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Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts.  This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions.  HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.

The use of autonomous agents is an ever-growing possibility in our day-to-day life and, in some cases, already a reality. One future use might be autonomous robots performing last mile deliveries, a service the company HUGO delivery is currently developing. The goal of developing their autonomous delivery robot HUGO is to reduce the emissions from deliveries in the last mile by replacing delivery trucks with emission free autonomous robots. However, this new way of receiving deliveries introduces new design challenges since most people have little to no prior experience of interacting with autonomous agents. The user interface is therefore of great importance in making the user understand and be able to interact comfortably with the autonomous agent, thus also a key aspect in reaching user adoption. This thesis work examines how an interface for an autonomous food delivery service, such as the HUGO delivery, could be designed by applying a Human-Centered Artificial intelligence and Activity Centered Design focus in the design process, resulting in a design proposal for a web app. The conclusion of the thesis includes identification of the six essential interactions present in an autonomous food delivery service, as well as how HCAI and which of its guidelines can be applied when designing an interface for the interaction with an autonomous delivery robot.