> including an “algorithmic transparency requirement” that would help people understand how their data was being used and what automated decisions were then bei

> including an “algorithmic transparency requirement” that would help people understand how their data was being used and what automated decisions were then being made about them. This needs to be required for any type of algorithmic decision making. Without algorithmic transparency ulterior motives, intentional or unintentional biases, and unnoticed mistakes are hidden from public review. A common response is that we don't know how some types of machine learning make their decisions. I agree this is occasionally true. Find a way generate an explanation, or use a different algorithm; transparency is a critical requirement.