Thesis Summary

Chapter 1: Engineering the American Dream

Why has tech become the focal point for ongoing discussions about race, gender and inequality in the rapidly changing labor market? This chapter explores how our understanding of tech's "diversity issue" connects to historical debates regarding the best way to achieve "equal opportunity" in a country where people start out at very different places in life.

Chapter 2: Tech's Mirrortocracy

The tech industry isn't just struggling to hire diverse workers -- they're spending thousands of dollars and a lot of time recruiting from an over-tapped pool of talent. This chapter digs into the ways that the tech industry's assumptions about talent have them stuck in a loop of insular hiring practices, and the assumptions that they'll need to change in order to identify and recruit from a broader pool of work-ready talent. 

Chapter 3: Calculated Bias in Algorithmic Recruitment

Emerging forms of data-driven recruitment have been enthusiastically received by some as a new approach to minimizing bias and discrimination in hiring. This chapter provides a detailed analysis of the ways big data can be used to address age-old biases in the labor market, and the pitfalls which currently limit its potential as a tool for equal opportunity hiring.  

Chapter 4: CODE2040

How might we build out an infrastructure for recruitment that fundamentally expands the diversity of the tech workforce pipeline? This chapter takes an in-depth look at how CODE2040 is working to increase both the supply and the demand for Black and Latino/a engineers in Silicon Valley by building a brand around top-notch minority talent. 

Chapter 5: Tech and the Future Battle for Equal Opportunity

This chapter connects the issue of diversity in tech to larger debates about the future of work, tech-driven inequality and visions of the new American Dream. 

You can find more condensed snippets of research from this thesis in my blog posts.