Decelerating Hypersonic Flight Experiment Using a CubeSat Platform; The Role of AI in the United States Legal System

The technical work addresses testing a hypersonic flight experiment on a 3U CubeSat platform. Motivations behind conducting hypersonic experiments are to provide affordable and accurate flight data for military and commercial applications. The STS research paper discusses the role that artificial intelligence contributes to the United States legal system in how it mitigates or worsens bias. The topic of the STS research paper is loosely connected to the capstone assignment; the technical component of the software and avionics sub team connects to the use of software in the form of algorithms in the legal system. The motivations behind the STS topic are to explore the effects – intended and unintended – of technological developments in fields that traditionally rely more on human actions.

The subject of the Capstone project describes the design of a proposed hypersonic flight experiment, conducted on a CubeSat platform. The proposed design will be submitted to the Department of Defense and NASA for funding approval. The CubeSat will collect hypersonic flight data – temperature and pressure – during the mission duration. The proposed project will launch a 3U CubeSat with a launch provider, such as Northrup Grumman. Then, the CubeSat will be released from the dispenser to fall toward Earth. During its re-entry, CubeSat will enter hypersonic flight and data collection will begin. Pressure and temperature data will be collected through sensors and transmitted via the Starlink network. After the period operation, the CubeSat will burn up and the mission will conclude.

The subject of the STS research addresses the usage of artificial intelligence (AI) in the United States legal system. The rising usage of artificial intelligence in the legal field primarily targets the component of recidivism in judicial sentencing, as data driven decisions are sought after for their perceived unbiased performance. However, researchers and law reviews have suggested both biases in recidivism prediction, as well as legal concerns over the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm specifically. Thus, the STS paper addresses the following question: how does the use of AI in the United States legal system reduce or exacerbate bias, compared to traditional sentencing? To analyze the question, the STS frameworks of the “technological fix” and Social Construction of Technology (SCOT) are used to analyze algorithm usage policies, law review publications, and statistical analysis. Using both frameworks, the research provides an understanding of the social and political context that this technology arises out of and the results of its application to “fix” societal problems. The results found should inform policy makers and algorithm designers on how to create equitable solutions and manage bias within the legal system, when using algorithms to inform sentencing.

In working on both projects, insight into the connections between technological and non-technical aspects of both projects were gained. As part of the software and avionics sub team, determining what components to select and how they will operate with other systems demonstrated how interdisciplinary engineering projects can be. The CubeSat project consists of 15 people and working between each sub team demonstrated the difficulty in communicating requirements and objectives of different subsystems. Working on the STS research paper with the CubeSat project was enlightening in understanding how non-technical factors could contribute to a project’s success or failure. For example, the different methods of communication presented a challenge to the team. Technical documentation and explanation of components required enough scientific and comprehension skills to understand. Non-technical skills have an immense effect on the project. Similarly, the STS research paper demonstrated how a background in engineering could be useful in a non-STEM area. Understanding the basics of statistics and machine learning were beneficial to reading the work of several researchers from different fields. Overall, the value of working on both projects has tremendously aided my
technical and non-technical skills.

School of Engineering and Applied Science

Bachelor of Science in Mechanical Engineering

Technical Advisor: Chris Goyne

STS Advisor: Bryn Seabrook

Technical Team Members: Emma Auld, Hannah Boyles, Taylor Chandler, Yulie Cheng, Carsten Connolly, Noah Dunn, Joshua Franklin, Samuel Goodkind, Amy Lee, Andrew Metro, Isaac Morrison, Charlie Osborne, Carlos Perez, Vincent Tate, and Micah Whitmire