The Missteps That Can Lead to Failure in AI Development

August 17, 2020

The benefits of AI are becoming well-established when applied to the right applications, but why do they sometimes fail to deliver on the promise? This was a question I examined in a recent article I wrote for the Forbes Technology Council.

Below are a few of the missteps I shared:

  • Gathering the wrong or incomplete data. Companies need to take great care to make sure that their data is accurate, clean and representative, otherwise as the expression goes, “garbage in, garbage out.” The problem is that when you don’t pay enough attention to your data or take proper steps, you might not even know that you’re getting flawed results.
  • Glamorizing AI. Many companies think AI will be the answer to all of their problems, but that’s not the case. Some problems can’t be solved through AI. AI doesn’t work in situations where the data is random, involves constant change and no discernable pattern; or there are too many variables. Examples include trying to predict the stock market or the weather.
  • Using biased data. Data engineers need to consider where the data is coming from and if it is representative and diverse. Without a representative, diverse sample, AI algorithms can deliver incomplete or false outcomes, or worse, promote gender or racial discrimination.

Check out the Forbes article to learn what action can be taken to address these missteps and put you on a solid path toward beneficial AI.
After all, AI has great potential to be a helpful partner to humans, but it’s important to beware the hazards and set realistic expectations.

 

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