The era of mass data collection is upon us and only recently have people begun to consider the value of their data. All of our clicks and likes have helped big tech companies build predictive models to tailor their product to the buying patterns of the consumer. Big data collection has its advantages in increasing profitability and efficiency, but many are concerned about the lack of transparency in these technologies (Dwyer). The dependency on algorithms to make and influence decisions has become a growing concern in law enforcement. The use of this technology is commonly referred to as data-driven decision making, which is also known as predictive policing. These technologies are thought to reduce the biases held in traditional policing by creating statistically sound evidence-based models. Although, many lawsuits have highlighted the fact that predictive technologies do more to reflect historical bias rather than to eradicate it. The clandestine measures behind the algorithms may be in conflict with the due process clause and the penumbra of privacy rights enumerated in the First, Third, Fourth, and Fifth Amendments. <br/> Predictive policing technology has come under fire for over policing historically black and latinx neighborhoods. GIS (Geographical Information Systems) is supposed to help officers identify where crime will likely happen over the next twelve hours. However, the LAPD’s own internal audit of their program concluded that the technology did not help officers solve crimes or reduce crime rate any better than traditional patrol methods (Puente). Similarly, other types of tools used to calculate recidivism risk for bond sentencing are disproportionately biased to calculate black people as having a higher risk to reoffend (Angwin). Lawsuits from civil liberties groups have been filed against the police departments that utilized these technologies. This paper will examine the constitutional pitfalls of predictive technology and propose ways that the system could work to ameliorate its practices.

Music streaming services have affected the music industry from both a financial and legal standpoint. Their current business model affects stakeholders such as artists, users, and investors. These services have been scrutinized recently for their imperfect royalty distribution model. Covid-19 has made these discussions even more relevant as touring income has come to a halt for musicians and the live entertainment industry. <br/>Under the current per-stream model, it is becoming exceedingly hard for artists to make a living off of streams. This forces artists to tour heavily as well as cut corners to create what is essentially “disposable art”. Rapidly releasing multiple projects a year has become the norm for many modern artists. This paper will examine the licensing framework, royalty payout issues, and propose a solution.

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.