How is UX Research for ML different from UX Research for design
How do you design digital products that people actually want? Get UX tips and insights from experts behind some of the most successful digital transformations and experiences in the world. Listen to our Insights Unlocked podcast and learn firsthand from some of the most notable names and brands in experience research. Click here to listen and subscribe on your favorite streaming platform.
There are many examples of how AI and machine learning can be used to inform UX research and design, but how will UX research inform how AI and ML models are created and deployed to the public?
In this Insights Unlocked episode, UserTesting’s Lawrence Williams, PhD talks with Dawn E. Procopio , founder and principal UX researcher at AI-Ethicist.com . They explore the critical role of UX researchers in ensuring that machine learning models are human-centered and ethically sound.?
Dawn is a veteran researcher and has worked with Siemens, Meta, and Amazon Web Services among other companies. As a specialist at the intersection of UX research and machine learning, she emphasizes the importance of ecological validity, ensuring that the model's conclusions actually apply to the real-world problem it intends to solve.
“And they’re not just really fanciful displays of human intellect with regard to the performance of the model,” she said.?
She said the other way UX researchers can improve a model’s performance is by collaborating with machine learning professionals throughout the process.?
“And, to me, these are almost the same issue because they do converge at some point” Dawn said. “But there are five main ways I think UX researchers need to collaborate with machine learning professionals.”
She uses the acronym PRIDE as a framework for those five ways for collaboration:
领英推荐
“Those five touchpoints in a conversation, or over many conversations, between a UX researcher and a machine learning professional can increase the ecological validity of the model and increase the performance of the model,” she said. “I just think that not a lot of researchers believe they can help these really talented engineers do their job better, but also that it's really almost unethical if they don't.”
With regard to the problem, “UX researchers are always looking to make sure that the problem is centered on the user,” Dawn said. “And this is a little bit different [with machine learning models]. When a machine learning scientist talks about a problem type, they are actually talking about the solution.”
Researchers, she said, should flip it around. When a machine learning professional says they have a problem type, Dawn said, they are probably talking about one of three things: classification, regression or clustering problems. “That’s how the data should appear to the humans, not necessarily how the human thinks about the problem” she said.?
An example would be a movie recommendation algorithm. An ML engineer may design the model to deliver movie recommendations based on how a movie is related to another movie (clustering). But the user really wants recommendations based on a thumbs up or star rating (regression). That would require changing the entire model and become very expensive, Dawn said.
By better understanding what the user wants, needs or expects from a model will help inform how the model is built (sound familiar?).
Listen to the full episode to hear Dawn and Lawrence further discuss Dawn’s PRIDE acronym and how UX researchers can better inform how AI and machine learning models are created.
Listen and subscribe to Insights Unlocked for more episodes.