Exploring issues related to relying on machine learning to make judgements for content relevancy, and questioning how successful artificial intelligence is at deriving user intention.
January 2017 – Present | Amazon
I work on Project Cracker Jack with the Amazon Seller Support Design and Research team, as well as a set of other University of Washington students, to examine predictive machine learning interfaces. My process includes:
Conducting literature and competitive reviews to evaluate existing contextual interfaces, creating target user profiles, performing usability evaluations of comparable interface designs, and presenting a report to Amazon stakeholders.
Synthesis of findings into user requirements, sketching, development, and iteration of prototype design solutions, and evaluation and presentation of design mockups.
1) Design without limitations. Get creative and think differently to invent then simplify.
2) Criticism is a gift. Getting critical feedback from the people around you will only help you grow.
3) NETWORK. If someone asks if you want to get coffee, get coffee.
*Due to non-disclosure agreements, I can’t provide project details. If you’re interested in learning more about my process, shoot me an email!