038 – (Special Co-Hosted Episode) Brian and Mark Bailey Discuss 10 New Design and UX Considerations for Creating ML and AI-Driven Products and Applica...

Mark Bailey is a leading UX researcher and designer, and host of the Design for AI podcast — a

program which, similar to Experiencing Data, explores the strategies and considerations around designing data-driven human-centered applications built with machine learning and AI.

In this episode of Experiencing Data — co-released with the podcast Design for AI — Brian and Mark share the host and guest role, and discuss 10 different UX concepts teams may need to consider when approaching ML-driven data products and AI applications. A great discussion on design and #MLUX ensued, covering:

• Recognizing the barrier of trust and adoption that exists with ML, particularly at non-digital native companies, and how to address it when designing solutions.
• Why designers need to dig beyond surface level knowledge of ML, and develop a comprehensive understanding of the space
• How companies attempt to “separate reality from the movies,” with AI and ML, deploying creative strategies to build trust with end users (with specific examples from Apple and Tesla)
• Designing for “undesirable results” (how to gracefully handle the UX when a model produces unexpected predictions)
• The ongoing dance of balancing UX with organizational goals and engineering milestones
• What designers and solution creators need to be planning for and anticipating with AI products and applications
• Accessibility considerations with AI products and applications – and how itcan be improved
• Mark’s approach to ethics and community as part of the design process.
• The importance of systems design thinking when collecting data and designing models
• The different model types and deployment considerations that affect a solution’s UX — and what solution designers need to know to stay ahead
• Collaborating, and visualizing — or storyboarding — with developers, to help understand data transformation and improve model design
• The role that designers can play in developing model transparency (i.e. interpretability and explainable AI)
• Thinking about pain points or problems that can be outfitted with decision support or intelligence to make an experience better

Resources and Links:

Designing for AI Podcast

Designing for AI

Experiencing Data – Episode 35

Designing for Analytics Seminar

Seeing Theory

Measuring U

Contact Brian

@DesignforAI

Quotes from Today’s Episode

“There’s not always going to be a software application that is the output of a machine learning model or something like that. So, to me, designers need to be thinking about decision support as being the desired outcome, whatever that may be.” – Brian

“… There are [about] 30 to 40 different types of machine learning models that are the most popular ones right now. Knowing what each one of them is good for, as the designer, really helps to conform the machine learning to the problem instead of vice versa.” – Mark

“You can be technically right and effectively wrong. All the math part [may be] right, but it can be ineffective if the human adoption piece wasn’t really factored into the solution  from the start.” – Brian

“I think it’s very interesting to see what some of the big companies have done, such as Apple. They won’t use the term AI, or machine learning in any of their products. You’ll see their chips, they call them neural engines instead have anything to do with AI. I mean, so building the trust, part of it is trying to separate out reality from movies.” – Mark

“Trust and adoption is really important because of the probabilistic nature of these solutions. They’re not always going to spit out the same thing all the time. We don’t manually design every single experience anymore. We don’t always know what’s going to happen, and so it’s a system that we need to design for.” – Brian

“[Thinking about] a small piece of intelligence that adds some type of value for the customer, that can also be part of the role of the designer.” – Brian

“For a lot of us that have worked in the software industry, our power trio has been product management, software engineering lead, and some type of design lead. And then, I always talk about these rings, like, that’s the close circle. And then, the next ring out, you might have some domain experts, and some front end developer, or prototyper, a researcher, but at its core, there were these three functions there. So, with AI, is it necessary, now, that we add a fourth function to that, especially if our product was very centered around this? That’s the role of the data scientist. And so, it’s no longer a trio anymore.” – Brian