Page 36 - FCW, July 2017
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Emerging Tech
“YOU’RE NOT
to change their skill set,” she said. “They’re accomplishing different things.”
REPLACING
She added that she and her colleagues at Dcode42, which offered an AI-in-government program for vendors this year, are seeing huge interest in AI, machine learning and related technologies from government agencies. Many, however, are still trying to understand the technologies, their effects and their ethical boundaries, she said.
STAFF. YOU
JUST NEED
TO CHANGE
How to audit a robot
THEIR SKILL
Beyond employee management issues, one of the biggest obstacles to using AI in government is the auditability of the decisions intelligent systems make.
SET. THEY’RE
In the European Union, regulators want people to be able to demand an explanation when an intelligent system makes a decision that affects them. A version of that right to an explanation might be included in the EU’s General Data Pro- tection Regulation due in 2018.
ACCOMPLISHING
DIFFERENT
A regulated right to an explanation hasn’t gained trac- tion in the U.S., but AI experts say intelligent systems must deliver repeatable results and provide documentation that backs up their recommendations. That’s especially important when AI systems make major decisions that affect people’s lives, they say.
IBM’s Gordy said that when intelligence agencies deploy the Watson AI system, they receive the documentation the system used to come up with its answers.
Other experts say that in most cases, agencies should view AI as a tool for augmenting rather than replacing human decisions.
The technology should not be making the final decision for agencies in many situations, said Aron Ezra, CEO of Offer- Craft, a vendor of machine learning-powered marketing tools. A human should, in almost all cases, review the AI system’s recommendation, whether the technology is approving an applicant for a government program or flagging tax fraud.
“The fear that, all of the sudden, everyone’s going to be sitting back and...letting computers make all the decisions for us is something that I don’t see happening for some time, if ever,” he said.
Furthermore, Enthoven said AI results improve substan- tially when the organization has an expert managing what’s going into the system.
“There’s this one approach where you throw all the data in a big hopper, turn the crank, see what comes out of the bottom, and it’s got to be right,” he said. “You’re going to have better luck and better accuracy if you don’t just turn the crank on the machine but actually understand what you’re doing.”
As with most systems, the garbage-in, garbage-out rule applies to AI. Although machine learning allows such systems to become more intelligent, it’s important to take the time to train the system and feed the proper data into it.
THINGS.”
MEAGAN METZGER, DCODE42
18 July 2017
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