Session C: 1:45PM – 3:15PM
Business. Session C – Poster Presentations, Ballroom, Union
SESSION C (1:45-3:15PM)
Location: Ballroom, A. Ray Olpin University Union
The Intersectionality of Entrepreneurship and Latinx Critical Theory: Promoting Access to Credit for Latinx Entrepreneurs of Construction Companies with Alternative Methods of Financial Reporting
Luis Ramirez, University of Utah
Faculty Mentor: Lyda Bigelow, University of Utah
SESSION C (1:45-3:15PM)
POSTER c71
Disproportionate outcomes exist in entrepreneurship based on the identity of the founders. The Hispanic/Latinx population in the U.S. grew by 23% from 2010 to 2020, which represented over half of the total growth in the U.S. population. Yet despite accounting for 19% of the population, Hispanic owned businesses account for only 5.8% of all businesses. Even in low-barrier industries there is underrepresentation, in part because they lack access to education and start-up capital. We are especially interested in understanding how a Hispanic/Latinx identity impacts entrepreneurial outcomes in the low-barrier industry of construction. Since many studies explore the obstacles and barriers in education for Hispanic/Latinx students, this paper explores the challenges in accessing start-up capital in the form of credit. Using a quantitative methodology and Critical Race Theory Framework, we collected data from the Utah Department of Occupational and Professional Licensing to estimate the number of contractor construction companies owned by Hispanic/Latinx founders. We used this data to conduct a survey on entrepreneurs of construction companies in Utah. The survey collected firm data (e.g., cash flows, legal entity, trade, employees, etc.) and owner demographic data (e.g., education, immigration, nationality, phenotype, language, etc). Using Python, the firm and owner demographic data will be used as independent variables for a regression analysis that predicts the cash-basis revenue performance of construction companies. The findings of our research provide alternative methods of financial reporting and credit risk assessment for promoting Latinx entrepreneurship in construction and solving the shortage of affordable housing.
Facial Recognition and AI Ethics: A Review of Literature to Develop a Framework of the Current State of our Understanding and Guidelines for AI System Development
Isaak Grettum, University of Utah
Faculty Mentor: Sankar Srinivasan, University of Utah
SESSION C (1:45-3:15PM)
POSTER C72
Ethics is currently developing into a fundamental problem in creating Artificial Intelligence (AI). From sampling methods for training data to user intent, issues with the ethical use of AI have been propping up. One such case is one in which a black man, Robert Julian-Borchak Williams, was misidentified as the suspect in a robbery, to which he bore no resemblance, by a facial recognition AI servicing a Detroit Police department (Hill). In another case, Gender Shades found that million-dollar facial recognition software from companies such as Microsoft, IBM, and Amazon had error rates for black females up to thirty-four percent greater than for white males (Najibi). In contrast to these events, emotion recognition and face detection AIs have been assisting children on the autism spectrum to improve their social skills (“Applying AI for social good”). Cases such as these have been the driving force to provide more safeguards for AI development. Though AI can be used for the ethical good, its potential for the ethical bad can hold it back from wider use, costing developers and businesses their jobs and income. As the usages of AI continue to expand in daily usage from social media recommendations to self-driving cars, it has become pertinent to explore the ethical dilemmas surrounding them. The market has since agreed, as agencies such as the Center for Information Technology Policy (CITP) and private experts delve into this vast new field. We will be synthesizing this research from several different sources & perspectives in order to find a more overarching guide for the public & private sectors. This will include the aforementioned prominent journals and first-hand accounts with experts. We will find an understanding of the overall state of the market through the review of literature from multiple fields of study, however, particularly concentrating on their perspectives focusing on AI for facial recognition. These fields of study will include but are not limited to psychology, economics, American Law, sociology, art, criminology, and sustainability. In a glancing likeness to Asimov’s Laws from Isaac Asimov’s philosophical short story Runaround, We will create a comprehensive set of principles and guidelines facing current ethical cases. This AI common law and guidelines will be used by system operators & managers to ensure ethical practices, thereby instantiating precedent for future growth and adaptation. This system would lessen racial profiling and hopefully cease it, on a governmental and private level. Would allow smartphones to be safer in protecting the contents of a phone, or other data storage. The cons of a system like this could be a controversial divide between ethical ideologies, much like the American political system. However, it is a much-needed start in the ever-expanding field of Artificial Intelligence systems.
Solving the Housing Crisis in Utah
Kevin Yang, University of Utah
Faculty Mentor: Sankar Srinivasan, University of Utah
SESSION C (1:45-3:15PM)
POSTER C73
The focus of our research is to explore solutions to tackle the housing crisis in Utah. The pandemic has had a significant impact on our society and economy at large. In Utah in particular, with a heavy influx of families and professionals, the already hot housing market has come under immense pressure. Due to high demand and low inventory the Salt Lake housing market has appreciated substantially in the last few years making it challenging to own a home for new homebuyers (first-time homebuyers). The home rental rates have also appreciated substantially making it expensive to rent a place of one’s choice without incurring substantial monthly expense. According to Emily Harris’s report for the University of Utah, “Utah was the fastest-growing state in the nation from 2010 to 2020”.(Emily Harris, Moving Past Net Migration: Demographic Characteristics of Utah’s Recent Migrants). Another contributor to the housing shortage in Salt Lake is the rapid growth of the city. For example, Utah’s population growth rate from 2010 – 2020 was 18.4%, which was the highest in the nation. In this research we explore possible solutions that could be applied to improve the housing situation in Utah. We do this systematically in the following ways – 1) examine what scholars have identified as solution for situations such as the one we have in Utah 2) examine what densely populated and expensive places like HongKong and Singapore have done to ensure housing for all 3) we take a look at models like AirBnb and Uber/Lyft that has made it possible to have idle resources to be of use to those in need while adding to the economic output and see if some lessons could be gleaned for a technology based solution to the current problem in Utah. We propose novel solutions by synthesizing our insights from the above methodology for the consideration of public policy experts. This research and its findings (proposed solutions) will be significant because it addresses one of the fundamental humans needs – Shelter and sees how to make it affordable and accessible to everyone. It also explores both established solutions (as seen from published research and those adopted by other successful places in the world) and also by attempting to identify a novel approach by adapting the success we have seen with business models that underlie the sharing economy.
