Today, every business sector and industry is incorporating artificial intelligence (AI) to automate processes, improve efficiency, and reduce costs, including the industry of digital transformation, which is using artificial intelligence specifically to improve the user experience.
In this article, I’ll discuss some impacts of artificial intelligence on user experiences, what UX processes artificial intelligence can make more efficient, how artificial intelligence can help UX designers, and how artificial intelligence might affect UX design jobs in the future. Read More
In this month’s edition of Ask UXmatters, our panel of UX experts discusses how UX design for artificial intelligence (AI) applications differs from designing a traditional application. A panelist warns the questioner about the dangers of over-relying on artificial intelligence instead of defining a product that truly meets users’ needs.
Our experts then consider the role of User Experience in the creation of AI applications—especially those that rely on machine learning (ML). Their discussion ranges from the importance of user advocacy, the value of doing user research, how to avoid bias, defining high-quality training data, transparency to users, and gaining user trust by ensuring that the user feels in control of an AI application. This column concludes with a brief discussion of the need for UX design best practices for AI applications. Read More
Artificial-intelligence (AI) technology is capable of behaving with human-like intelligence. With recent advances, AI has become more pervasive. Insurance companies use AI in processing claims and banks rely on automated stock trading. People can perform self-checks for skin cancer, using smart apps such as Skinvision or HealthAI-Skin Cancer, or they can interact with intelligent services through user interfaces such as Google Home or Amazon Echo, which are themselves smart because they understand natural-language queries and provide answers using natural language as well.
Most users of AI technologies do not have sufficient insight into their inner workings to understand how they’ve arrived at their outputs. This, in turn, makes it hard for people to trust the technology, learn from it, or to be able to correctly predict future situations. Read More