A R N I N G C H A N G E S T H E D E S I G N P R O C E S S MACHINE LEARNING WON’T REACH ITS POTENTIAL – AND MAY ACTUALLY CAUSE HARM – IF IT DOESN’T DEVELOP IN TANDEM WITH USER EXPERIENCE DESIGN. Caroline Sinders, Fast Company
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Spotify - Discovery Weekly Discovery Weekly is an automated music recommendation digest for each Spotify user every monday. It uses a feedback loop mechanism to personalize, optimize or automate the existing service.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Re-think customer personas Thanks to machine learning, computers will soon know your customers better than your customers know themselves. • They’re much better at „guesswork“ than humans are • More efficient targeting of new customers • More cost-effective
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Make tons of data manageable Big data techniques and analytics changed the way that businesses conduct their everyday operations. The sheer volume of data is where Deep Learning algorithms come in to deliver superior insights.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Let users tell about poor information For example in banking, one could consider the temporal evolution of account balances to segment savings behaviors. This type of algorithms that leads to decision-making needs to learn to be more precise. It’s the designer’s job to find ways to let users tell implicitly or explicitly about poor information.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Design for engagement responsibly Today, algorithms typically score the relevance of social and news content. Major online services are fighting to hook people, grab their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. They use techniques that promote addiction = hooking people endlessly searching for the next reward. That new power raises the need for new design principles in the age of machine learning.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Empathy is not (yet) available The ethical and practical considerations of machine learning have to be shaped by how products using machine learning affect users and how users can understand and see those effects.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Illustrate for transparency When users don’t understand how an algorithm gets its results, it can be difficult to trust the system. Transparency communicates trust.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Seamful design Designers must know that a „Prediction Feature“ is not the same as informing, and consider how well such a prediction could support a user action.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Challenges for UX Machine bias: AI can lead to discrimination Most of the current facial recognition techniques use the same data set, which was trained on mainly white people. It would not recognise people with other skintones.
R N I N G C H A N G E S T H E D E S I G N P R O C E S S Principles for designing AI responsibly • AI must be designed to assist humanity • AI must be transparent • AI must maximize efficiencies without destroying the dignity of people • AI must be designed for intelligent privacy • AI must have algorithmic accountability • AI must guard against bias Satya Nadella, Microsoft CEO