This is a good thing --an algorithm with up to a 30 error rate is unreliable. One hopes this scrutiny will lead to development of more dependable and trustworthy algorithms. Also hopeful politicians don't have the ability to tweak things in order to fit their agendas.

For example an algorithm is too good at detecting which gov't employee is stealing from the public. Or it tells them they should decrease fines in some area to get better results --but that would affect their budget adversely, etc.

One of those paradoxical situations when something _really_ needs to be regulated but can't be regulated. E.g. how do you know what data set is the result of an algorithm (a dubious consolidation step?), or how do you regulate algorithmic learning when we don't fully understand how learning works?

I wonder how an algorithm with low explainability will be treated, like a large nueral network. Seems like it would be hard to pin it on the algorithm rather than the data.

I find this incredibly off putting since the city cannot even account for its own wasteful budget.

There is nothing wrong with accountability and am glad they are starting somewhere but until they start holding others like the ones who purposesly destroy on time metrics of transit in order to keep thier salary gravy train choo-chooing, I am not holding my breath.

https://mobile.nytimes.com/2017/11/18/nyregion/new-york-subw...

I think it's a travesty that the source code won't be shared with the public, but I hope it would at least be provided for review to the agencies adopting the algorithms.

In my field we are highly regulated and must provide the regulatory agencies with an audit trail for all data. I would like to see an audit trail for these algorithms that would allow someone to follow the decision tree and review the outcome of the algorithms.

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This is a good move. I hope other states follow suit. I highly recommend reading Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy