We tend to think of technology as value neutral, as a set of essentially objective tools that people use, sometimes for good, sometimes for bad, particularly when questions of race and racial justice are involved. But are the technologies we develop and deploy really neutral? Or might they be shaped by historical prejudices, biases, and social inequalities? To what extent is technology perhaps no less biased and racist than the underlying society in which it exists? We will consider these questions and more in the context of a wide range of technologies, including risk assessment algorithms for bail, predictive policing, and other decisions in the criminal justice system; facial recognition systems; surveillance tools; algorithms for medical diagnosis and treatment decisions; targeted online housing ads that result in "digital redlining;" programs that determine entitlement to credit or public benefits and/or purport to detect fraud by recipients; algorithms used in recruiting and hiring; social-media targeting and disinformation; digital divide access gaps; and more. We will seek to articulate a framework for understanding how bias in tech might occur and how it might be related to racism and discrimination more broadly in our society. Finally, we will explore how these problems might be addressed, including by regulators, legislators, and courts as well as by technology developers and educators. Elements used in grading: Full attendance, reading of assigned materials, and active participation. Class meets: 4:LAW 30 PM-6:LAW 30 PM on Sept. LAW 17, Oct. 1, Oct. LAW 15, Oct. LAW 29.
1 units · Law Mandatory P/R/F
We tend to think of technology as value neutral, as a set of essentially objective tools that people use, sometimes for good, sometimes for bad, particularly when questions of race and racial justice are involved. But are the technologies we develop and deploy really neutral? Or might they be shaped by historical prejudices, biases, and social inequalities? To what extent is technology perhaps no less biased and racist than the underlying society in which it exists? We will consider these questions and more in the context of a wide range of technologies, including risk assessment algorithms for bail, predictive policing, and other decisions in the criminal justice system; facial recognition systems; surveillance tools; algorithms for medical diagnosis and treatment decisions; targeted online housing ads that result in "digital redlining;" programs that determine entitlement to credit or public benefits and/or purport to detect fraud by recipients; algorithms used in recruiting and hiring; social-media targeting and disinformation; digital divide access gaps; and more. We will seek to articulate a framework for understanding how bias in tech might occur and how it might be related to racism and discrimination more broadly in our society. Finally, we will explore how these problems might be addressed, including by regulators, legislators, and courts as well as by technology developers and educators. Elements used in grading: Full attendance, reading of assigned materials, and active participation. Class meets: 4:30 PM-6:30 PM on Sept. 17, Oct. 1, Oct. 15, Oct. 29.
Offered in Autumn 2026 at Stanford University.