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NYC Announces Plans To Test Algorithms For Bias (betanews.com)
The mayor of New York City, Bill de Blasio, has announced the formation of a new task force to examine the fairness of the algorithms used in the city's automated systems. From a report: The Automated Decision Systems Task Force will review algorithms that are in use to determine that they are free from bias. Representatives from the Department of Social Services, the NYC Police Department, the Department of Transportation, the Mayor's Office of Criminal Justice, the Administration for Children's Services, and the Department of Education will be involved, and the aim is to produce a report by December 2019. However, it may be some time before the task force has any sort of effect. While a report is planned for the end of next year, it will merely recommend "procedures for reviewing and assessing City algorithmic tools to ensure equity and opportunity" -- it will be a while before any recommendation might be assessed and implemented.
An un-biased algorithm means removing race and sex from consideration. But this is not what is being sought. The results from removing these two factors from school admission or job qualification determinations will produce results that will be automatically labeled racist or sexist. Competency and quality will always rise to the top regardless of race or sex but that is not an acceptable result in today's society.
...Affirmative Algorithms.
Put in a mandate that all government algorithms most be open sourced in an easily accessible fashion, and all data passed through them must also be easily accessed. This will enable 3rd parties, ANY 3rd party, not just contracted "companies" (usually in the pockets of the people making decisions) to audit the code and data for flaws.
One of the largest issues I've seen in the past with these systems is that they falsely assume correlation = causation. And quite often, the cause and effect are backwards, too. One example I always liked was that "overhead high voltage power lines caused health issues for those that live near them" - when once the data was updated with more inputs, it was discovered that it was an entirely different cause all together. High voltage power lines are unsightly, causing housing values around them to be below the average for the community. Poorer families were buying/renting them. Poorer families are more likely to have health issues due to financial constraints. In the end, the correlation wasn't causation, but each item both shared a similar root cause.
The headline is actually reversed. Algorithms aren't biased, or racist, or whatever, they take all the input data they can get, crunch the numbers, and produce a result based on the data. The goal in this case is to take unbiased algorithms and, if they produce a result that SJWs object to, bias them to produce a result more in line with what the SJWs want to see. So the idea is to make the algorithms biased, not unbiased.