Abstract: The past 25 years have seen the most radical expansion of minority representation in the Federal legislature in American history. Despite these advancements, questions persist about the nature of political minority representation, especially in minority-majority districts (MMDs) — a district in which a racial minority group or groups comprise a majority of the district’s total population.

I argue that since the duty of a representative is to the district they serve and not the larger political structure, MBD representatives are stronger advocates for descriptive issues compared to their peers, providing both descriptive and substantive representation for Black constituents and accomplishing the goal of their appointment and the creation of the district. Assuch, any “trade-offs” that exist by creating majority-Black districts do not outweigh the benefits of this representation. From this theory, I expect this level of representation to positively impact the level of political participation and satisfaction within these districts. Comprehensive analysis of congressional behavior and constituent participation.

In this paper, I will review relevant literature about descriptive and substantive representation within MBDs. However, this literature does not directly evaluate how MBD representatives legislate. This paper quantifies MBD representatives’ legislative behavior against the remaining members of the US House of Representatives by reviewing their voting patterns surrounding Black Issues.

Abstract: This study aims to discern patterns, trends, and potential biases associated with the geographical proximity of police officers to the communities they police. A comprehensive exploration of demographic, socioeconomic, and policing variables contributes to a nuanced understanding of the factors influencing fatal police shootings. Furthermore, the study seeks to identify any disparities in incident rates based on officers’ residency status, considering variables such as race, community demographics, and departmental policies.

The insights derived from this case study bear substantial implications for informing public policy, refining police training protocols, and strengthening community relations. By unraveling the nuanced dynamics surrounding police residence and fatal police shootings, this case study aims to provide evidence-based recommendations to enhance transparency, accountability, and trust between law enforcement agencies and the communities they serve. In doing so, it contributes to the broader discourse on police reform, fostering a data-driven approach to address critical issues and promote safer, more resilient communities.

Abstract: In the face of escalating digital surveillance technologies, the right to assemble freely and peacefully is increasingly under threat. This paper proposes the development of “Watch the Watchers,” a crowdsourced platform designed to enhance public awareness of surveillance in major American cities. Inspired by Amnesty International’s Decoders model, the platform employs a systematic approach to identify and categorize surveillance cameras, integrating an exposure-based navigation system. The key objectives include accurately tagging cameras, ensuring user privacy by securing internal communication through end-to-end encryption, and optimizing navigation based on surveillance camera exposure. We were inspired by initiatives like Decode Surveillance NYC. Much like the quest to map CCTV cameras in New York City, our method empowers individuals to contribute vital data to a collective effort aimed at preserving civil liberties. We delve into the implications of surveillance on the right to assemble. Our research takes a multidimensional approach, introducing a novel visual model that goes beyond traditional analyses and develops a tool for non-academics to use. 

Abstract:We conducted an experiment testing the capability of natural language programs (NLPs) to generate images for social media-focused news content. We then measured how well humans classify images in simulated interaction. Our null hypotheses were 1) that humans would be able to distinguish AI-generated from real images with 70% accuracy, and 2) that adding an education step to the beginning of the survey would significantly improve results.
Results Summary: We found that artificial intelligence (AI) can generate images that pass for human-captured 45% of the time (false negative rate). We also found when faced with a human-
captured image, people will incorrectly identify it as AI 41% of the time (false positive rate). We found differences in classification ability across age, but no significant difference when providing classification tips to users before they began the survey.

Abstract: American public education is at a critical point. The Covid-19 pandemic put education into a gridlock as educators scrambled to fashion together an unprecedented and inadequate teaching system. In the years following this shutdown, the public school system has seen an exodus of schoolchildren as parents begin to relinquish their education to other schooling methods. This standstill has forced the decision-makers in schooling, parents, educators, and legislators alike, to interrogate why K12 American public schooling has been so inadequate. How is it that public schools have failed to meet the bar for decades, producing globally subpar achievement and failing to close gaps in equity and achievement domestically? Billions have been spent trying to “fix” the equity issue, and bureaucrats have tried to legislate the achievement gap out of schools to a moot point. Nothing has proved successful because even the most well-funded per-pupil schools will not produce excellence when under-resourced. In an attempt to bring more and more 21st-century resources into classrooms, schools have been neglecting a student’s most important tool for learning, the teachers.