it is important to note that many of these databases are very esoteric—for example, “Leading Anti-aging Facial Brands in the U.S. Sales 2018.” Nonetheless, data are becoming more accessible.
As many researchers often say: All data are not equal. The inherent assumptions and context that are associated with datasets often get overlooked. If scientists do not give sufficient care to a dataset’s hygiene before plugging it into an ML system, the AI might never learn—or worse, could learn incorrectly, as we described earlier. In cases where the quality of the data may be suspect, it’s difficult to know whether the learning is real or accurate. This is a huge risk.
Knowing what we now know about machine learning and the risks and limitations of datasets, how can we mitigate these risks? The answer involves User Experience.
What you will learn through lecture, discussion and exercises:
- How UX benefits AI and ML · Limitations and pitfalls of current datasets · Examples of recent data collection projects
- Important elements in measuring and recording behavior so that the data are useful to ML scientists
- How to design a research program to collect data to support AI and ML
Michelle is an innovative and engaging critical care clinician with experience in medical device design, patient safety, and human factors testing. She manages multiple user research programs all along the development cycle and works tirelessly to ensure clinical needs are met in the user interface. In her role at GE Healthcare, Michelle works with global design and development teams to help solve some of the toughest challenges in the acute care setting. Michelle has a degree from Michigan State University.
Bob has more than 25 years of experience in UX research. He works across a variety of industries and products. With a passion for global testing, Bob authored The Handbook of Global User Research. He holds several patents, and speaks frequently on the latest trends in UX research at national and international conferences. He is co-founder of Bold Insight, and founded the first international network of UX consulting firms, which has grown to 25 countries. Bob holds a PhD in Cognitive and Experimental Psychology from the University of Illinois, Urbana-Champaign.
Gavin S. Lew. Gavin has over 25 years of experience in corporate and academic environments. He founded User Centric in 1999 and grew it to be the largest private UX consultancy in the US. After selling the company to GfK, one of the largest market research companies in the world, he continued at GfK to lead the North American UX team to become one of the most profitable business units of the parent organization. He is a frequent presenter at national and international conferences and the inventor of several patents. He is an adjunct professor at DePaul and Northwestern Universities. Gavin spent seven years in the doctoral program at Loyola University but departed ABD (all but dissertation). He has an Masters in Experimental Psychology from Loyola University. Gavin currently is the Managing Director of Bold Insight, part of a globally funded and employee owned UX consulting practice.