We study human-chatbot collaborative conversation systems that enable humans to leverage AI chatbot outputs during an online conversation with others. We evaluate response quality in two collaborative systems and compare them with human-only and chatbot-only settings. Both collaborative systems present AI chatbot results as suggestions but encourage the synthesis of human and chatbot responses to different extents. We also examine the influence of chatbot choices, including both retrieval-based and generation-based methods, and the number of suggestions on collaborative systems. Experimental results show that our collaborative systems can significantly improve the efficiency to formulate a response and improve its quality compared with a human-only system while sacrificing the fluency and humanness of the messages. Compared with a chatbot, collaborative systems can provide answers that are more fluent, human-like, and informative. We also found that the retrieval-based chatbots perform better than the generation-based one from all aspects. The optimal number of chatbot suggestions is one, and showing more suggestions has reduced user efficiency. / Master of Science / Artificial Intelligence (AI) systems have become remarkably interactive and accurate with them becoming an integral part of our life. The increasing use of personal assistants like Siri and the application of AI in important real-world tasks such as medical imaging and diagnosis show that AI can perform as good as trained human experts. Organizations today are expanding at a rapid rate and need to service millions of customers concurrently to remain competitive in the market. With the recent success of AI chatbots, the collaboration of Human and AI to augment customer service management is one of the most sought out solutions to this requirement. A service flow where virtual agents and people work together can be a boon to the industry by making the human agents smarter with a bot "whispering" in their ears. We present the design of various collaborative systems we have developed and discuss the improvements in response efficiency and quality due to them in multiple online user experiments. The results of this study can be used to improve conversational chat systems that assist human agents to improve their response time and quality and identify features of the AI agent that are most beneficial for improving the conversation.

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.

This thesis work examines how people accomplish annotation tasks (i.e., labelling data based on content) while working with an artificial intelligence (AI) system. When people and AI systems work together to accomplish a task, this is referred to as human-AI teaming. This study reports on the results of an interview and observation study of 15 volunteers from the Washington DC area as the volunteers annotated Twitter messages (tweets) about the COVID-19 pandemic. During the interviews, researchers observed the volunteers as they annotated tweets, noting any needs, frustrations, or confusion that the volunteers expressed about the task itself or when working with the AI. This research provides the following contributions: 1) an examination of annotation work in a human-AI teaming context; 2) the HATA (human-AI teaming annotation) framework with five key factors that affect the way people annotate while working with AI systems--background, task interpretation, training, fatigue, and the annotation system; 3) a set of questions that will help guide users of the HATA framework as they create or assess their own human-AI annotation teams; 4) design recommendations that will give future researchers, designers, and developers guidance for how to create a better environment for annotators to work with AI; and 5) HATA framework implications when it is put into practice.

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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 today's world, Artificial Intelligence (AI) has seamlessly integrated into ourdaily lives without us even realising it. We witness AI-driven innovations allaround us, subtly enhancing our routines and interactions. Ranging from Siri, Alexa, to Google Assistant, voice assistants have become prime examples of AI technology, assisting us with simple tasks and responding to our inquiries. As these once futuristic ideas have now become an indispensable part of our everyday reality, they also become relevant for the field of GUI. This thesis explores the views of stakeholders, such as designers, alumni, students and teachers, on the inevitable implementation of artificial intelligence(AI) into the graphical user interface (GUI) development. It aims to provide understanding on stakeholders thoughts and needs with the focus on two research questions: RQ1: What are the viewpoints of design stakeholders regarding using Artificial Intelligence tools into GUI development? And RQ2: What characteristics should be considered in including AI in GUI development? To collect data, the thesis will use A/B testing and question sessions. In the A/B testing, participants will watch two videos, one showing how to digitise asketch using an AI tool (Uizard) and the other showing how to do the samething using a traditional GUI design tool (Figma). Afterwards, the participants will answer questions about their experience regarding the two different ways to digitise a sketch. The study highlighted a generally positive outlook among the participating stakeholders. Students and alumni expressed more enthusiasm whereas experienced professionals and teachers were cautious yet open to AI integration. Concerns werevoiced regarding potential drawbacks, including limited control and issues of over-reliance. The findings underscored AI's potential to streamline tasks but also emphasised the need for manual intervention and raised questions about maintaining control and creative freedom. We hope this work serves as a valuable starting point for other researchers interested in exploring this topic.

