The United States’ War on Drugs declared in 1971 by President Richard Nixon and revamped by President Reagan in the 1980s has been an objectively failed initiative with origins based in racism and oppression. After exploring the repercussions of this endeavor for societies and individuals around the world, global researchers and policymakers have declared that the policies and institutions created to fight the battle have left devastation in their wake. Despite high economic and social costs, missed opportunities in public health and criminal justice sectors, and increasing limits on our personal freedoms, all the measures taken to eradicate drug abuse and trafficking have been unsuccessful. Not only that, but militarized police tactics, mass incarceration, and harsh penalties that stifle opportunities for rehabilitation, growth, and change disproportionately harm poor and minority communities. <br/>Because reform in U.S. drug policy is badly needed, the goals of America’s longest war need to be reevaluated, implications of the initiative reexamined, and alternative strategies reconsidered. Solutions must be propagated from a diverse spectrum of contributors and holistic understanding through scientific research, empirical evidence, innovation, public health, social wellbeing, and measurable outcomes. But before we can know where we should be headed, we need to appreciate how we got to where we are. This preliminary expository investigation will explore and outline the history of drug use and prohibition in the United States before the War on Drugs was officially declared. Through an examination of the different patterns of substance use, evolving civil tolerance of users, racially-charged anti-drug misinformation/propaganda campaigns, and increasingly restrictive drug control policies, a foundation for developing solutions and strengths-based strategies for drug reform will emerge.

An examination upon the historical evolution of the quarterback reveals that there were three foundational cycles leading up to 2007 which established the model for the mobile quarterback in the NFL. These were especially marked by exceptional quarterbacks breaking molds and pioneering African American quarterbacks overcoming racial stigma. Since 2007, there has been a steady trend of mobile quarterbacks replacing pocket passers, especially among playoff teams. Using k-means clustering, three different categories of quarterbacks were established: pocket passers, scramblers, and dual-threats. After evaluating various player metrics describing quarterback mobility, using yards per game, run-to-pass ratio, scramble rate, and designed run rate on third down produced the best model. This yielded an accurate prediction of covariance and a good overall fit. Teams with dual-threat quarterbacks had more success than other quarterback types on third-and-medium for dropbacks, third-and-long for designed runs, and explosive plays (plays which gain 20+ yards) on designed runs, passes, and quarterback scrambles. An examination into the schematic tendencies using film reveals that mobile quarterbacks allow the offense to have more freedom in its play calling and reduces the margin of error for defenses. Alongside the NFL’s increased focus on the concept of positionless football, this provides the framework for what this thesis calls the “Slashback Offense,” in which the offense utilizes a young, athletic quarterback in multiple positions in conjunction with a mobile starting quarterback. This can enhance option plays, establish the threat of another passer, and reduce the physical burden on the starting quarterback.

Even though criminal justice outcomes frequently receive more media, public, and research attention than civil legal outcomes, civil legal outcomes are equally important in ensuring fairness, accountability, and justice for both individuals and society as a whole. This provides individuals an avenue to pursue justice and restitution for civil wrongs, protects civil rights, and compensates those who have been harmed financially. This study examined the relationship between regional implicit racial bias and racial disparities in outcomes of real-world civil trials. In particular, I explored whether the racial composition of the attorneys on the defense teams or race of the plaintiff predicted plaintiff verdicts and greater damage awards. I hypothesized that all-White defense attorney teams and plaintiffs would win their cases at higher rates and would subsequently be awarded more in damages than their non-White counterparts, especially in regions reporting high levels of implicit racial bias. Using real-world civil trials and Project Implicit Race IAT data, I conducted logistic and linear regression analyses to test the effects of race and regional bias on trial outcomes. The results showed that the likelihood of a pro-plaintiff verdict increased when the defense team included at least one non-White attorney. That is, more racially diverse defense teams won their cases less then all-White defense teams. Additionally, I found that the likelihood of a pro-plaintiff verdict decreased in regions reporting relatively higher levels of regional implicit racial bias. Future research aimed at understanding and reducing disparities and bias in the legal system should be extended to include civil trials and both attorney and client demographics.

The sudden turn to artificial intelligence has been widely supported because of the several proposed positive outcomes of using such technologies to support or replace humans. Automating tedious processes and removing potential human error is exciting for society, but some concerns must be addressed. This essay aims to understand how artificial intelligence can automate domains that likely significantly impact underprivileged and underrepresented groups. This essay will address the potentially devastating effects of algorithmic biases and AI’s contribution to perpetual economic inequality by surveying different domains, such as the justice system and the real estate industry. Without society broadly understanding the potential negative side effects on systems that matter, the rapid growth of artificial intelligence is a recipe for disaster. Everyone must become educated about AI’s current and potential implications before it is too late to stop its damaging effects.