Algorithmic decision systems have been demonstrated to replicate and exacerbate racial bias in the following ways:
Auto insurance is more expensive.
Communities of color pay 30% more for auto insurance premiums than whiter communities with similar accident costs.
Credit scores are lower.
White homebuyers have credit scores 57 points higher than Black homebuyers, and 33 points higher than Latinx homebuyers.
Mortgages are more expensive or altogether inaccessible.
Higher, discriminatory mortgage prices cost Latinx and Black communities $750 million each year. At least 6% of Latinx and Black applications are rejected but would be accepted if the borrower were not a part of these minority groups.
Students get screened out of better schools and assigned worse grades.
In New York City, Black and Latinx students are admitted to top schools at half the rate of white and Asian students. At some universities, Black students are up to 4 times as likely to be labeled ‘high risk’ as white students.
Patients are denied life-saving care.
White patients with the same level of illness were assigned higher algorithmically determined risk scores than Black patients. As a result, the number of Black patients eligible for extra care was cut by more than half.
Criminal justice system is more punitive.
Black defendants are 45% to 77% more likely to be assigned higher risk scores than white defendants.
Communities are over surveilled and policed.
Black individuals were targeted by predictive policing for drug use at twice the rate of white individuals. Non-Black people of color were targeted at a rate 1.5 times that of white individuals. Notably, the actual pattern of drug use by each race is comparable across the board.
Chung, J. (2021). Racism in, racism out: A primer on algorithmic racism. Public Citizen. https://www.citizen.org/article/algorithmic-racism/