Abstract/Description

How do you build experiences that effectively combine the power of humans and machines— especially when emerging technologies like Machine Learning and Artificial Intelligence (“ML / AI”) are involved? The design sprint methodology provides a great set of tools to understand these problems quickly. Unfortunately due to the complexity of the interactions, the process isn’t straightforward. Where do we start?

This talk will introduce the basics of data science, ML / AI (so you know what you are getting into). From there we will consider how these complex and non-deterministic systems will be used in the human and organizational contexts. Exploring and understanding the expectations for humans— as well as for machines in different environments— can lead us to potential solutions.

We will take you through the Design Sprint stages (Map, Sketch, Decide, Prototype, and Test), when considering data science, ML / AI systems. As a bonus, we’ll include the often-missed step: Synthesis, and what it means for non-human intelligences.

We will discuss UX topics that include prototyping and research, to understand the assumptions around abstractions, mental models, affordances, interpretability, and trust. The talk will conclude with a discussion on how these tools will change the way we do our work as practitioners— and make us more effective in our roles.

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