Originally posted 18 Jan 20
Here is an excerpt:
Any standard-setting in this field must be rooted in the understanding that data is the lifeblood of AI. The continual input of information is what fuels machine learning, and the most powerful AI tools require massive amounts of it. This of course raises issues of how that data is being collected, how it is being used, and how it is being safeguarded.
One of the most difficult questions we must address is how to overcome bias, particularly the unintentional kind. Let’s consider one potential application for AI: criminal justice. By removing prejudices that contribute to racial and demographic disparities, we can create systems that produce more uniform sentencing standards. Yet, programming such a system still requires weighting countless factors to determine appropriate outcomes. It is a human who must program the AI, and a person’s worldview will shape how they program machines to learn. That’s just one reason why enterprises developing AI must consider workforce diversity and put in place best practices and control for both intentional and inherent bias.
This leads back to transparency.
A computer can make a highly complex decision in an instant, but will we have confidence that it’s making a just one?
Whether a machine is determining a jail sentence, or approving a loan, or deciding who is admitted to a college, how do we explain how those choices were made? And how do we make sure the factors that went into that algorithm are understandable for the average person?
The info is here.