Akshita K's Senior Project Blog

Project Title: Demographic Disparities in AI-Disrupted Jobs: The Impact of AI on the U.S. Labor Market
BASIS Advisor: Thomas Carpenter
Internship Location: Independent
Onsite Mentor: Krystian Confeiteiro, Student; Embry-Riddle



Project Abstract

With the rise of new technology, the U.S. labor market is undergoing significant change. Artificial Intelligence (AI) is increasingly integrated into various industries, reshaping the demand for labor and the skills employers seek. This study examines the impact of AI and computerization on different demographic groups within the U.S. labor market, focusing on sex, age, level of education, duration of residency in the US, race, income, and occupation type. To examine occupation-type differences, I classify jobs as “white-collar” or “blue-collar,” and group them by industry (e.g., healthcare, IT, manufacturing) to evaluate sector-specific patterns. Additionally, I analyze the regression interactions between race and occupation type to explore why certain racial groups may experience higher AI exposure. Understanding these demographic disparities is crucial for workers to understand their future job prospects, and for policymakers to support affected populations, ensuring a more equitable workforce transition into the era of AI.

    My Posts:

  • Week 11: Thank You and Farewell

    Hi everyone! Welcome back to the final post of my senior project. It’s been an eye-opening experience to explore how AI is reshaping the workforce—and more importantly, to find who’s most affected by these changes. Today, I want to take a moment to express my gratitude to everyone who made this project possible. First off,... Read More

  • Week 10: Research Conclusion and Policy Recommendations

    After weeks of regression models, if there is one thing that my project has made clear, it’s that AI doesn’t affect everyone equally. Some groups—like women, older workers, certain racial minorities, and recent immigrants—are more likely to be in jobs with higher vulnerability to AI. As much as AI creates exciting new tools and careers,... Read More

  • Week 9 – Finalizing My Results: Confounding Variables, Multicollinearity, and Interactions

    Welcome back to Week 9 of my senior project! This week, I’ve been finalizing my results, addressing multicollinearity, exploring potential lag effects over time, and running regression interactions to explain the seemingly contradictory results between my initial findings and my results after controlling for confounding variables. Multicollinearity Multicollinearity occurs when two or more independent variables... Read More

  • Week 8: Regional Effect on AI Exposure and Confounding Variables

    Welcome back to Week 8 of my senior project! This week, I’ve been diving into regional differences in AI exposure and confounding variables. AI Exposure across different regions of the U.S. The map below demonstrates which regions are most vulnerable to AI-induced job displacement. I found these results slightly disturbing, as we can see here... Read More

  • Week 7: Analyzing AI Exposure by Occupation and Industry

    Welcome back to Week 7 of my senior project! This week, I’ve been analyzing the relationship between occupation type (white/blue collar) and industry using linear regression. Regression Results You can find the full regression results here. AI Exposure and Occupation Type For this analysis, the “whiteCollar” variable is defined as: 1 for white-collar workers 0... Read More

  • Week 6: Analyzing AI Exposure Through Polynomial Regression

    Welcome back to another week of my senior project! After exploring linear regression in the previous post, this week I’ve been analyzing the relationship between various demographic factors and AI exposure using a polynomial regression to see if a more complex model reveals any non-linear patterns that we didn’t capture with linear regression. Note: I... Read More

  • Week 5: Analyzing AI Exposure Through Linear Regression

    Hey everyone, welcome back to Week 5 of my senior project! This week, I’ve been running regressions in RStudio to examine which demographics are most vulnerable to AI-induced job displacement. As I mentioned in previous posts, we can analyze this by looking at the relationship between demographic factors (such as education level) and AI exposure... Read More

  • Week 4: An Introduction to Polynomial Regression

    Welcome back! This week, I have been studying polynomial regressions and how I can apply them to my research. What is Polynomial Regression? Last week, I introduced different types of regression models, including linear and logistic regression. However, real-world data is often more complex than a simple straight-line relationship. This is where polynomial regression becomes... Read More

  • Week 3: Why These Demographics Matter in AI Job Displacement

    Hey everyone, welcome back to my blog! This week, I’ve been diving deeper into why we are analyzing specific demographic variables. Why Are We Looking at These Variables? To understand AI-driven job displacement, we need to analyze how different demographic groups are affected. The variables I selected—sex, education, race, job type, income, and duration of... Read More

  • Week 2: An Overview of Data Collection and Types of Regression

    Welcome back to my blog! This is Akshita again and this week, I have been exploring how I can collect and analyze data for my project. In this post, I want to introduce you to the database I will be using for my research, and the types of regression models that could be helpful in... Read More

  • Week 1: Will AI Take Your Job? Understanding the Shift in the Workforce

    Hey everyone, welcome back to my blog! This week, I focused on understanding how past technological advancements have shaped employment and what that means for AI’s impact on the job market today. Historical context: Automation in the workforce     To predict AI’s effects on employment in the U.S., it is essential to first examine... Read More

  • Introduction

    Hey everyone! My name is Akshita Khanna, and my senior project examines the demographic disparities in AI-induced job displacement. My prior computational experience, from analyzing the All of Us Database to examine neurotrauma due to elder abuse through the University of Arizona KEYS Internship to studying the women’s lack of healthcare rights in Senegal and... Read More