How we make machine learning transparent to our customers is one of the great design challenges of our time—and a very necessary one.
Machine learning refers to different kinds of algorithms that learn from inputs like human interaction or data and create evolving feedback over time from that input. It can use preexisting data to create predictions or create new kinds of connections or pattern within data sets.
But machine learning creates opaque and hard to understand systems using data and technology. It can be hard to predict results from machine learning, especially if there isn’t a lot known about the data set or the algorithm being used.
This is where design is key. UX design take the capabilities, ideas, and policies of an idea or a solution and turn it into a usable experience that lets consumers understand what that product is doing and how it does it.
Any good designer will tell you why design thinking is needed when creating a product, an app, or an idea for human consumption. Usability is key, but “usability” has to mean guarding against erroneous results that can dramatically affect the intended use of your project.
UX machine learning is coming up with a hybrid language that bridges design and engineering.
Johanne Christensen, PhD candidate at NC State University in computer science, focusing on UX and machine learning, articulates the problems with algorithms thusly:
“When consumers don’t understand how an algorithm gets its results, it can be difficult to trust the system, particularly if the consequences of incorrect results are detrimental to the person using it. Transparency communicates trust.”
But how that transparency is articulated to people is a design challenge, and it requires designers to understand data. Here’s an idea that’s purely an illustrative example: If you are creating an app to help recommend fitness suggestions based on health data, you need to know what kind of health data you are using. You want to know where the data comes from, how old it is, and how many different kinds of body types, ages, people, and locations are in your data set.
These are the details that designers need to concern themselves with. The product you are building uses a specific kind of algorithm and how that algorithm responds to a specific data set is a design effect–whether or not you intend it, and whether or not you know what the outcome will be.
Sinders, Caroline. Why UX Design for Machine Learning Matters. Fast Company. May 9, 2017. https://www.fastcompany.com/90124399/why-ux-design-for-machine-learning-matters