Points of View

Avoid Garbage in, Garbage out: AI Leaders Must Focus on Business Outcomes, not Algos

Jan 23, 2019 Jamie Snowdon Ollie O’Donoghue Brier Rigby Dames

According to over 380 enterprise leaders, a third of enterprises are aggressively pushing for increased adoption of AI technologies in their business operations. However, it is critical to design a method for developing algorithms in real-time to mitigate bias, in order to achieve the desired goals of the AI strategy.

 

Exhibit 1: 31% of enterprises are placing significant investment in AI

 

How much investment or focus is your organization making in the following in the next year to help you achieve operational cost saving goals? (top five with significant or some investment focus)

 

 

 

Source: HFS Research in conjunction with KPMG, State of Operations and Outsourcing 2018. Sample: Global 2000 Enterprise Buyers = 381

 

It’s not hard to see how bias in insurance, healthcare, and justice system AI solutions could cause detrimental effects to society. So, enterprise leaders looking to implement AI, must ensure they recognize the potential consequences. Also, they should adopt these technologies, which they may not fully understand, at a pace that factors in any additional required safeguards.

From the mainstream media to dystopian sci-fi films—we already know bias in technology is damaging. But how do we fix it?

 

The crux of the problem is that if bias goes uncorrected, AI tools will present a false caricature of the world, rather than an accurate and useful representation. For example, researchers from the University of Washington demonstrated how unintended correlations could creep into training data and cause errors in results. In this case, an algorithm designed to differentiate huskies from wolves classified a series of huskies as wolves simply because there was snow in the background. Bias had crept into the data set because all training pictures of wolves were set with snowy backgrounds; the algorithm used this characteristic of the data instead of others to make its determination (see Exhibit 1). This unconscious bias reveals just how quickly correlation can pull at the wrong strings. It’s not difficult to imagine scenarios where the ramifications could be more extensive.

 

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