AI has been a game-changer in almost every industry, but Finding the Best Use Cases for AI in Your Products have been an ever-increasing and complex challenge. With the advent and hyper-growth in compute capacity over the last decade humans have become bottlenecks when it comes to harnessing Data for insights and that’s where AI steps in. UX solves a critical challenge, of enabling a virtual connection between humans’ cognition and AI insights.

Product Leadership @Uber. Product and data enthusiast, serial Inventor, and patent holder with leadership capabilities in launching 0-1 Products, global execution, enhancing product pipeline value, and leveraging data analytics for strategic planning. Ability to expand product / portfolio-line profit margins. Skilled at transitioning from developing winning strategies to disciplined execution while nurturing and motivating multidisciplinary teams..

Can you do UX Research on machine learning systems? Can you get feedback from real users before the AI has been built? Can you even test an ML system before you have a production-ready model? Yes, yes, and yes! In this talk, Michelle Carney will share about how she combines her background in machine learning with her expertise in UX - including the MLUX meetup she organizes, her favorite resource the People + AI Research Guidebook, and a case study on her process of doing UXR for ML.

Michelle Carney (@michelleRcarney) is a Computational Neuroscientist turned UX Researcher, whose practice focuses on the intersection of Data Science and UX. Currently a Senior UX Researcher on Google’s Tensorflow Team, Michelle's projects focus on combining Machine Learning and UX! Her work includes Magenta’s latest Tone Transfer project and People + AI Research team! Outside of work, Michelle organizes the Machine Learning and UX Meetup, and teaches at the Stanford d.school on Designing Machine Learning.

Are you trying to understand AI at your organization? Looking for ways to connect with engineers? Hoping to design human-centered AI experiences for users?

This talk will cover:

1. LinkedIn AI and the importance of quality data

2. A framework on how to design human-centered AI experiences

3. Cross-functional workshops that help engineers and other functions understand user perceptions of AI

Carolyn Chang is a Principal User Experience Researcher at LinkedIn. Her current passion areas include: AI, enterprise products, research impact and marketplaces. You’ll likely find her managing ambiguous, horizontal projects. One important hallmark of her work is that stakeholders have a meaningful, fun time learning about users. At home, she tries to manage 2 young kids and have a relaxing fun time.

Christine Liao is a Product Design Lead at LinkedIn. Originally intending to study medicine, she found herself enamored by the creative industry, and found her way to architecture. After a short stint at an architectural firm, she transitioned into UX design.

At LinkedIn, she manages a team focused on helping recruiters search and find candidates, and is passionate about leveraging AI as a way to help people discover candidates they might have otherwise overlooked. In her spare time, she likes baking meticulous things and drawing!

Products and solutions around us are getting increasingly smarter through their use of AI. Designing for these intelligent solutions introduces challenges for design, because they are more dynamic, personal and can continuously evolve. In this talk I give insight in the data-enabled design process that we adopt at Philips Experience Design to design for AI. Through multiple innovation projects, I give insight in our design process and shine light on our most important learnings along the way. I show how our perspective on data as a design material is instrumental in creating meaningful solutions and how designing for intelligent solutions goes way beyond technical challenges.

Sander Bogers is a Senior Data Designer at Philips Experience Design. His work focuses on how data can be a more inherent part of design processes, especially in the design for more intelligent solutions. People and their stories, and how these can be captured by data, play a central role in that. His current work is a mix of front-end innovation projects, data & AI competence building in the design team, and product development for more personalized solutions.

There will be a one-hour discussion panel with the AI / ML design experts above. Audience quesstions will be submitted via the Q&A function within Zoom.

Paul Bryan is the organizer of the UX STRAT Europe, UX STRAT USA and UX STRAT Asia conferences, as well as this Design Thinktank. Prior to organizing UX STRAT events, Paul worked for 10 years as a design strategy and innovation consultant.