Indiana University-Purdue University Indianapolis (IUPUI) / The rapid infusion of artificial intelligence into everyday technologies means that consumers are likely to interact with intelligent systems that provide suggestions and recommendations on a daily basis in the very near future. While these technologies promise much, current issues in low transparency create high potential to confuse end-users, limiting the market viability of these technologies. While efforts are underway to make machine learning models more transparent, HCI currently lacks an understanding of how these model-generated explanations should best translate into the practicalities of system design. To address this gap, my research took a pragmatic approach to improving system transparency for end-users. Through a series of three studies, I investigated the need and value of transparency to end-users, and explored methods to improve system designs to accomplish greater transparency in intelligent systems offering recommendations. My research resulted in a summarized taxonomy that outlines a variety of motivations for why users ask questions of intelligent systems; useful for considering the type and category of information users might appreciate when interacting with AI-based recommendations. I also developed a categorization of explanation types, known as explanation vectors, that is organized into groups that correspond to user knowledge goals. Explanation vectors provide system designers options for delivering explanations of system processes beyond those of basic explainability. I developed a detailed user typology, which is a four-factor categorization of the predominant attitudes and opinion schemes of everyday users interacting with AI-based recommendations; useful to understand the range of user sentiment towards AI-based recommender features, and possibly useful for tailoring interface design by user type. Lastly, I developed and tested an evaluation method known as the System Transparency Evaluation Method (STEv), which allows for real-world systems and prototypes to be evaluated and improved through a low-cost query method. Results from this dissertation offer concrete direction to interaction designers as to how these results might manifest in the design of interfaces that are more transparent to end users. These studies provide a framework and methodology that is complementary to existing HCI evaluation methods, and lay the groundwork upon which other research into improving system transparency might build.

Heutige Roboter sind nur begrenzt in der Lage etwas zu erlernen, sich unerwarteten Umständen anzupassen oder auf diese zu reagieren. Als Antwort auf diese Fragen, develomental robotics setzt sich den Aufbau eines künstlichen Systems zum Ziel, das motorische und kognitive Fähigkeiten analog zur menschlichen Entwicklung durch Interaktion mit der Umgebung entwickeln kann. In dieser Arbeit wird ein ähnlich Ansatz verwendet, mit Hilfe dessen grundlegende Verhaltenskomponenten identifiziert werden sollen, die eine autonome Aneignung motorischer und kognitive Fähigkeiten durch die Roboter ermöglichen könnten. Diese Arbeit untersucht die sensomotorische Interaktion als Mittel zur Schaffung von Erfahrungen. Es werden Experimente zu explorative Verhaltensweisen zur Aneigung von Arbewegungen, der Werkzeugnutzung und von interaktiven Fähigkeiten vorgestellt. In diesem Rahmen wird auch die Entwicklung sozialer Fähigkeiten, insbesondere durch joint attention, behandelt. Dabei werden zwei Vorraussetzugen zu joint attention untersucht: Zeigegesten und Erkennung von visueller Salienz. Dabei wurde das Framework der interen Modelle für die Darstellung von sensomotorischen Erfahrungen angewendet. Insbesondere wurden inverse und Vorwärtsmodelle mit unterschiedlichen Konfigurationen am sensorischen und motorischen Daten, die vom Roboter durch exploratives Verhalten, durch Beobachtung menschliche Vorführern, oder durch kinästhetisches Lehren erzeugt wurden geschult. Die Entscheidung zu Gunsten dieses Framework wurde getroffen, da es in der Lage ist, sensomotorische Zyklen zu simulieren. Diese Arbeit untersucht, wie grundlegende kognitive Fähigkeiten in einen humanoiden Roboter unter Berücksichtigung sensorischer und motorischer Erfahrungen implementiert werden können. Insbesondere wurden interne Simulationsprozesse für die Implementierung von Kognitivenfahigkeiten wie die Aktionsauswahl, die Werkzeugnutzung, die Verhaltenserkennung und die Self-Other distinction, eingesetzt. / State-of-the-art robots are still not properly able to learn from, adapt to, react to unexpected circumstances, and to autonomously and safely operate in uncertain environments. Researchers in developmental robotics address these issues by building artificial systems capable of acquiring motor and cognitive capabilities by interacting with their environment, inspired by human development. This thesis adopts a similar approach in finding some of those basic behavioural components that may allow for the autonomous development of sensorimotor and social skills in robots. Here, sensorimotor interactions are investigated as a mean for the acquisition of experience. Experiments on exploration behaviours for the acquisition of arm movements, tool-use and interactive capabilities are presented. The development of social skills is also addressed, in particular of joint attention, the capability to share the focus of attention between individuals. Two prerequisites of joint attention are investigated: imperative pointing gestures and visual saliency detection. The established framework of the internal models is adopted for coding sensorimotor experience in robots. In particular, inverse and forward models are trained with different configurations of low-level sensory and motor data generated by the robot through exploration behaviours, or observed by human demonstrator, or acquired through kinaesthetic teaching. The internal models framework allows the generation of simulations of sensorimotor cycles. This thesis investigates also how basic cognitive skills can be implemented in a humanoid robot by allowing it to recreate the perceptual and motor experience gathered in past interactions with the external world. In particular, simulation processes are used as a basis for implementing cognitive skills such as action selection, tool-use, behaviour recognition and self-other distinction.