With artificial intelligence (AI) becoming ubiquitous in a broad range of application domains, the opacity of deep learning models remains an obstacle to adaptation within safety-critical systems. Explainable AI (XAI) aims to build trust in AI systems by revealing important inner mechanisms of what has been treated as a black box by human users. This thesis specifically aims to improve the transparency and trustworthiness of deep learning algorithms by combining attribution methods with image segmentation methods. This thesis has the potential to improve the trust and acceptance of AI systems, leading to more responsible and ethical AI applications. An exploratory algorithm called ESAX is introduced and shows how performance greater than other top attribution methods on PIC testing can be achieved in some cases. These results lay a foundation for future work in segmentation attribution.

Graphic design has throughout history been affected by disruptive technologies, every technical advancement within the industry has led to a need for adapting in order to stay relevant. The biggest disruptor within the industry today is without doubt artificial intelligence. AI has already changed the workflow and design processes, and it is evident that AI is here to stay. This emerging technology has the potential to further revolutionise the creative process by providing designers with new tools and techniques to enhance efficiency and improve quality of their outputs. However, many uncertainties and concerns persist regarding how AI will impact the design profession and the space for human creative abilities. This essay aims to contribute to the ongoing discourse surrounding the role of AI in the creative industry through a thematic literature review. The findings of this study disproves the notion that AI poses a threat to creativity and emphasises the perpetual need for human expertise in a design process. This research underscores the value of human creativity and highlights how AI can complement and empower designers rather than replace them.

Identifying individuals in historical photographs is important for preserving material culture, correcting historical records, and adding economic value. Historians, antiques dealers, and collectors often rely on manual, time-consuming approaches. While Artificial Intelligence (AI) offers potential solutions, it's not widely adopted due to a lack of specialized tools and inherent inaccuracies and biases. In my dissertation, I address this gap by combining the complementary strengths of human intelligence and AI. I introduce Photo Sleuth, a novel person identification pipeline that combines crowdsourced expertise with facial recognition, supporting users in identifying unknown portraits from the American Civil War era (1861--65). Despite successfully identifying numerous unknown photos, users often face the `last-mile problem' --- selecting the correct match(es) from a shortlist of high-confidence facial recognition candidates while avoiding false positives. To assist experts, I developed Second Opinion, an online tool that employs a novel crowdsourcing workflow, inspired by cognitive psychology, effectively filtering out up to 75% of facial recognition's false positives. Yet, as AI models continually evolve, changes in the underlying model can potentially impact user experience in such crowd--expert--AI workflows. I conducted an online study to understand user perceptions of changes in facial recognition models, especially in the context of historical person identification. Our findings showed that while human-AI collaborations were effective in identifying photos, they also introduced false positives. To reduce these misidentifications, I built Photo Steward, an information stewardship architecture that employs a deliberative workflow for validating historical photo identifications. Building on this foundation, I introduced DoubleCheck, a quality assessment framework that combines community stewardship and comprehensive provenance information, for helping users accurately assess photo identification quality. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that foster accurate decision-making in the context of historical photo identification. / Doctor of Philosophy / Identifying historical photos offers significant cultural and economic value; however, the identification process can be complex and challenging due to factors like poor source material and limited research resources. In my dissertation, I address this problem by leveraging the complementary strengths of human intelligence and Artificial Intelligence (AI). I built Photo Sleuth, an online platform, that helps users in identifying unknown portraits from the American Civil War era. This platform employs a novel person identification workflow that combines crowdsourced human expertise and facial recognition. While AI-based facial recognition is effective at quickly scanning thousands of photos, it can sometimes present challenges. Specifically, it provides the human expert with a shortlist of highly similar-looking candidates from which the expert must discern the correct matches; I call this as the `last-mile problem' of person identification. To assist experts in navigating this challenge, I developed Second Opinion, a tool that employs a novel crowdsourcing workflow inspired by cognitive psychology, named seed-gather-analyze. Further, I conducted an online study to understand the influence of changes in the underlying facial recognition models on the downstream person identification tasks. While these tools enabled numerous successful identifications, they also occasionally led to misidentifications. To address this issue, I introduced Photo Steward, an information stewardship architecture that encourages deliberative decision-making while identifying photos. Building upon the principles of information stewardship and provenance, I then developed DoubleCheck, a quality assessment framework that presents pertinent information, aiding users in accurately evaluating the quality of historical photo IDs. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that encourage accurate decision-making in the context of historical photo identification.