Facial recognition systems pose numerous ethical challenges around privacy, racial and gender bias, and accuracy, yet little guidance is available for designers and developers. We explore solutions to these challenges in a four-phase design process to create Civil War Twin (CWT), an educational web-based application where users can discover their lookalikes from the American Civil War era (1861-65) while learning more about facial recognition and history. Through this design process, we synthesize industry guidelines, consult with scholars of history, gender, and race, evaluate CWT in feedback sessions with diverse prospective users, and conduct a usability study with crowd workers. We iteratively formulate design goals to incorporate transparency, inclusivity, speculative design, and empathy into our application. We found that users' perceived learning about the strengths and limitations of facial recognition and Civil War history improved after using CWT, and that our design successfully met users' ethical standards. We also discuss how our ethical design process can be applied to future facial recognition applications. / Master of Science / Facial recognition systems, such as those used in cities, smartphone application and airports, pose numerous ethical challenges around privacy, racial and gender bias, and accuracy. Little guidance is available for designers and developers to create ethical facial recognition systems. We explore solutions to these ethical challenges of creating facial recognition systems in a four-phase design process to create Civil War Twin (CWT), an educational web-based application where users can discover their lookalikes from the American Civil War era (1861-65) while learning more about facial recognition and history. CWT allows users to upload a selfie, select search preferences (e.g., military service, gender, ethnicity), and use facial recognition to discover their "Civil War twins" (i.e., photographs of people from the American Civil War era who look like them). Through this design process, we synthesize industry guidelines, consult with scholars of history, gender, and race, evaluate CWT in feedback sessions with diverse prospective users, and conduct a usability study. We iteratively formulate design goals to incorporate transparency, inclusivity, critical thinking, and empathy into our application. We found that users' perceived learning about the strengths and limitations of facial recognition and Civil War history improved after using CWT, and that our design successfully met users' ethical standards. We also discuss how our ethical design process can be applied to future facial recognition applications.

This project investigates the intersection between reflection through journaling and Artificial Intelligence (AI), more specifically Conversational Agents (CA) in interaction design. Journaling together with a CA is a relatively unexplored area in HCI and Interaction Design, especially when studying the experiential aspect. Furthermore, designing for reflection has been a rising topic within HCI and Interaction Design. The project used a modified version of the double diamond model as a design process to research, create and test a concept for multimodal interactive journaling using a conversational agent. The results suggest that conversational agents have the potential to play an influential and positive role in evoking reflection in an individual through collaborative and conversational interaction. The project provides design recommendations for designing an interactive journaling experience with a conversational agent and an example of how designers can design using interaction, AI, and language as a design material. The project contributes insights to designing artifacts for reflection and how a design process can be designed to design for AI and conversational interfaces.

Computational art is a creative field that refers to a futuristic idea of artificial intelligence. Contrary to the common belief that a machine cannot create art, technological advancements made the rise of a new form of art possible. Artificial intelligence programs can generate various art forms, such as poetry, music, visual art, design and architecture.  The aim of this thesis is to analyse and understand how the emerging community around type-generated art perceives AI in the practice, as well as to assess the main themes of discussion among the community. The study focused on Midjourney (a type-based generative art system) ’s communities on both Facebook and Twitter, two online social media platforms. The methods of netnography and content analysis were applied as a means to study these communities. Netnography helped identify members’ behaviours inside the community as well as the mutual engagement among them. Several discussions were considered in this thesis, where content analysis helped in dividing and analysing the main recurrent categories.  The theoretical framework of communities of practice and actor-network theory is applied in order to understand the findings in this research. Communities of practice refer to a group of people who engage in a practice of collective learning guided by the same interests. Whilst actor-network theory is used to attribute equally agency to humans and nonhumans. Several concepts (the myth of technology and technophobia) emerged throughout the analysis phase, which have been used to support the findings. This research applies the research paradigm of interpretivism, which lead to generalisations.  The conclusions drawn from this study show that the community sees AI as a tool for collaboration and a means for supporting and augmenting the creative process of type-based generative art. Lastly, limitations and further research were discussed in this thesis.

In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and jl{events surrounding} certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the task. We propose a four-step human-AI collaboration workflow to support the jl{discovery} and clustering of these backdrops. Focusing on the painted backdrops of the American Civil War (1861 -- 1865), we present Backdrop Explorer, a content-based image retrieval (CBIR) system incorporating computer vision and novel user interactions. We evaluated Backdrop Explorer on nine users of diverse experience levels and found that all were able to effectively utilize Backdrop Explorer to find photos with similar backdrops. We also document current practices and pain points in Civil War backdrop research through user interviews. Finally, we discuss how our findings and workflow can be applied to other topics and domains. / Master of Science / In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and events surrounding certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the largely manual task. We present Backdrop Explorer, a reverse image search system that helps users discover and subsequently group photos with similar backdrops. We evaluated the system and found that it effectively supported the tasks. We also document current practices and pain points in Civil War backdrop research. Finally, we discuss how our findings and system can be applied to other domains.

The ubiquitous yet opaque logic of machine learning complicates both the design process and end-use. Because of this, much of Interaction Design and HCI now focus on making this logic transparent through human-like explanations and tight control while disregarding other, non-normative human-AI interactions as technical failures. In this thesis I re-frame such interactions as generative for both material exploration and user experience in non-purpose-driven applications. By expanding on the notion of machine learning uncertainty with play, queering, and more-than human design, I try to understand them in a designerly way. This re-framing is followed by a material-centred Research through Design process that concludes with Object Detection Radio: a ludic device that sonifies Tensorflow.js Object Detection API’s prediction probabilities. The design process suggests ways of making machine learning uncertainty explicit in human-AI interaction. In addition, I propose play as an alternative way of relating to and understanding the agency of machine learning technology.

Interactive machine learning (IML) is a promising approach for personalising physiotherapy training. This thesis uses a research- through-design and reflective approach to explore how IML can be ecologically integrated within in-clinic physiotherapy. Domain expert interviews and observations with physiotherapists were conducted to gain a broader understanding of the physiotherapy context, the role of feedback provided to patients, and how technology could be integrated into this context. Three design artefacts were proposed to participants to provoke a discussion on the implications and current practices. Due to time constraints, the findings suggest incorporation within the consultation may be difficult. The clinic’s gym or administration time revealed to be promising alternatives. Furthermore, results highlight the importance of IML supporting richer interaction forms and the implicitness and flexibility needed to describe movement and feedback, which define physiotherapy’s hands-on approach. Togetherness was a reoccurring theme, suggesting that the input and guidance of the IML system could be something done with the patient. Finally, a reflection on the results and the study opens up a discussion of the fitness of IML in physiotherapy.

This paper describes a modification to the mixed-initiative co-creative tool, the Evolutionary Dungeon Designer, and a study performed to evaluate and analyze the co-creative relationship between human designer and AI. The relationship between AI and human designer in creative processes is delicate, as adjusting the autonomy of the AI can negatively affect the user experience. To perfect this type of Human-AI collaboration, further research is needed. In this thesis, different degrees of initiative of the AI are explored, to gain further understanding of mixed-initiative co-creative tools. A user study was performed on the Evolutionary Dungeon Designer, with three varying degrees of AI-initiative. The study highlighted elements of frustration that the human designer experiences when using the tool, and the behaviour in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions, as well as suggested further research.