The increasing capacity to capture and feed behavioral data for systems to learn is transforming the design of user experiences. In this paper, we discuss two effects of this emerging toolkit. On the one hand, creating experiences with learning algorithms is pushing designers to consider how users begin, evolve, and end their interactions, which themselves produce and consume data. On the other hand, the design of experiences powered by machine learning is now occurring in new, multidisciplinary teams, which presents a variety of frictions and opportunities for misunderstandings which must be overcome. We discuss the similarities and differences in the methods that designers and data scientists use in their work, and conclude with a series of touch points and principles that partnerships between designers and data scientists can consider for productive relationships.
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