This thesis explores the impact of artificial intelligence (AI) interventions on player decision-making in an investment-themed serious game, The Strategists. This Monopoly®-inspired game incorporates rule-based advice, machine learning-based predictions, and data-driven visualizations to create a unique platform for studying user interactions with AI-assisted decision support systems. The research investigates how players perceive and utilize AI-driven features in a game environment and how these interventions influence gameplay outcomes. Through a series of user studies involving 37 participants across 29 game sessions, the study compares player behavior and performance between control groups with and without access to AI features. Key findings reveal that players who followed AI advice more frequently were likelier to win the game. However, the study also uncovered an interesting paradox: players with more gaming experience tend to distrust and undervalue AI assistance despite evidence of its effectiveness. The findings show gender differences in gameplay outcomes, with female players more likely to win than male players. Players who finished last viewed AI advice more frequently but were less likely to follow it, suggesting a complex relationship between advice visibility and following. This research adds to human-computer interaction and game studies by providing insights into AI-driven decision support systems design in gaming contexts. It also addresses the need for interdisciplinary approaches in game research by combining elements from game design, artificial intelligence, data visualization, and financial education. The study highlights the significance of understanding user perceptions and trust in AI systems.

Artificial Intelligence (AI) technology has advanced significantly, enabling AI models to learn from diverse data and automate tasks previously performed solely by humans. This capability has excited people from various domains outside the AI research community and has driven technical experts from non-AI backgrounds to leverage AI for their domain-specific tasks. However, when these experts attempt to use AI, they face several challenges: understanding AI models' behavior and results in intuitive ways, adapting pre-trained models to their own datasets, and finding comprehensive guidelines for AI integration practices. This dissertation takes a user-centered approach to address these challenges by designing and developing interactive systems and frameworks that make AI more interpretable and accessible. The dissertation focuses on three key areas: Explaining AI Behavior: For domain experts from a non-AI background, understanding AI models is challenging. Automated explanations alone often fall short, as users need an iterative approach to form, test, and refine hypotheses. We introduce two visual analytics systems, ConceptExplainer and ASAP, designed to provide intuitive explanations and analysis tools, helping users better comprehend and interpret AI's inner workings and outcomes. Simplifying AI Workflows: Adapting pre-trained AI models for specific downstream tasks can be challenging for users with limited AI expertise. We present InFiConD, an interactive no-code interface for streamlining the knowledge distillation process, allowing users to easily adapt large models to their specific needs. Integrating AI in Domain-Specific Tasks: The integration of AI into domain-specific visual analytics systems is growing, but clear guidance is often lacking. Users with non-AI backgrounds face challenges in selecting appropriate AI tools and avoiding common pitfalls due to the vast array of available options. Our survey, AI4DomainVA, addresses this gap by reviewing existing practices and developing a roadmap for AI integration. This guide helps domain experts understand the synergy between AI and visual analytics, choose suitable AI methods, and effectively incorporate them into their workflows.

Machine Learning and Artificial Intelligence algorithms are deeply embedded in our everyday experiences, from the moment we wake up to tracking our REM sleep through wearable devices. These technologies are not only applied to a wide array of challenges but have also become ubiquitous, with many people interacting and relying on AI decisions - often without fully understanding how these models work.Despite the widespread use of AI, the reasoning behind its decisions is frequently opaque. This lack of transparency can lead to users either underestimating or overestimating the capabilities of AI systems. Even in scenarios where AI models provide explanations for their decisions, the impact of these justifications on end-users' perceptions remains unclear. These issues raise important questions about ways to improve model transparency, methods to aid in decision making and understanding the impact of such explanations on user's trust. To address these issues, this thesis focuses on: 1. Explainability of Reinforcement Learning for non expert users: From 2019 to 2023, HCI saw 154 Explainable ML papers in many HCI conferences like IEEEVis, EuroVis and CHI. Comparatively, field of explainable RL (XRL) has however been underdeveloped. I contributed two novel visualization-driven systems for explainable RL: PolicyExplainer that provides a visual explanations and PolicySummarizer that provides policy summaries for novice users. 2. Accessibility to perform downstream decision making tasks: As AI becomes more accessible, users may struggle to leverage these tools within their limits. I studied how to design effective interfaces for identifying underlying coverage diversity among different news organizations, operationalized as NewsKaleidoscope that helps users identify coverage biases in news and PromptAid to offer AI-based prompt recommendations for better task performance. 3. User perceptions and impact of machine generated rationales: Finally, explanations can be a double-edged sword, potentially increasing trust in flawed models. I explored how user perceptions are affected by rationales generated by ChatGPT, especially when it justifies incorrect predictions due to hallucinations and ways to circumvent the issue.

