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India is facing an air pollution crisis. It houses 11 of the 15 of the world’s most polluted cities (World Air Quality Report, 2021). This has high costs. Air pollution accounted for 1.6 million premature deaths and a loss of 0.3-0.9 percent of GDP in a single year. The failure of executive and legislative policies has pushed citizens to approach India’s courts for solutions. This project studies the overall impact of judicial policies on air quality. We take a novel approach to estimate the impact of environmental litigation on environmental as well as human capital outcomes. We begin by constructing a unique database of all cases that pertain to air pollution that have been heard in the higher judiciary of India for the past 30 years. This unique data set consists of approximately 7,500 court orders. For 2,500 cases that directly cited the Air Act, we rely on manual reading, interpretation and categorization by a team of law students. For these cases, we are also using ChatGPT to determine whether a particular judgment is likely to have a positive impact on the environment. In addition to the environmental impact of cases, we combine data on judgements with additional data on the characteristics of judges as well as air pollution levels. In the coming months, we will work to compare the analysis of the court cases by ChatGPT with that of the human coders. We will identify similarities and discrepancies. With this in hand, we will examine the impact of “pro-green” cases on actual environmental outcomes. Given the significance of these cases in Indian law and society, we seek to prepare a comprehensive database that can be made available to other researchers in this area. Given that there are 84 million court cases that are now electronically available from the court system of India, our methodology presents a unique opportunity to leverage new tools of AI to understand how Indian courts dispense justice and how the real-life impacts of these decisions can be studied by researchers.

A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.

The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.