Philosophy of AI: At the confluence of artificial intelligence, we critically examine political philosophy and the philosophy of science. By doing so, we engage deeply with the broader societal, ethical, and foundational scientific implications of AI technologies.
Ethical AI Design and Social Responsibility: Ensuring fairness within algorithmic designs remains a cornerstone of our research. By identifying and understanding biases, especially those inherent in datasets like underrepresentation, our primary objective is to design AI systems that are not just advanced but also ethically responsible and just.
Modeling Social Phenomena with AI: Harnessing the power of methodologies like reinforcement learning and algorithmic game theory, we intricately model social behaviors and dynamics. Such models enable us to unravel the mysteries behind decision-making under adversity, understand the formation of information cascades, and appreciate the immense influence of individual attributes like 'grit' in countering societal pessimism.
Proxy Attributes in AI: Proxies, especially in artificial intelligence, hold significant importance. Our team delves into the realm of latent variables using proximal causal inference, exploring latent treatments such as race as a motivational element. Concurrently, we also engage with mechanisms for filtering proxy attributes, leveraging innovative techniques to understand and interact with these proxies.
Theoretical Foundations in AI: We maintain a deep interest in foundational topics like learning theory and causal inference, striving to bridge the gap between theory and practical application. This allows us to remain at the forefront of AI research, ensuring our work is both cutting-edge and deeply rooted in strong theoretical principles