Developing Design Features to Facilitate AI-Assisted User Interactions;Job Displacement Due to the Implication of AI in the Workplace

In our ever-evolving society, digital and computational technologies provide the backbone for our way of life and spur innovation. One such innovation that has come from this backbone is artificial intelligence. Artificial Intelligence (AI) has been present in society prior to the creation of LLMs like Chat GPT, but AI models such as GitHub Copilot and Chat GPT have caused the technology to become mainstream in society and allows users such as us to have direct interaction with these AI models to answer a wide array of questions that we may have. In the near future, these AI models will disrupt the workplace as they have for the educational system. This prospectus dives into the STS concerns with AI induced job displacement as well as the creation of an interface used for an AI software used in the Business Intelligence industry via my Capstone Project.
The core of our Capstone project revolves around the pressing issue of data analytics in business intelligence. Our mission is to streamline and revolutionize this field through the seamless integration of user-friendly AI. In the digital age, businesses are in a constant race to achieve streamlined operations and harness data-driven insights to maintain their competitive edge. The challenge we tackle head-on is the complexity and the steep learning curve associated with existing data analytics platforms. Specifically, we're collaborating with cloud-based machine data analytics company, to address the challenge of enhancing data analytics through AI.
In evaluating the satisfaction with the search category refinement feature, the results highlighted varying preferences between novice and expert users, with both groups favoring the AI-suggested dropdown over other options. Novice users struggled with the federated search buttons, confusing them for query filters, which increased their cognitive load, whereas expert users questioned the federated search's ability to effectively display categories, citing potential information overload. Conversely, the AI-suggested dropdown was well-received for its ability to narrow down search categories effectively, though experts preferred typing directly into the search bar, which allows for wildcard entries. The mega menu confused novices with its complex hierarchy, and while not fitting well within expert users' mental models, it was seen as potentially more novice friendly. The chatbot feature posed usability challenges for novices, particularly in its visibility and interaction design, suggesting a need for more intuitive design elements to prevent user errors. Overall, expert users expressed a preference for using UI elements for common functions to enhance system efficiency and align with user expectations. These insights are crucial for the B2B data analytics field, as they underscore the importance of balancing user customization with AI-integrated assistance to optimize the querying process and ensure efficient user navigation through AI-enhanced systems.
In my thesis, I explore the complex interplay between the adoption of Artificial Intelligence (AI) in the workplace and its multifaceted impacts on job displacement and organizational decision-making. This investigation is motivated by the rapid advancement of AI technologies, such as Generative Pre-trained Transformers (GPT), which are transforming job roles, decision-making processes, and organizational structures across various sectors. Drawing from Science and Technology Studies (STS), my research situates AI adoption within broader socio-cultural and power dynamics to understand how AI reshapes work environments, emphasizing the importance of ethically integrating AI to ensure equitable and sustainable workplace transformations. Methodologically, I employ a comprehensive literature review and Actor-network theory (ANT) to dissect the socio-technical dynamics at play, allowing for a nuanced analysis of how AI influences human actors and organizational systems, thereby highlighting both the opportunities and challenges of AI in modern workplaces. Through this approach, my research aims to contribute actionable insights into the strategic integration of AI technologies, fostering a future where AI enhances rather than diminishes workplace inclusivity, equity, and sustainability.
My literature review delves deeply into the integration of Artificial Intelligence (AI) within Business Intelligence (BI) systems, marking significant advancements from basic data reporting to sophisticated, predictive analytics that reshape strategic business decisions. Studies like Bulusu & Abellera (2021) and McKinsey (2020) illustrate how AI enhances operational efficiencies and alters market dynamics by enabling unprecedented data insight. The review also addresses the substantial challenges of AI integration, such as technical barriers and the need for significant infrastructural and workforce investments, as highlighted by Suguna, Dhivya, and Paiva (2021). Furthermore, it discusses the broader implications of AI on employment, organizational roles, and the ethical dimensions of AI deployment in various sectors, underscoring the dual nature of AI’s impact: it both displaces jobs and creates new opportunities, necessitating strategic reskilling and ethical consideration. The conclusion of the thesis reiterates the transformative potential of AI in redefining business practices and decision-making processes. It stresses the importance of navigating AI integration with a focus on ethical standards and human values to ensure that AI's deployment supports a more inclusive, equitable, and sustainable future in the workplace. This synthesis underscores a call for comprehensive strategies that balance technological advancements with socio-technical and ethical considerations, ensuring that AI contributes positively to the evolving dynamics of the modern workplace.

School of Engineering and Applied Science

Bachelor of Science in Systems Engineering

Technical Advisor: Gregory Gerling

STS Advisor: Pedro Francisco

Technical Team Members: Stacy Meng, Anika Sharma, Anmol Kaur, Ghislain Ventre, Rebecca Dollahite