A Graph Based Departmental Spoken Dialogue System

Spoken dialogue systems are automatic, computer based systems that are a great way for people to receive important information. In this project, I created a spoken dialogue system that people can use to learn about the Computer Science Department at Union College. The system was built by populating an open source dialogue system using a graph based dialogue manager. I improved upon a previous working dialogue system by making the conversations sound more natural, improving the flexibility of the system and making the system more robust. To help with this process a corpus was created using about 200 different dialogues from about 20 people produced by Wizard of Oz Experiments. When the system was complete and implemented it was evaluated with 10 different participants to ensure the system is usable. The evaluation was based on the rates of task completion of people using the system, the number of turns a person has to achieve their goal and a survey given to participants. Based on the evaluation,the main issue that appears is from the speech recognition not working as well as it should. The graph based dialogue manger works well, provided the other components of the whole system works properly

Trust dynamics and verbal assurances in human robot physical collaboration

Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant

A Multidisciplinary Design and Evaluation Framework for Explainable AI Systems

Nowadays, algorithms analyze user data and affect the decision-making process for millions of people on matters like employment, insurance and loan rates, and even criminal justice. However, these algorithms that serve critical roles in many industries have their own biases that can result in discrimination and unfair decision-making. Explainable Artificial Intelligence (XAI) systems can be a solution to predictable and accountable AI by explaining AI decision-making processes for end users and therefore increase user awareness and prevent bias and discrimination. The broad spectrum of research on XAI, including designing interpretable models, explainable user interfaces, and human-subject studies of XAI systems are sought in different disciplines such as machine learning, human-computer interactions (HCI), and visual analytics. The mismatch in objectives for the scholars to define, design, and evaluate the concept of XAI may slow down the overall advances of end-to-end XAI systems. My research aims to converge knowledge behind design and evaluation of XAI systems between multiple disciplines to further support key benefits of algorithmic transparency and interpretability. To this end, I propose a comprehensive design and evaluation framework for XAI systems with step-by-step guidelines to pair different design goals with their evaluation methods for iterative system design cycles in multidisciplinary teams. This dissertation presents a comprehensive XAI design and evaluation framework to provide guidance for different design goals and evaluation approaches in XAI systems. After a thorough review of XAI research in the fields of machine learning, visualization, and HCI, I present a categorization of XAI design goals and evaluation methods and show a mapping between design goals for different XAI user groups and their evaluation methods. From my findings, I present a design and evaluation framework for XAI systems (Objective 1) to address the relation between different system design needs. The framework provides recommendations for different goals and ready-to-use tables of evaluation methods for XAI systems. The importance of this framework is in providing guidance for researchers on different aspects of XAI system design in multidisciplinary team efforts. Then, I demonstrate and validate the proposed framework (Objective 2) through one end-to-end XAI system case study and two examples by analysis of previous XAI systems in terms of our framework. I present two contributions to my XAI design and evaluation framework to improve evaluation methods for XAI system