_:n9de1ab0afc5e432b8220b6229e9ff41fxb0
<http://schema.org/description>
"They are expected to trust that the algorithm is fair, that the variables or features in it are the right ones, that the data is accurate, and that an information system correctly deploys the algorithm. It’s about Tammy Dobbs, a woman in Arkansas with cerebral palsy whose home healthcare help was cut substantially because of a machine learning algorithm. Brant Fries , a professor and researcher who heads the organization that developed the algorithm, is undoubtedly a smart quant, and it’s fair for him to defend his group’s algorithm by saying that there needs to be a fair basis for allocating scarce healthcare resources. Fries, who admitted that the algorithm was complex, told the reporter that “you’re going to have to trust me that a bunch of smart people determined this is the smart way to do it.” A Legal Aid attorney, Kevin De Liban, didn’t trust the smart people, and filed suit in Arkansas courts to address the issue. The reporter asked Fries whether algorithms for something so important should be explainable; he clearly wasn’t too bothered about the issue: Fries says there’s no best practice for alerting people about how an algorithm works. Of course, providing explanations for algorithms isn’t easy, and it can be extremely challenging for some types of machine learning like deep learning-based image recognition algorithms."
<https://iianalytics.com/community/blog/welcome-to-the-age-of-explainability>