jm + ai-now   1

The 10 Top Recommendations for the AI Field in 2017 from the AI Now Institute
I am 100% behind this. There's so much potential for hidden bias and unethical discrimination in careless AI/ML deployment.
While AI holds significant promise, we’re seeing significant challenges in the rapid push to integrate these systems into high stakes domains. In criminal justice, a team at Propublica, and multiple academics since, have investigated how an algorithm used by courts and law enforcement to predict recidivism in criminal defendants may be introducing significant bias against African Americans. In a healthcare setting, a study at the University of Pittsburgh Medical Center observed that an AI system used to triage pneumonia patients was missing a major risk factor for severe complications. In the education field, teachers in Texas successfully sued their school district for evaluating them based on a ‘black box’ algorithm, which was exposed to be deeply flawed.

This handful of examples is just the start — there’s much more we do not yet know. Part of the challenge is that the industry currently lacks standardized methods for testing and auditing AI systems to ensure they are safe and not amplifying bias. Yet early-stage AI systems are being introduced simultaneously across multiple areas, including healthcare, finance, law, education, and the workplace. These systems are increasingly being used to predict everything from our taste in music, to our likelihood of experiencing mental illness, to our fitness for a job or a loan.
ai  algorithms  machine-learning  ai-now  ethics  bias  racism  discrimination 
november 2017 by jm

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