Designing for interaction requires new ways to navigate and negotiate the needs of various users and stakeholders. Design should address the potential power imbalance between people and predictive, decentralized systems.
How will we make decentralised systems work for society?
Designing for multi-intentional interactionYuxi Liu
Designing for multi-intentional interactionThis project is about the challenges of decentralized interaction with data-driven systems, and the development of novel design principles for multi-intentional interaction. In this project we explore how future interfaces can manifest the potentially conflicting needs of the multiple users and stakeholders of a data-driven product-service system ("multi-intentionality"). In particular we focus on the use of techniques that can provide an additional layer of legibility of the system’s behaviour and enable trust.
Designing co-predictive relationsGrace Turtle
Designing co-predictive relationsThis project is about the challenges of delegation in the relationship between people and predictive systems, and the development of novel design principles for predictive relations. In this project we explore how different forms of recursive interplay between user and system in carrying out social practices (“co-performance”) can provide handles to a more equitable interaction between people and predictive systems, and how such forms of interaction may shape users’ sense of futurity.
Designing for contestable systemsRob Collins
Designing for contestable systemsThis project is about the challenges involved in making it possible for people to contextualise and negotiate a data-driven system’s response to users’ actions (“response-ability”) in and through use. In this project we explore what features, mechanisms and techniques need to be designed and implemented in the front-end for users to understand, contest and possibly repair inappropriate actions by a system.
Designing for trusted collaboration in human-AI teamsJacob Browne
Designing for trusted collaboration in human-AI teamsThis project explores the design of AI-enabled decision-support systems from the point of view of human-AI collaboration. In particular, we explore the different roles AI-powered systems can play to provide continuity and knowledge transfer across teams (e.g., in medical teams) through meaningful AI- and data-enabled experience concepts.