Today we all use artificial intelligence in our daily lives, whether it’s through Alexa or your next pizza being delivered in a self-driving car. How does this affect our jobs in UX and where do we begin to understand its impact?
Here’s a roundup of six great reads:
What is deep learning and how does it work? The easy guide for everyone
This is your 101 course on AI, machine learning, and deep learning. As the terms are sometimes used interchangeably the differences can be unclear.
By walking through an example, it explains
- general AI and narrow AI
- that machine learning is simply a way of achieving AI
- how deep learning uses neural networks to mimic human intelligence and the three elements in a neural network
The impact of AI on UX design in 2018
In this quick read, Jack Strachan, who is studying industrial design and technology, provides a human-centered approach to today’s technology. Right away, I was compelled to read further by learning “over the past five years alone the accuracy of this intelligence has increased to an average of 95% when compared to the human eye.”
Jack has three lessons, or really mantras, when approaching UX design with AI:
- Think collaboratively, not competitively
- Design for interaction
- Be transparent, be inclusive
This one statement really sums up their view of AI: “Let people do what they do best and let machines do what people do worst (Humans take pictures, machines find answers) because in order for us to build trust in the impact of AI we must feel reassured, included and informed.”
AI UX: 7 principles of designing good AI products
This articles goes through seven basic AI UX principles products to follow when designing AI products. It provides many great examples of good and bad AI use to support each principle.
“As designers, we aim to create useful, easy-to-understand products in order to bring clarity to this shady new world of machine learning. Most importantly, we want to use the power of AI to make people’s lives easier and more joyful.”
The seven principles highlighted:
- Differentiate AI content visually
- Explain how machines think
- Set the right expectations
- Find and handle weird edge cases
- Provide engineers with the right training data
- User testing for AI products (default methods won’t work here)
- Provide opportunity to give feedback
Mitigating algorithmic bias in predictive justice: 4 design principles for AI fairness
AI isn’t being used just for consumer products. Algorithms are being used to convict criminals and decide jail time. Vyacheslav Polonski, a UX researcher at Google, wants to make sure they are fair. He points out, “there is a wave of new companies that provide predictive services to courts; for example, in the form of risk-assessment algorithms that estimate the likelihood of recidivism for criminals to help judges in their decision-making.” But many of these algorithms are reinforcing racial biases in law enforcement data.
The article, based on research he did at Oxford University, provides food for thought about the data being used to train AI products and the importance of being aware of blind spots.
The UX of AI
Google works across the company to bring UXers up to speed on core ML concepts, understand how to best integrate ML into the UX utility belt, and ensure the ML and AI are built in inclusive ways.
The article uses a case study of Google Clips, an intelligent camera designed to capture candid moments of familiar people and pets. It uses completely on-device machine intelligence to learn to only focus on the people you spend time with, as well as to understand what makes for a beautiful and memorable photograph.
They also highlight a human-centered approach to AI. “If you aren’t aligned with a human need, you’re just going to build a very powerful system to address a very small — or perhaps nonexistent — problem.”
How AI solves UX/UI design problems?
AI is arising as an effective tool to bring a better user experience for every individual. It can help to personalize the user experience positively by using readily available data.
The article highlights:
- how AI can externalize the UX process by providing data about user behavior to identify design problems and help identify solutions.
- the amount of quantitative usability information that can be gathered including user flow, number of visits in a day, drop rate, and more.
- how AI can eliminate one-sided testing approaches.