Data visualization is essential for communicating complex information to diverse audiences. However, a gap persists between visualization design objectives and the understanding of non-expert users, with limited experience. This dissertation addresses challenges in designing for non-experts, referred to as the D.U.C.K. bridge: (i) user unfamiliarity with DATA analysis domains, (ii) variation in user UNDERSTANDING mechanisms, (iii) catering to individual differences in CREATING visualizations, and (iv) promoting KNOWLEDGE synthesis and application. By developing human-driven principles and tools, this research aims to enhance visualization creation and consumption by non-experts. Leveraging linked interactive visualizations, this dissertation explores the iterative education of non-experts when navigating unfamiliar DATA realms. VAIDA guides crowd workers in creating better NLP benchmarks through real-time visual feedback. Similarly, LeaderVis allows users to interactively customize AI leaderboards and select model configurations suited to their application. Both systems demonstrate how visual analytics can flatten the learning curve associated with complex data and technologies. Next, this dissertation examines how individuals internalize real-world visualizations—either as images or information. Experimental studies investigate the impact of design elements on perception across visualization types and styles, and an LSTM model predicts the framing of the recall process. The findings reveal mechanisms that shape the UNDERSTANDING of visualizations, enabling the design of tailored approaches to improve recall and comprehension among non-experts. This research also investigates how known design principles apply to CREATING visualizations for underrepresented populations. Findings reveal that multilingual individuals prefer varying text volumes based on annotation language, and older age groups engage more emotionally with affective visualizations than younger age groups. Additionally, underlying cognitive processes, like mind wandering, affect recall focus. These insights guide the development of more inclusive visualization solutions for diverse user demographics. This dissertation concludes by presenting projects aimed at preserving cognitive and affective KNOWLEDGE synthesized through visual analysis. The first project examines the impact of data visualizations in VR on personal viewpoints about climate change, offering insights for using VR in public scientific education. The second project introduces LINGO, which enables the creation of diverse natural language prompts for generative models across multiple languages, potentially facilitating custom visualization creation via streamlined prompting.

Geospatial machine learning (ML) models and their applications have recently gained significant attention due to the rising availability of raster and spatiotemporal datasets. Three important limitations in ML for the geospatial domain are the following. Firstly, real-world geospatial datasets are often too large, and many geospatial ML algorithms represent the geographical region in terms of a grid. If the granularity of the grid is too fine, it results in a large number of grid cells, leading to long training time and high memory consumption issues during the model training. Secondly, current machine learning systems are mainly designed for text, image, audio, and video data, and they often fall short of adequately supporting geospatial datasets. This is because machine learning and data preprocessing techniques in this domain fail to capture the spatial autocorrelation property, a key characteristic available in geospatial datasets. Thirdly, many real-world inference workflows in this domain involve preprocessing steps that join data from multiple data silos to assemble feature vectors. Often, these geospatial joins are expensive and become bottlenecks in the inference process. In this dissertation, I will discuss novel solutions to these three major concerns of spatiotemporal machine learning. In particular, the dissertation includes three main research components. The first one presents a machine learning-aware technique for re-partitioning geospatial data to shorten the training duration of spatial machine learning models; the second component introduces an end-to-end framework for deep learning and data preprocessing with spatiotemporal vector and raster dataset; the third solution presents a strategy to co-optimize a preprocessing and inference pipeline consisting of costly join queries and model inferencing. Additionally, I will present experimental evaluation results using a variety of real-world datasets to demonstrate the effectiveness of all three solutions.

