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- Creators: Department of Information Systems
This creative project outlines the steps taken to successfully plan and host a fundraising event at Arizona State University. In my case, this more specifically dealt with organizing a dodgeball tournament between two friendly rivals: police officers and firefighters in the city of Phoenix. All proceeds raised from this fundraising dodgeball tournament were donated back to first responders working in the city of Phoenix.
This project focuses on the effects of partisanship and electoral contestation on the likelihood of state legislators to adopt an independent ethics commission. Existing literature suggests that ethics reform is a function of public perception and the need to assuage public outrage in the face of scandal. Additionally, many legislators view ethics laws as suggestions of their own ineptitude and thus resist reform. However, this existing view fails to consider the unique nature of the enabling legislation of ethics commissions and often conflates external, public drivers of reform with internal drivers personal to the individual legislators. Using logistic regression and time series analysis, this project finds that increased durations of single-party control in state legislatures decreases the chances of that legislature having an independent commission, suggesting that legislators use the partisan ethics committees as political weapons when they are in power. When the dominant party does not face the risk of becoming the minority, there is little in place to motivate ethics reform, thus the lack of commissions. This research identifies the need to develop more focused measures of inter-legislator partisanship and suggests that the effects of different types of ethics laws, specifically those pertaining to ethics commissions, should more often be studied in isolation, rather than as one single category.
This thesis seeks to investigate the use of Artificial Intelligence when reviewing STEM job applications and the human biases that are present in AI system training datasets. Further, it proposes to gender neutralize training dataset terms to evaluate job applications based on merit and qualifications, promoting the inclusivity of women in STEM jobs and seeking to eliminate job application system bias from a Utilitarian perspective.