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Reimagining the Goals and Methods of UX for ML/AI

This position paper for the “Designing the User Experience of Machine Learning Systems” symposium challenges UX conventions and proposes new approaches for Machine Learning (ML) and Artificial Intelligence (AI). Through live demos and a presentation, I’ll discuss how designers can reimagine the goals and focus of UX for the unique potentials of ML/AI. Using animistic design as an example, I’ll propose how using simple intelligence, machine learning, and autonomous personalities can allow the designer to shift from crafting task oriented experiences for users, to building evolving, diverse, autonomous ecologies that support collaborative exploration and creativity for machine and human participants alike. A new context requires new approaches Machine Learning (ML) and Artificial Intelligence (AI) based systems are significantly different contexts for design than that of traditional mobile, web, service and product design. ML/AI systems are often non-visual and focused on complex behaviors and extended interactions with multiple people and digital systems, balancing goals through a collaborative approach that is not only focused on task completion. For example, autonomous cars will simultaneously interact with passengers, pedestrians, cyclists, police officers, and traffic control personnel. These interactions will span time: pre-ride, ride, post-ride, and modes of communication: voice, gesture, facial expressions, tangible interaction. Using its own intentions, the vehicle negotiates the often competing goals and communications of people, while at the same time negotiating with many other autonomous systems such as other vehicles, smart sensors, intelligent light posts, and municipal traffic management systems. This is a complex and different design context. An evolving, negotiated, inconsistent, improvised, serendipitous interaction that does not easily resolve to task accomplishment, efficiency, certainty, ROI, customer expectations, or for that matter, one user’s experience. And this different complexity is not a problem to be eliminated, but is a necessary and useful outcome. When ML/AI systems are constantly learning, adapting, and renegotiating in a context of other evolving autonomous systems and humans, the design constraints and goals are different from conventional UX. For example, in a future of pervasive autonomous vehicles, the overall success or health of the transit ecosystem may become the highest priority, creating a different emphasis than designers are accustomed to. Similarly, in AR/VR contexts, multiple ML/AI driven virtual entities will “live” and interact with each other and people inside mixed reality environments. These smart virtual entities will autonomously gather and represent data, assist humans, construct and modify virtual elements, and collaborate with ML/AI systems out in the real world. Because autonomous virtual entities are unconstrained by the limits of physics, scale, and economy, there is a bigger potential for manifesting their POV, intentions, and “ideas” through action in the virtual or mixed environment. Multiple smart virtual entities can actively collaborate in much more “tangible” ways, working side-by-side with people to create virtual buildings, artwork, products, datascapes, biological models and so on. Will conventional approaches to User Experience (UX) work well in contexts like this? What would a journey map look like? How would user interviews work? What would a content strategy be? How would a competitive analysis be useful? Would personas be an effective model for understanding interactions? How would these methods work when the interactions are complex negotiations between multiple autonomous ML/AI systems, each with different characteristics? Who is the “user,” or is “user” even an appropriate way to understand the problem? Whether autonomous cars or VR robot collaborators, the ways in which autonomous things behave, interact, communicate, embody a “lived” history, evolve, and thrive will call for new design methods and patterns. Because of this, the advent of ML/AI requires the reimagining of design conventions. To create systems that successfully use the affordances and constraints of ML/AI The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems Technical Report SS-17-04