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.