Forecasting Breakthroughs: Identifying Future Leaders in the Semiconductor Industry; The Ethics and Implications of AI Surveillance in Grocery Stores

Underwood, David, School of Engineering and Applied Science, University of Virginia
Porter, Michael, Systems and Information Engineering, University of Virginia

Technical Report
Breakthrough technologies have the potential to disrupt markets and society. Anticipating such disruptions is crucial for policymakers, investors, and businesses in being proactive with regard to regulatory policies and in allocating resources effectively. This project aims to develop an analytical approach to identify companies that will lead in developing breakthrough technologies. The analysis focuses on the semiconductor industry, which has seen rapid growth in recent decades, surging from $139 billion in revenue in 2001 to $573.5 billion in 2022. Our systematic approach to predicting technological disruption in the semiconductor industry involves leveraging a combination of quantitative company data, human-centric elements, and feature engineering. Data was collected on 244 private semiconductor companies between 2012 and 2018, encompassing information about leadership profiles, research endeavors, media exposure, and financial performance. Two models were developed: a penalized regression model, and a boosted tree model, both aimed at forecasting the probability of a company achieving a valuation exceeding $500 million within five years of its first funding deal. Key variables such as the number of employees, year founded, total invested equity, number of active patents, and country of origin emerged as significant predictors of company success. This paper discusses the performance of our models and explores applying our findings to identify disruptive companies across industries.

STS Project
Grocery stores are frequently visited by Americans every day, as they allow civilians to acquire their basic needs at one focal location. As hundreds, sometimes thousands, stroll the aisles of a grocery store every day, a question arises: What measures can be implemented to ensure that the utilization of surveillance technology does not lead to discrimination or unjust targeting of certain groups? I believe that in order for such surveillance software to be free from biases and power imbalances, certain checks must be put in place for both grocery store CEOs and the software engineers that develop the classification technology to ensure that is applied fairly to members of all demographics. In the subsequent sections, I will first explain the frameworks and methods I will be using and how they help answer my question. STS frameworks help examine complex interactions between technology and society with the goal of better explaining the morals and implications these technologies have. With the help of the frameworks, Actor Network theory, and Race Critique of Classification and Surveillance, I will be able to break down the problems into smaller sections that will render my ability to answer them properly. After laying out potential problems that could lead to unjust targeting of certain groups, I will discuss potential next steps or solutions that could be implemented to help mitigate the risks of power imbalances and discrimination through surveillance.

BS (Bachelor of Science)
Classification Model, Semiconductor, Facial Recognition

School of Engineering and Applied Science

Bachelor of Science in Systems and Information Engineering

Technical Advisor: Michael Porter

STS Advisor: Joshua Earle

Technical Team Members: Adam Rogers, Ethan Kuzneski, Lauren Sullivan, Carter Dibsie, Robert Brozey

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