2-day virtual workshop
June 10-11 2024, 8am-12pm PT
AI innovations have spread across most aspects of people’s lives. Spam filters block messages no one wants, ride-sharing services predict service demand and dynamically adjust pricing, entertainment and retail services recommend desired items and content, bots and robots automate tedious and dangerous work, and intelligent systems help forecast the future, from this week’s weather to the number of sweaters a company will sell to how bad traffic might be. Recent advances created new capabilities, such as systems that detect cancer better than doctors, AI players that beat grandmasters, driverless road and aerial vehicles, and content generation systems that open a world of possibility and raise red flags around ethics and unintended harm.
The success of AI makes it feel like this technology is ripe for innovation. However, today, almost 90% of AI initiatives fail. In addition, innovation teams often fail to recognize low-hanging fruit, situations where a little simple AI would add real customer value. Current technical innovation approaches don’t work well when applied to AI. The HCI research community has been working on how to improve the process from brainstorming to prototyping to delivery. This workshop takes some of what we’ve been teaching our students at the Human-Computer Interaction Institute at Carnegie Mellon University and adopts it for practitioners.
This workshop will be teaching a handful of new techniques that designers, product managers, and researchers can take back and start using immediately. The workshop will be short lectures to introduce a technique, then exercises working with the method hands-on. The first half of the workshop will teach the techniques of Matchmaking and Concept Ranking: how to find the best uses of different AI technologies. How to build the right thing, in other words.
In the second half, we’ll consider How to Build the Thing Right. We’ll start with Adaptive UIs: how to utilize places in our existing products where we can insert AI for the most value for our users. We’ll do an exercise on Explaining AI: how can users trust what they’re seeing and what happens when AI guesses wrong. We’ll end with Consequence Scanning to identify risks (and attempt to overcome them).
