These algorithms feed into an artificial intelligence framework where machine learning makes decisions and predictions from data about people – decisions previously made by people. The report reviews a number of incidents that have made it into the media in which algorithms perpetuated discrimination based on race, gender or income – and those reports represent just the tip of the iceberg, because most algorithms operate in the background, unseen and unknown by those whose lives they impact. In February, the New York Times reported serious issues with an algorithm the federal government uses to manage COVID-19 vaccine allocations: "The Tiberius algorithm calculates state vaccine allotments based on data from the American Community Survey, a household poll from the United States Census Bureau that may undercount certain populations – like undocumented immigrants or tribal communities – at risk for the virus." If passed, Assembly Bill 13 would set forth criteria for the procurement of high-risk automated decision systems by government entities in order to minimize the risk of adverse and discriminatory impacts resulting from their design and application. Specifically, the bill would require a prospective contractor to submit an Automated Decision System Impact Assessment to evaluate the privacy and security risks to personal information and risks that may result in inaccurate, unfair, biased or discriminatory decisions impacting individuals.