Statii News: IBM: If AI decision needs closer look, stay woke, and here’s how

Sunday 23 September 2018

IBM: If AI decision needs closer look, stay woke, and here’s how

OK we get it. The gee-whiz feeling of artificial intelligence as shown in its developmental glory is with us and we ride the wave willingly.

A sobering phase though is definitely in the wings. Now that we have AI, what are we doing with it, and are we managing, even assessing, it well?

“Like video gamers looking for the next hack, employees will need to monitor, understand, question and exploit the vulnerabilities of their tools and account for them,” said John Sumser, principal analyst at HR Examiner.

“Digital employees are central to our future, but managing them is very different than managing people or older software.” Quoted in Human Resource Executive:”…understand that we are at the beginning of building and using intelligent tools, there is much work ahead and we will have to think about our machines differently from now on.”

The AI supporters for decisions as used by governments and large organizations, after all, do impact our lives.

The big question is, who and what are training AI to make decisions? Is there bias baked into the training phase? If so, how can be sure the result is the fairest decision?

Long and short, IBM researchers have been busy devising ways to reduce bias in the datasets used to train AI. What are they doing? Are we to just look at yet another white paper? They are doing more than that.

They are delivering a rating system that can rank the relative fairness of an AI system.

Fairness is not just something that caught the attention of IBM. Zoe Kleinman, technology reporter, BBC News, wrote, “There is increasing concern that algorithms used by both tech giants and other firms are not always fair in their decision-making.”

IBM’s arsenal of artificial intelligence technology tools now includes a way to ferret out unconscious bias in making decisions. Bias doesn’t always come dressed in neon lights and magic-marker labels. Half the time we are even cross-examining out own ability to judge, feeling uneasy about the other half of us that suspects the decision was rigged with bias. Make no mistake, though, our sniffers are often correct.