Michelle Carney began her career in the worlds of neuroscience and machine learning where she worked on the original Python Notebooks. As she fine-tuned ML models and started to notice discrepancies in the human experience of using these models, her interest turned towards UX. Michelle discusses how her work today as a UX researcher at Google impacts her work with teams leveraging ML in their applications. She explains how her interest in the crossover of ML and UX led her to start MLUX, a collection of meet-up events where professionals from both data science and design can connect and share methods and ideas. MLUX now hosts meet-ups in several locations as well as virtually.
Our conversation begins with Michelle’s explanation of how she teaches data scientists to integrate UX into the development of their products. As a teacher, Michelle utilizes the IDEO Design Kit with her students at the Stanford School of Design (d.school). In her teaching she shares some of the unlearning that data scientists need to do when trying to approach their work with a UX perspective in her course, Designing Machine Learning.
Finally, we also discussed what UX designers need to know about designing for ML/AI. Michelle also talks about how model interpretability is a facet of UX design and why model accuracy isn’t always the most important element of a ML application. Michelle ends the conversation with an emphasis on the need for more interdisciplinary voices in the fields of ML and AI.
Skip to a topic here:
- Michelle talks about what drove her career shift from machine learning and neuroscience to user experience (1:15)
- Michelle explains what MLUX is (4:40)
- How to get ML teams on board with the importance of user experience (6:54)
- Michelle discusses the “unlearning” data scientists might have to do as they reconsider ML from a UX perspective (9:15)
- Brian and Michelle talk about the importance of considering the UX from the beginning of model development (10:45)
- Michelle expounds on different ways to measure the effectiveness of user experience (15:10)
- Brian and Michelle talk about what is driving the increase in the need for designers on ML teams (19:59)
- Michelle explains the role of design around model interpretability and explainability (24:44)
Quotes from Today’s Episode
- “The first step to business value is the hurdle of adoption. A user has to be willing to try—and care—before you ever will get to business value.” - Brian O’Neill (13:01)
- “There’s so much talk about business value and there’s very little talk about adoption. I think providing value to the end-user is the gateway to getting any business value. If you’re building anything that has a human in the loop that’s not fully automated, you can’t get to business value if you don’t get through the first gate of adoption.” - Brian O’Neill (13:17)
- “I think that designers who are able to design for ambiguity are going to be the ones that tackle a lot of this AI and ML stuff.” - Michelle Carney (19:43)
- “That’s something that we have to think about with our ML models. We’re coming into this user’s life where there’s a lot of other things going on and our model is not their top priority, so we should design it so that it fits into their ecosystem.” - Michelle Carney (3:27)
- “If we aren’t thinking about privacy and ethics and explainability and usability from the beginning, then it’s not going to be embedded into our products. If we just treat usability of our ML models as a checkbox, then it just plays the role of a compliance function.” - Michelle Carney (11:52)
- “I don’t think you need to know ML or machine learning in order to design for ML and machine learning. You don’t need to understand how to build a model, you need to understand what the model does. You need to understand what the inputs and the outputs are.” - Michelle Carney (18:45)
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