The widespread usage of technology has led to an increase in cyber threats. Organizations use indices to measure, understand, and make decisions in response to cybersecurity threats. However, the same tools do not exist to help individuals to make informed cybersecurity decisions. This work aims to understand the impact of cyber threats on individuals and take steps toward developing a composite indicator that engages them in conversations around cybersecurity. A composite indicator consolidates single indicators around a complex topic, such as cybersecurity, into one, thereby providing a means for measuring a non-trivial topic. A tool such as a composite indicator will help individuals make better cybersecurity policy decisions and enable researchers to benchmark cybersecurity consequences for the general public. However, more data and information are needed to create such a tool.To this end, this work presents semi-structured interviews with people about their exposure to cyber threats and documents some of the challenges and harms of a cyber-related incident. Based on interviews and a literature survey, this work proposes a Cyber Harm Framework for Citizens that reflects the dimensions of harm experienced by users. This framework provides a conceptual starting point for building a composite indicator. In order to develop a human-centered cyber indicator, this work explores the potential social, ethical, and design challenges that must be considered. Future work will focus on integrating the framework into a cyber-harm composite indicator, enabling individuals to make informed cybersecurity decisions.

In today’s world, artificial intelligence (AI) is increasingly becoming a part of our daily lives. For this integration to be successful, it’s essential that AI systems can effectively interact with humans. This means making the AI system’s behavior more understandable to users and allowing users to customize the system’s behavior to match their preferences. However, there are significant challenges associated with achieving this goal. One major challenge is that modern AI systems, which have shown great success, often make decisions based on learned representations. These representations, often acquired through deep learning techniques, are typically inscrutable to the users inhibiting explainability and customizability of the system. Additionally, since each user may have unique preferences and expertise, the interaction process must be tailored to each individual. This thesis addresses these challenges that arise in human-AI interaction scenarios, especially in cases where the AI system is tasked with solving sequential decision-making problems. This is achieved by introducing a framework that uses a symbolic interface to facilitate communication between humans and AI agents. This shared vocabulary acts as a bridge, enabling the AI agent to provide explanations in terms that are easy for humans to understand and allowing users to express their preferences using this common language. To address the need for personalization, the framework provides mechanisms that allow users to expand this shared vocabulary, enabling them to express their unique preferences effectively. Moreover, the AI systems are designed to take into account the user’s background knowledge when generating explanations tailored to their specific needs.

This research project seeks to develop an innovative data visualization tool tailored for beginners to enhance their ability to interpret and present data effectively. Central to the approach is creating an intuitive, user-friendly interface that simplifies the data visualization process, making it accessible even to those with no prior background in the field. The tool will introduce users to standard visualization formats and expose them to various alternative chart types, fostering a deeper understanding and broader skill set in data representation. I plan to leverage innovative visualization techniques to ensure the tool is compelling and engaging. An essential aspect of my research will involve conducting comprehensive user studies and surveys to assess the tool's impact on enhancing data visualization competencies among the target audience. Through this, I aim to gather valuable insights into the tool's usability and effectiveness, enabling further refinements. The outcome of this project is a powerful and versatile tool that will be an invaluable asset for students, researchers, and professionals who regularly engage with data. By democratizing data visualization skills, I envisage empowering a broader audience to comprehend and creatively present complex data in a more meaningful and impactful manner.

In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.

Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. However, existing design tools for mid-air haptics primarily support the creation of static tactile sensations (tactons), which lack adaptability at runtime. These tactons do not offer the required expressiveness in interactive scenarios where a continuous closed-loop response to user movement or environmental states is desirable. This thesis proposes AdapTics, a toolkit featuring a graphical interface for the rapid prototyping of adaptive tactons—dynamic sensations that can adjust at runtime based on user interactions, environmental changes, or other inputs. A software library and a Unity package accompany the graphical interface to enable integration of adaptive tactons in existing applications. The design space provided by AdapTics for creating adaptive mid-air ultrasound tactons is presented, along with evidence that the design tool enhances Creativity Support Index ratings for Exploration and Expressiveness, as demonstrated in a user study involving 12 XR and haptic designers.