HUMAN-CENTERED AI DEVELOPMENT FOR AUGMENTED COGNITION
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2024
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Abstract
This dissertation examines the integration of artificial intelligence (AI) into daily life, emphasizing human-centered AI development for augmented cognition (AC), which focuses on enhancing rather than replacing human capabilities. It explores how individuals perceive and interact with AI systems compared to how they interact with other humans. In addition, it studies how AI systems can interpret and adapt to humans’ cognitive states.During the development of AI systems, the focus has been on creating fully autonomous systems, completely replacing manual human labor. However, there are tasks where humans outperform AI systems, and there are also tasks where human-AI systems (HAIS) can outperform both traditional human labor and autonomous AI systems. Rapid replacement of humans with AI systems raises security and privacy concerns and increases operational complexity. In addition, AI models have limited personalization, marginalizing user groups from diverse languages and cultures.
This research examines designing AI systems to complement human capabilities: how they could detect, adapt to, and personalize responses based on a human’s cognitive state, encompassing intentions, languages, and cultural backgrounds. The following studies identify user intent through biometric markers (e.g., mouse or pen pressure), detect native and non-native users, assess the impact of linguistic and cultural diversity on user preferences and performance, and evaluate Large Language Models (LLMs) as tools for human enhancement.
This dissertation underscores the importance of designing AI systems with consideration and adaptability to users’ diverse backgrounds, ensuring inclusivity and personalization and that they are being delivered to people in a way that enhances their capabilities.
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Computer science, Artificial intelligence, Augmented Cognition, Code Comprehension, Deep Learning, Human Computer Interaction, Human-centered AI, Large Language Models
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165 pages
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